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Machine Learning Monitoring: What Is Data Drift?

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Deep dive into creating models that monitor AI data.

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Deep dive into creating models that monitor AI data.

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*Record scratch*

*Freeze frame*

Yup, that's me being plowed to the ground because the business just lost more than $500,000 with our fraud detection system by wrongly flagging fraudulent transactions as legitimate, and my boss's career is probably over. And that guy in the chair? That's our DevOps Engineer. You're probably wondering how we got here...

My story began with an image that you've probably seen over 1,001 times- the lifecycle of an ML project.

A few months ago, we finally deployed to production after months of perfecting our model. "Congratulations!" I told myself and my colleague, "Our hard work has surely paid off, hasn't it?". Excitement was immeasurable.

Our model was serving requests in real-time and returning results in batches-good stuff! Surely that was enough, right? Right?

Well, not quite, which we got to realize in a relatively dramatic fashion.

I'm not going to bore you with the cliché reasons why the typical way of deploying working software just doesn't cut it with machine learning applications. I'm still trying to recover from the bruises that my boss left on me, and the least I can do is help you not end up in a hospital bed after "successful model deployment", like me.
I'll tell you all about:

Why deployment is not the final step for you,

Why you need to also own and monitor models in production,

What you should monitor in production and how to monitor it,

Different monitoring and observability platforms and how to choose them,

Logging and alerting,

Challenges and best practices for monitoring your models in production,

And lots more!

By the end of this article, you should know exactly what to do after deploying your model, including how to monitor your models in production, how to spot problems, how to troubleshoot, and how to approach the "life" of your model beyond monitoring.

Congratulations ... But it does not stop at deployment

Take a look at your traditional software application after deployment:

Great, right? You almost don't have to worry about anything. Based on the software development lifecycle, it should work as expected because you have rigorously tested it and deployed it. In fact, your team may decide on a steady and periodic release of new versions as you mostly upgrade to meet new system requirements or new business needs.

In terms of monitoring your application, you might be worried about system metrics, error rates, traffic volume, app loading times, infrastructure (server counts, load, CPU/GPU usage), things like that. Your application scales to a lot of users, and it works as intended and solves the problem it was built to solve.
That's conventional software. But now, enter the machine learning world:
Deploying your model was likely a hassle in itself. But now, who's taking care of what you deployed? The software might have a DevOps team that monitors and maintains the system in production but with machine learning applications, you can't just hand your deployed models off to the Ops team-in your case, it has to be a shared responsibility.

As you can see from the image above, machine learning models degrade over time. They're dynamic and sensitive to real changes in the real world.

But models are kind of like cars. The value of a new car goes down 10% the moment they're driven out of a dealership -the moment they're "deployed" to the real world. Same with your new model.

From the moment you deploy your model to production, it begins to degrade in terms of performance. A model is at its best just before being deployed to production. This is why deployment should not be your final step.

Beyond that, there has to be a way you consistently report back to business stakeholders on whether, and how, your deployed machine learning solution(s) are solving any problems.

Validation results during development will seldom fully justify your model's performance in production. This is a key reason why you have to monitor your models after deployment-to make sure they keep performing as well as they're supposed to.

There could also be situations where you deploy models that are localized to multiple geographic regions. How do you ensure that your models serve the intended results (predictions) to each client concerning high interest in their own reality? How do you explain why a model makes certain predictions for one geographic region and a different set of predictions for another region?

Ultimately, the key lesson is this: there's a lot of work to be done after deployment, and you shouldn't hand off your models too easily. Deployment isn't the end of working on your model, it's actually the beginning!

Why you need to monitor your Machine Learning models in production

If you've made it this far, I probably managed to convince you that models perform worse in production than they do in development. This is a challenge you need to be aware of as a data scientist or machine learning engineer. You'll find the solutions to this challenge in production, and this is where you take the next step after deployment-monitoring!

But why do you need to monitor your models?

To answer this question, let's look at some of the challenges your model will encounter in production:

This should give you a broad idea of the challenges that you may encounter after deploying to prod, and why you need to continue your good work after deployment by monitoring your model(s) in production.

Essentially, the goal of monitoring your models in production is:

To detect problems with your model and the system serving your model in production before they start to generate negative business value,

To take action by triaging and troubleshooting models in production or the inputs and systems that enable them,

To ensure their predictions and results can be explained and reported,

To ensure the model's prediction process is transparent to relevant stakeholders for proper governance,

Finally, to provide a path for maintaining and improving the model in production.

Let's get more specific.

What to monitor based on challenges your model could encounter in production: Functional monitoring vs operational monitoring

You've deployed your model, and now you need to observe and check the progress and quality of your ML application in production over its entire lifetime, keeping it under systematic review so it keeps serving its intended value-this is monitoring in the context of production machine learning systems.

Your machine learning application isn't just the model, but everything that enables your model in production, including infrastructure, input data, resources, and other upstream and/or downstream services.

If you're wondering about what to monitor, you should generally think about two things:

What could go right?

Wondering about what could go right, you should think back to the planning phase of your machine learning project workflow, where you hopefully planned around the user and the business objective.

Primarily, everything that could go right is all that guarantees that the business objective and the user (human/service/system) needs are met.

How do you monitor and measure this? It's very unique for each business use case, and typically depends on:

What does your business define as success? What were the KPIs set during the planning phase?

What were the performance expectations before deploying to production?

If my model returns a prediction to a client, what does it expect in terms of results and how quickly should it be delivered?

To answer the questions above, you need some metric selection criteria. Success for a business could mean an entire system in which your model only plays a small role.

To hone in and get specific with your metric selection criteria, you can follow some of these best practices (credits to Lina Weichbrodt for this):

To think about what success means for a business, you also have to think about what qualifies as a good user experience. Then think about how your model contributes to that good user experience in the context of the entire system-your model is usually not the main solution to UX issues.

For example, if we're building a loan approval system, a good business objective could be:

"quickly approving a loan for a customer that will pay back at the stipulated time"

A goal like this would probably result in a good user experience, but it would be difficult to monitor how well our model contributes to it. There are lots of variables that contribute to whether a customer pays back a loan or not (including how they perceive the business, the interest rates offered, and so on). We can't monitor that.

So, a decent metric to monitor could be:

"How fast does the model score a request from the client and return a prediction?"

With constraints like these:

In this way, I have picked a metric that:

Anything that negates the point above should be considered a "bad" user experience and could be described as everything that could go wrong. Case in point, if the model returns a likelihood score of 0.55 for a loan request, that should either alert someone (maybe a consultant that can take a look at the loan request) or alert a different system to load up another model for scoring.

Now for the fun part-monitoring the things that could go wrong.

What could go wrong?

You can monitor what could go wrong with your machine learning model in production at two levels:

Functional level monitoring - monitoring model performance, inputs (data), and outputs (predictions).

Operational level monitoring - monitoring at system and resource level.

Functional monitoring

As a data scientist, you're primarily responsible for monitoring at this level. You're mostly monitoring the performance of your model in relation to the inputs, as well as the prediction results and what goes on in the model while learning in production.

Data

Input level functional monitoring is crucial in production because your model reacts to the inputs it receives. If the inputs aren't what your model expects, it will most likely influence performance. Monitoring and measuring input level challenges is the first step to troubleshooting functional performance issues and solving them before serious damage is done.

Below are the three cases you may want to monitor at the input level.
1. Data quality issues
Data quality (integrity) issues mostly result from changes in the data pipeline. To validate production data integrity before it gets to the model, we have to monitor certain metrics based on data properties. This way, if the input data isn't what we expect or what we think the model expects, an alert can be triggered for the data team or service owner to take a look.

The primary goal of monitoring here is to flag any data quality issue, either from the client or due to an unhealthy data pipeline, before the data is sent to your model (which would generate unreliable predictions in response).

Some of the trackable causes of data quality issues are:
Preprocessing production data
In some cases, your streaming data pipeline will ingest data from multiple sources. It's easy for a change in one or more sources to cause a breakage in the data preprocessing step in the pipeline-GIGO (garbage in garbage out), pretty much.
Changes to the source data schema
In other cases, you could encounter situations when valid changes have been made to the data at the source, and preprocessing works just fine but it's not the sort of input configuration the model was trained on.

For example, a schema change could be the database admin for one of your data sources renaming a feature column and adding another column to catch new data. While this might scale through the preprocessing step, it sure would be a hassle for the model to predict, so it will likely give a partial response as the prediction. The schema change would mean that the model needs to be updated before it can map the relationship between the new feature column and the old ones.
Data loss/corruption at the source
In some other cases, a data pipeline may not be able to ingest data from one source because the data is unavailable, perhaps due to changes in a data source upstream, or data not being recorded. There could be cases where the upstream data source has corrupted or missing features. It's imperative to monitor issues like these as they will undoubtedly affect the overall performance of your system.
Data quality issue detection techniques
Write tests to detect data quality issues. Some data quality checks include:

Testing input data for duplicates,

Testing input data for missing values,

Catching syntax errors,

Catching data type and format errors,

Checking schema for semantic errors in terms of feature names,

Effective data profiling for complex dependencies in the data pipeline,

General integrity checks; does the data meet the requirements of downstream services or consumers?

Possible solutions after detecting data quality issues

Provide an alert following a schema change.

Ensure proper data validation practices are implemented by data owners.

Ensure everyone is aware of their role in getting the data to the pipeline and enable effective communication between data owners so that when a change is made at the data source, the model owner(s) and other service owners are aware.

2. Data/feature drift
Monitoring your input is perhaps the most vital aspect of functional monitoring. It could inform you ahead of time about the changing landscape and context around the business case you're solving. Models aren't intelligent enough to adjust to a changing world unless they're constantly retrained and updated.

Data drift refers to a meaningful change in distribution between the training data and production data. Changes in input data distribution will affect model performance over time, although it's a slower process than in the case of data quality issues.

While it's possible to monitor this drift at the level of the entire dataset, it is often advisable to monitor it at the feature level.
Feature/attribute drift
Monitoring at the feature level is often the best way to detect issues with input data. Getting this right is very helpful during the analysis stage when you're seeking explanations for model performance and behavior.

You can monitor feature drift by detecting changes in the statistical properties of each feature value over time. Some of these properties include standard deviation, average, frequency, and so on.

Feature drift can occur due to data quality issues or general changes in the real world, like changes in the preferences of business customers. An example of feature/attribute drift is illustrated below, where a historical set of attributes are used as a baseline and newer attributes are compared so that changes in the distribution of the attributes can be detected.

Oftentimes, the changes that degrade model performance the most are changes made to the most important features that the model uses to "connect the dots" and make predictions. Monitoring input (data) drift closely can give you a heads-up on model drift/model performance before it becomes problematic. When the distribution of the data changes, features drift, or other issues happen with the input, you can be alerted before they start degrading the performance of your model.
Data drift detection techniques
To detect data drift, perform distribution tests by measuring distribution changes using distance metrics:

Basic statistical metrics you could use to test drift between historical and current features are; mean/average value, standard deviation, minimum and maximum values comparison, and also correlation.

For continuous features, you can use divergence and distance tests such as Kullback-Leibler divergence, Kolmogorov-Smirnov statistics (widely used), Population Stability Index (PSI), Hellinger distance, and so on.

For categorical features, chi-squared test, entropy, the cardinality or frequency of the feature.

Some platforms (such as Fiddler) are now providing out-of-the-box monitoring solutions for outlier detection using machine learning and other unsupervised methods.

If the features are enormous, as is the case with a lot of datasets, you may want to prune them using dimensionality reduction techniques (such as PCA) and then perform the necessary statistical test.

If you're looking to learn more about these statistical measures, this white paper by Arize AI provides a conceptual and detailed look into these statistical checks.

Thankfully, most ML monitoring platforms (more in a coming section) provide some of these metrics out-of-the-box so you don't have to script them yourself.
Possible solutions after Data Drift detection

The most plausible solution is to trigger an alert and send a notification to the service owner. You might want to use an orchestration tool to kick off a retraining job with production data, and if the distribution change is really large, you might want to build another model with your new data.

Oftentimes, your new data won't be large enough for retraining your model or remodeling. So, you could combine and prepare your new data with historical (training) data and then, during retraining, assign higher weights to the features that drifted significantly from each other.

In other cases, you might be lucky to have your new production data sufficient for the task. In such a case, you can go ahead and build a challenger model(s), deploy it (either offline or online), and test using shadow testing or A/B testing approaches to determine if it's better than or as good as the champion (current) model in production.

3. Outliers
Monitoring outliers in your input data is crucial, but also very tricky. You're monitoring extreme and anomalous events that may be one-off events or a group of one-off events. This can inevitably affect model performance, as outliers don't have sufficient learnable structure across the entire dataset, which will cause the model to return an unreliable response as it won't quite "connect the dots" from your production data.
Outlier detection
You may not be able to set specific monitors to detect outliers, but what you can do is:

Use the tests we discussed in the previous section to determine if values and distributions of features are drastically different from normal benchmark periods-very noticeable drifts.

Perform statistical distance tests on single events or a small number of recent events detecting out-of-distribution issues.

Analyze if the features your model is most sensitive to-the most important features your model learned after training-have changed drastically.

Use any of the suitable distribution tests to determine how far off the features (outliers) are from the features in the training set.

Use unsupervised learning methods to categorize model inputs and predictions, allowing you to discover cohorts of anomalous examples and predictions. Some platforms use AutoML to detect outliers that your test can't catch.

Possible solutions after outlier detection

Perform data slicing methods on sub-datasets to check model performance for specific subclasses of predictions. You can automate this process as your model makes and logs predictions to an evaluation store using your monitoring tool.

If your model keeps performing poorly based on your metrics, you might want to consider evaluating the model at its current state and then training a new challenger model.

Document the issue and track if this is a seasonal outlier or an extreme, one-off outlier so you can strategize how to go about troubleshooting such problems in the future.

If the performance of the model can't be improved after retraining or the new model can't quite cut it, you might want to consider the model's performance benchmark and perhaps have a human in the loop, assisting the decision process for that period.

Model

The model is the most important piece to monitor, it's the heart of your entire system in production. With your model, you monitor its performance in production, components, version, and security threats.
1. Monitoring model drift
Model drift, or concept drift, happens when the relationship between features and/or labels-in cases of supervised or unsupervised learning solutions-no longer holds because the learned relationship/patterns have changed over time. It's when a model consistently returns unreliable and less accurate results over time compared to benchmarks or business metrics/KPIs.

An example is deploying a sentiment classification model. Over time, people's sentiment about any topic changes. If you trained your model on positive or negative sentiment with words and certain topics, some sentiments that were tagged as positive may over time evolve to be negative, you can't rule that out in our extremely opinionated social media world.

Model drift happens because the real-world changes (consequently the ground truth/target the model has been trained to predict)-the answers to business questions are always evolving. What holds today may no longer hold tomorrow, and we're expected to reflect this fact in our machine learning applications.

Model drift can be gradual, like when the business climate changes and evolves naturally, and it can also be sudden-as in cases when extreme events suddenly disrupt all operations.
Model drift can happen in different ways

Instantaneous model drift: Happens when there's a sudden drop in model performance over time. It could be a bug in the data pipeline causing data quality issues, or the model being deployed in a new domain, or outlier events (like a global crisis).

Gradual model drift: Most common type of model drift happens as a result of the natural consequences of a dynamic, changing, and evolving business landscape. It could happen as a result of user preferences changing over time, new demographics of customers adopting your product, or newly introduced features that skew the underlying pattern in the data.

Recurring model drift: This is the result of seasonal events that are periodic and recurring over a year-the pattern is always known and can be forecasted. These could be holidays and yearly discounts. In most cases, user preferences are seasonal or one model serves different regions.

Temporary model drift: This is quite difficult to detect by rule-based methods and is often detected using unsupervised methods. It happens due to strange, one-off events such as adversarial attacks, users using the product in a way that was not intended, a model temporarily serving newer clients, or system performance issues.

Model drift detection

You can detect model/concept drift using the same statistical tests as in the case of data drift.

Monitoring predictive performance (with evaluation metrics) of your model is reduced over time. By setting a predictive metrics threshold, you can confirm if your model consistently returns unreliable results and then analyze the prediction drift (changes in prediction results over time) from there.

Monitoring data drift can give you a heads-up on whether you should analyze your model for degradations or drifts.

Monitor label drift (changes in the distribution of real labels, for supervised learning solutions) when you can compare ground truth/actual labels to your model's prediction to analyze trends and new interpretations of data.

Possible solutions after detecting model/concept drift

Keep monitoring and retraining deployed models according to your business reality. If your business objectives and environment change frequently, you may want to consider automating your system to schedule and execute retraining at predefined intervals compared to more stable businesses (learn more about retraining here).

If retraining your models doesn't improve performance, you may want to consider remodeling or redeveloping models from scratch.

If you're working on larger scale projects with a good budget and little trade-off between cost and performance (in terms of how well your model catches up with a very dynamic business climate), you may want to consider online learning algorithms for your project.

2. Model configuration and artifacts
The model configuration file and artifacts contain all the components that were used to build that model, including:

Training dataset location and version,

Test dataset location and version,

Hyperparameters used,

Default feature values,

Dependencies and their versions; you want to monitor changes in dependency versions to easily find them for root cause analysis when model failure is caused by dependency changes,

Environment variables,

Model type (classification vs regression),

Model author,

Target variable name,

Features to select from the data,

Code and data for testing scenarios,

Code for the model and its preprocessing.

Track the configurations for relevance-especially the hyperparameter values used by the model during retraining for any abnormality.
3. Model versions
Monitoring model versions in production are critical if you want to be sure that the right version is deployed.

You can monitor model versions by configuring your retraining pipeline to automatically report model versions after training and log metadata to a metadata store. Version history should be logged to an evaluation store alongside model predictions, this way problems will be easier to tie to model versions.
4. Concerted adversaries
Every business faces security threats. With machine learning applications increasingly becoming the central decision system of most companies, you have to be concerned about the security of your model in production. Most machine learning models are susceptible to adversarial attacks.

This is especially common with machine learning applications in credit risk and financial institutions in general-for example, fraudsters may attempt to fool a model that is tasked with detecting suspicious credit card transactions.

Other applications susceptible to adversarial attacks include:

media companies adopting AI to detect fake news and other inappropriate content,

general applications that deal with audio or image recognition.

Concerted adversaries come directly from systems or hackers that intentionally engage your system through adversarial attacks. They mislead the system with adversarial examples so it can provide unreliable results and cause it to make mistakes. Such attacks (which are often anomalous) represent a specific problem in the safety of your machine learning application in production and need to be monitored.
You can monitor your system for adversarial attacks by:

Using the same steps you use to flag inputs with outlier events because adversarial threats don't follow a pattern, they're atypical events.
Possible solutions after detecting concerted adversaries
There's ongoing research on methods and algorithms that could defend models from adversarial threats, but most of this research is still at an early stage. The solutions to stopping concerted adversaries still require routing detected anomalous events to a human supervisor before predictions are used. Subject matter experts can study these cases and use them to defend the model from further threats.

In business-critical applications, speed matters. How fast experts detect an adversarial threat, study such a threat, patch the vulnerability by retraining the model, and redeploy the model may make all the difference for the business.

The Adversarial Robustness Toolbox (ART) by Trusted AI is worth checking out.

Predictions (Output)

Monitoring model output in production is not just the best indicator of model performance, but it also tells us if business KPIs are being met. In terms of model predictions, the most important thing to monitor is model performance in line with business metrics.
Model evaluation metrics
Using metrics to evaluate model performance is a big part of monitoring your model in production. Different metrics can be used here, such as classification, regression, clustering, reinforcement learning, and so on.

We typically evaluate the model using predefined model scoring metrics (accuracy, AUC, precision, etc) when you have a ground truth/label to compare your model with.
Ground truth/actual labels
Your ground truth/actual label is the correct solution to the problem your model tries to solve in the real world.

A basic example would be:

"Will this user click on this ad?"

If a user clicks on the ad, then the actual (label) is yes. An aggregation of the comparison between your model's predictions (if they clicked on the ad or not) and what the correct solution is can give you an overview of how well your model is doing in production. There's real-time feedback in this case because your system can tell almost immediately if a user clicked an ad or not. A comparison could look like this:

However, comparing actuals to predictions in production is a very difficult task in most cases. Especially if you don't have dedicated and expert annotators to log the actual labels for you if they're impossible to get in real-time. For example, a loan approval system wouldn't give you "correct solutions" in real-time because loan repayment options might take months, even years. This process involves a sophisticated feedback loop for us to be able to measure the performance of our model against real-world expectations.

Sometimes, you might have situations where your ground truth is influenced by your model's predictions.

For example, if you build a loan approval model to predict which customer will likely repay a loan, your model might perform well by appropriately approving loans for customers that will rightly pay back (that is, agreeing with the ground truth). But how do we know for sure that the customers it didn't approve wouldn't have paid you back? Well, the ground truth might not be the most appropriate source of truth on the performance of such a model.
Scoring models when ground truth is available
The most effective way to gauge the functional performance of your model in production is to monitor the prediction of the model and compare it to what's true in the real world.

To achieve this, model predictions are logged alongside the ground truth collected to give a concrete idea about performance in a production environment. Below is an example of how you can set up a monitoring system that collects ground truth data from various sources or a single source of truth:

At 1, a part of the production data (input data) is channeled to the ground truth service which typically involves real-time ground truth generated by your system (for example, logging if a user clicked on an ad when the model predicted they would), a human label annotator, or other data labeling vendors for more complicated tasks (such as confirming if a customer repaid a loan at the stipulated time, or confirming if a transaction was fraudulent or legitimate after contacting a customer).

The event id that tracks prediction and model details is tagged with that ground truth event and logged to a data store. The data is then ingested into the monitoring platform, which computes the model performance metric given the model's prediction and the actual label.

As you probably already know, metrics for a classification model include:

Accuracy

Confusion Matrix,

ROC-AUC Score,

Precision and Recall Scores,

F1-Score.

Metrics for a regression model include:

Calculating the model metrics above is only possible when you have the ground truth available.
Scoring models when ground truth is NOT available-prediction drift
How about when the ground truth isn't available or is compromised? We use the prediction results distribution as a performance proxy because that, hopefully, has been set in line with the business KPI.

Hopefully, your monitoring platform is set up in such a way that model predictions are also logged to a model evaluation store. A model evaluation store holds the response of the model (a signature of model decisions) to every piece of input data for every model version, in every environment. This way, you'll be able to monitor model predictions over time and compare the distribution using statistical metrics such as Hellinger Distance (HDDDM), Kullback-Leibler Divergence, and Population Stability Index (PSI).

There are also other cases for monitoring models in production that you may want to consider. Check out this useful playbook on monitoring model performance in production by Aparna Dhinakaran.

Operational level monitoring

Monitoring at the operations and system level is primarily the responsibility of the IT operations people or the DevOps team. But, it also has to be a shared responsibility between you and the Ops team. When things go bad at this level, alerts are typically forwarded to the Ops team to act on-but you can also opt to get a copy.

At this level, you're mostly monitoring the resources your model runs on (and runs in) in production and making sure that they're healthy. Resources such as pipeline health, system performance metrics (I/O, disk utilization, memory and CPU usage, traffic, things that ops people typically care about), and cost.

System performance monitoring for ML models in production

Operationalizing machine learning solutions in production is fundamentally an infrastructure problem. Monitoring model performance and data without monitoring the performance of your ML infrastructure is like building sand castles-your infrastructure is the foundation to a successfully deployed and maintained machine learning solution.

Monitoring your system/application performance can help you answer questions such as:

Does this application meet uptime requirements?

Is it serving requests quickly enough?

Is it efficiently utilizing resources and saving costs?

How about changes in code dependencies, can it handle it?

Does it meet scalability requirements?

What are its serving limitations?

System performance metrics
What we monitor here is influenced by the challenges we expect our model and data to face during the utilization. While this isn't your primary monitoring concern, you do need to be aware of certain metrics that can give you an indication of how your model performs in line with the entire application stack. If your model has high latency in returning predictions, that is bound to affect the overall speed of the system.

The system/application performance metrics to monitor that will give you an idea of model performance include:

CPU/GPU utilization when the model is computing predictions on incoming data from each API call; tells you how much your model is consuming per request.

Memory utilization for when the model caches data or input data is cached in memory for faster I/O performance.

Number of failed requests by an event/operation.

Total number of API calls.

Response time of the model server or prediction service.

Neptune gives us really good insight into simple data processing jobs that are not even training. We can, for example, monitor the usage of resources and know whether we are using all cores of the machines. And it's quick - two lines of code, and we have much better visibility. Hubert Bryłkowski, Senior ML Engineer at Brainly

System reliability
Here, we're monitoring the infrastructure and network uptime. How many clusters are running, which of the machines are running, and everything related to infrastructure. You're monitoring the number of clusters up and running at any given time, and which of the prediction services (if you have more than one) get the most requests.

This isn't our primary concern as ML engineers and data scientists, but don't you want to be aware of the reliability of the entire system that powers your model? It can't hurt to know these things.

Pipelines

Monitor the health of your data and model pipeline. Unhealthy data pipelines can affect data quality, and your model pipeline leakages or unexpected changes can easily generate negative value.

Data pipelines

Monitoring the health of data pipelines is extremely crucial because data quality issues can arise from bad or unhealthy data pipelines. This especially is extremely tricky to monitor for your IT Ops/DevOps team and may require empowering your data engineering/DataOps team to monitor and troubleshoot issues.

It also has to be a shared responsibility. Work with your DataOps team, communicate what your model expects, and the team will tell you what the output of their data pipeline is-this can help you tighten up your system and drive positive results.

If you're charged with the responsibility of monitoring your data pipeline, here are some metrics and factors you may want to track:

Input data - are the data and files in the pipeline with the appropriate structure, schema, and completeness? Are there data validation tests and checks in place so that the team can be alerted in case of an oddity in ingested data? Monitor what comes into the data pipeline to keep it healthy.

Intermediate workflow steps - are the inputs and outputs of every task and flow in the DAG as expected, in terms of the number of files and file types? How long does a task take to run in the pipeline? This could be the data preprocessing task, or the validation task, or even the data distribution monitoring task.

Output data - is the output data schema as expected by the machine learning model in terms of features and feature embeddings? What's the typical file size expected from an output file?

Data quality metrics - tracking the statistical metrics according to the data that flows in. This could be basic statistical properties of the data such as mean, standard deviation, correlation, and so on, or distance metrics (such as KL divergence, Kolmogorov-Smirnov statistic). The statistical metric used will be mostly dependent on the dimension of data expected; a couple of features or several features.

Scheduled run time of a job, actual run time, how long it took to run, and the state of the job (successful, or failed job?).

Model pipeline
You want to track crucial factors that can cause your model to break in production after retraining and being redeployed. This includes:

Dependencies - you don't want a situation where your model was built with Tensorflow 2.0 and a recent dependency update by someone else on your team that's bundled with Tensorflow 2.4 causes part of your retraining script to fail. Validate the versions of each dependency your model runs on and log that as your pipeline metadata, so dependency updates that cause failure can be easier to debug.

The actual time a retraining job was triggered, how long it took the retraining job to run, resources usage of the job, and the state of the job (successfully retrained and redeployed model, or failed?).

Cost
You need to keep an eye out for how much it's costing you and your organization to host your entire machine learning application, including data storage and compute costs, retraining, or other types of orchestrated jobs. These costs can add up fast, especially if they're not being tracked. Also, it takes computational power for your models to make predictions for every request, so you also need to track inference costs.

Your application may be hosted at cloud vendors such as AWS or Google Cloud. These vendors track your bills and how much each service is costing you and enable you to set budgets and send alerts if those budgets have been maxed. For an on-premise system, monitoring system usage and cost can help you analyze what part of the application is most costly, and see what compromises (or not) can be made.

Another metric you might want to consider monitoring is service level agreements (SLAs) in your application. The prediction service may have a set SLA with the data pipeline or the service that queries it. Make sure to set a threshold to trigger an alert when the SLA is not met.

We've covered a lot of things you might want to consider monitoring and it's quite overwhelming, isn't it? In the next section, you'll learn what to monitor at your current MLOps maturity level and this will give you an actionable idea of where to get started.

If you have a DevOps background, you may know that monitoring a system and observing the same system are two different approaches.

Observability is your ability to look at the metrics you've been monitoring and perform root-cause analysis on them to understand why they are a certain way, and what threat they pose to the overall performance of your system-all to improve system quality.

Monitoring is pretty much everything that happens before observability:

To put it simply, you can monitor without observing, but can't observe your system's overall performance without monitoring it. Monitoring is about collecting the dots, observability is about connecting them!

If you want to learn more about observability and monitoring for machine learning production systems, check out Christopher's article.

How to get started monitoring your Machine Learning models in production

Your next quest is to start monitoring your models after you deploy them. Below is a visual illustration of the step-by-step approach you should take, now that you know why you need to monitor and what you need to monitor.

1. Needs analysis: What to monitor based on your MLOps maturity level

There's a relatively large number of things to track and by different users, which makes monitoring a model in production a sophisticated process. We need tools and platforms to make things easier. What are the right tools and platforms and how can I know when I see one?

Here's the thing-ML monitoring is a constantly evolving landscape. I can guarantee you that there are no universally good tools, only tools best suited for your particular needs.

Laying out requirements and figuring out what your needs are upfront can be very tricky, so I figured it might be made simpler by meeting you at your MLOps "maturity stage" as highlighted in this Google Cloud blog post. I also summarized this with an illustration in the "Bringing Them All Together..." section.

You are currently at level 0 in your MLOps maturity stage

Being at this level means that you're training and deploying models manually. At this stage, you probably aren't even thinking of monitoring your model yet, perhaps just finding a way to validate your model on the test set and hand it off to your IT Ops or software developers to deploy.

I know because I was there. I celebrated when I handed it off, as mentioned at the beginning of this article, but as you know-a couple of months later-it has indeed ended in tears and on the hospital bed.

For you to avoid this scenario, I propose you prioritize the lowest hanging fruit. Although less informative, and won't help you monitor model performance, it can still serve as a reasonable performance proxy to tell you if your general application is working as intended.

You don't want to spend long hours focusing on monitoring your model's metrics or try to justify its performance in line with a business KPI when your workflow is still in its manual deployment stage; such metrics will get easier to measure and analyze when your MLOps system gets mature, and you can collect ground truth labels or integrate other performance proxies in the absence of ground truth.

You are currently at level 1 in your MLOps maturity stage

Being at this level means that you have automated the machine learning pipeline to enable continuous training of your machine learning models based on triggers that have been set by criteria or a defined threshold.

At this stage, I reckon you focus more on monitoring:

The business metric used to gauge your model's performance (see "What Could Go Right" section)-if it doesn't turn out to be pretty difficult to measure, especially if you can't spend them on getting ground truth for monitoring model metrics.

The properties of your production data and your model's performance in production to detect model staleness and degradation; can help with continuous training through triggers that automate the ML production pipelines to retrain models with new production data.

Your model's retraining process needs to log pipeline metadata, model configuration, and model metadata because you're most likely going to manually deploy a retrained model, and you want to make sure you can monitor the properties of that model before redeploying it to production.

You also need to monitor your production pipeline health as retraining steps are automated, and your data pipeline validates and preprocesses data from one or more sources.

You should also start monitoring how much your continuous training process is incurring so you don't wake up with a gigantic AWS bill one day that you or your company did not plan for.

You are currently at level 2 in your MLOps maturity stage

Being at this level indicates that you're completely mature in your MLOps implementation and pretty much the entire pipeline is a robust, automated CI/CD system. Your training, validation, and deployment phases are all automated in a complimentary feedback loop.

At this stage, you should pretty much monitor everything but your team's focus should be on the more informative metrics, making sure that all the relevant stakeholders are empowered with the more informative metrics before spending more time on the least informative metrics.

Bringing them all together...

Below is a visual illustration of what I reckon you should focus on monitoring depending on the maturity of your MLOps workflow.

While this isn't a hard and fast rule and can take a bit of brainstorming to figure out with your team, the way I think of it is for you to ask some questions:

What do we want from an MLOps monitoring platform or tool?

What are the most important metrics to us at this time?

What kind of insights do you want to derive? Are these insights directly in line with improving the performance of our entire application to ensure continuous positive business value?

What's 20% of the effort we can put in right now to get 80% of the result? 80%, in this case, is positive business value.

What is our existing ecosystem of tools? Do we have a dedicated team of ITOps/DevOps engineers?

How easy is it for an MLOps monitoring solution to integrate with the existing software deployment ecosystem?

Oftentimes, these are difficult to think through at a go, but they have to be done and constantly re-evaluated. I reckon you start with the question "What's 20% of effort we can put in right now to get 80% of the result?".

Once you can effectively document the answer to the above question with your team, tracking the necessary metrics is hard enough-you should start with a simple solution. Or at least a solution that helps you use fewer tools to monitor your most important metrics. Then you can iterate as you learn about the tools and your needs, adding richer tools if you need them and knowing how to use them.

Surveying existing ecosystem of tools

You know what to monitor based on your MLOps maturity level. If available, you might want to survey your organization for existing tools that your Ops/Engineering team uses to monitor deployed applications.

For example, you might be in an organization where your Ops team already uses Prometheus and Grafana for monitoring system performance and other metrics. That's probably good news for you as you may not have to worry too much about looking for a platform, you just set up and define your metrics, and/or write test cases for your model and data.

This is rarely the case, regardless. Oftentimes, you may have to be the one to set out and look for a platform that can help you monitor the metrics you need based on your needs analysis.
Be sure to survey your company, understand engineering and Ops culture (talk to the Ops team if you have one!), the tooling, and possible ecosystem integrations before you set out to find a platform that will be suitable for your needs.
Choosing a monitoring/observability platform

Choosing an observability platform can be very tricky, and you have to get it right. There's a lot of costs, security concerns in terms of data, and other challenges.

Thankfully, it can be simplified by:

Understanding your monitoring needs, which you probably do by this point in the article.

Knowing what you have available; survey your organization AND the budget on the ground for you to work with. Some platforms can be quite pricey with features that you don't need, and there are also open-source solutions available.

Taking into account the necessary qualities and observability platform that ML should have.

Considerations to make before choosing a monitoring/observability platform

For now, principles that have long worked for monitoring and observing software in production are being adopted for monitoring data and machine learning models in production.

Some platforms are mostly suited for monitoring and observing data quality (such as Monte Carlo and Anomalo) in production while others (such as superwise.ai and Arize AI) are general (production) machine learning observability platforms.

Below is an example Neptune dashboard that tracks some notable performance metrics of an application:

Your ML monitoring/observability platform should:

Be easy and intuitive to use, configurable by an individual service owner (you!) instead of a central ops team; putting a model to production is hard enough, don't make things harder for yourself.

Have out-of-the-box metrics, providing smart defaults for metrics on your registered model.

Have out-of-the-box and flexible integrations: Make sure platforms can integrate your production data with their systems through DB (database) connectors, API, file transfer, and generally be able to integrate data-whether batch or streaming-from any data source.

Have out-of-the-box visualization and dashboard: provide smartly prepopulated dashboards and graphs that visualize your metrics. You should also be able to add new panels, create visualizations with the necessary information, and create custom queries depending on your need.

Have flexible access: Some platforms offer you both programmatic access and access through a management console or user interface.

Be customizable: You should be able to define your metrics, write your test cases and checks, create custom dashboards and visualizations, custom checks and plugins, set custom thresholds, and generally set up custom integrations and workflow.

Be collaborative: This isn't a one-person job like others you may have seen. You should be able to share data, dashboards, and reports widely with relevant stakeholders and service owners. The platform should be able to empower each concerned stakeholder with insights and alerts on how best they can continue to provide positive business value based on our model predictions.

Have model explainability features: Some tools can help you explain model decisions by testing on a particular segment using methods like data slicing.

Be able to test model predictions to detect bias and threats (for example, adversarial attacks).

Be environment- and language-agnostic: You want a platform that can work regardless of what technology you used in building your model or what environment it's running on in production.

Be extensible to other platforms and easily integrate with an existing organization ecosystem.

Be able to auto-detect outliers: Platforms are beginning to use AutoML and unsupervised methods to detect anomalies in input data.

Have Cloud and/or on-premise deployment solutions: Most of the platforms offer either SaaS-based services or on-premise solutions. Even better, some offer both!

Be production-grade: Production is a very hard place, especially for models. You don't want a situation where the platform you choose cannot keep up with monitoring models that make thousands to even millions of predictions per second-essentially, think about the platform's scalability.

Granular point of view: The platform/tool should be able to provide you with a granular look at your data and model performance. For example; with this tool, can you drill into each feature in your production data to detect problems and drifts at the feature level? Can you monitor metrics (or an aggregate of the metrics) per second, per hour, per day, per month, and even per year? This will ensure flexibility irrespective of the use case (real-time or batch) that you're monitoring.

Perform log auditing: Audit logs from models, data, pipelines, and other system logs, for proper auditing and lineage traceability.

As you have seen, picking one tool requires thorough analysis. Below are some tools I have seen that tick all or most of the boxes above. If you want to learn more about some of the tools and deployment platforms you can choose, Dr. Ori Cohen has already explained most of them in this article and I urge you to take a look at them:

Other challenges you might encounter when monitoring Machine Learning systems in production

Challenges monitoring at input level

Some of the challenges you might encounter in production while monitoring your input data include:

Your data sources in production may be scattered and unreliable and as a result, it might be very difficult to unify them into a single source of truth or data mesh depending on your data architecture.

You do not have clear data requirements in terms of what the data structure and schema should be, or what an acceptable service level agreement (SLA) is for the upstream data source.

Your data sources don't have defined ownership and this could cause cross-functional miscommunication between teams, especially if someone makes an update to a data source (say a schema change to a DB) and doesn't inform others.

Metadata for your production data workflow is not discoverable and as a result, tracking data lineage becomes very difficult. Logging events as well; you wouldn't know the producer and consumer of the data in question making tracking, troubleshooting, and maintenance difficult.

Some of these challenges aren't just architectural but they can be cultural too. If data is a product, then:
If you're curious, you can learn more about "data as a product" thinking here.

Some challenges when monitoring model quality

Some of the challenges you might encounter in production while monitoring your input data include:

Ground truth availability; you will most likely encounter cases where you don't have real-time ground truth collection and might have to rely on upstream systems or even human annotators to collect actual labels.

Model biases that stem from algorithmic biases or dataset biases or sometimes both.

Black box models where predictions are unexplainable and could cause compliance issues.

Not tracking model metadata in production for lineage traceability. This one doesn't have to be an issue when you use a metadata store like neptune.ai- single place to manage all metadata.

A solution you can use is to perform data slicing to analyze segments of your model's predictions and test for bias. Some tools can automatically do this for you. Some biases peculiar to the dataset can also be prevented and/or eliminated through production data validation test cases. Biases peculiar to the model can be detected through the model metadata and/or metrics.

PRO-TIP: You may also want to take a look at Google's new web-based data exploration tool Know Your Data which, according to them, helps researchers, engineers, product teams, and decision-makers understand datasets to improve data quality, and helping mitigate fairness and bias issues.

Best practices for monitoring Machine Learning models in production

It should be clear at this point, there's a lot of work still to be done beyond deploying your model(s) to production.

This will help you understand if your model is performing as expected and solving the problem, or if it needs to be retrained, a challenger model is deployed, or if you need to redefine what good business performance looks like. Below are some best practices I recommend you begin with.

General monitoring best practices

Focus on people first. If you build a culture where data is also treated as the product in your organization, people will most likely be inclined to take ownership of the product to ensure it serves its intended purpose end-to-end. You can learn a lot from DevOps cultural change.

If it's possible, don't give the application's "monitoring power" to one person. If you have a cross-functional team of data professionals and Ops engineers, let everyone handle their service and communicate effectively. This will help decentralize knowledge and know-how and when the use cases scale, no one will be overwhelmed.

Take a lean approach; using too many tools can be very tasking. Centralize your tools but decentralize the team; everyone staying on top of a task.

Monitoring doesn't start after deployment, it starts when you begin experimentation. Build a culture of monitoring right from the model development stage (monitoring model experimentation metrics, logs, and so on).

Always consider what's optimal for the productivity of your team when you encounter any crucial decision-making point.

Encourage your team to properly document their troubleshooting framework and create a framework for going from alerting to action to troubleshooting for effective model maintenance.

Best practices for data monitoring

Batch and streaming data should be processed in the same manner, using the same pipeline so that issues with the data pipeline are a lot more intuitive to troubleshoot.

Ensure you go beyond checking for the drift for an entire dataset and look gradually at the feature drift as that can provide more insights.

Invest in a global data catalog that can help log high-quality metadata for your data that every user (your data and ML team) can rely on; it will help you tackle challenges with streaming and maintaining reliable data quality. It will also make lineage tracking easier.

Perform a pre-launch validation on your evaluation set before moving your model to production to establish a baseline performance.

Best practices for model monitoring

Model performance will inevitably degrade over time, but beware of a big dip in performance which is often indicative of something wrong-you can select tools that detect this automatically.

Perform shadow deployment and testing with the challenger model vs the champion model and log the predictions so that performance on the new model can be tracked alongside the current model in production; before you decide to deploy the newly trained (challenger) model.

You can use a metadata store (like neptune.ai) to store hyperparameters for models that have been versioned and retrained in production; this improves auditing, compliance, lineage traceability, and troubleshooting.

Best practices for monitoring predictions/output

Prediction drift can be a good performance proxy for model metrics, especially when ground truth isn't available to collect, but it shouldn't be used as the sole metric.

Track unreasonable outputs from your model. For example, your classification model predicting the wrong class for a set of inputs with a high confidence score, or your regression model predicting a negative score (when the base metric score should be 0) for a given set of features.

Alerting is a crucial part of monitoring. If you track your metrics but aren't notified when something goes wrong, why even track them? You definitely want to be notified when something goes wrong.

Inevitably, different things with varying degrees of priority will go wrong. How do you separate the wheat from the chaff? Some tools offer out-of-the-box and smart alerting features but oftentimes, it will boil down to what makes sense to a particular business, based on that application.

Setting up alerts the proper way

The best set of advice I got on actionable alerts is from Ernest Mueller and Peco Karayanev in one of their DevOps courses;

Test your alerts before they go into production

For example, in the case of data drift, write test cases that simulate the statistical metrics you're using to monitor distribution changes between a development (validation) dataset and the production data you're simulating. Some platforms (like Domino) automatically apply these statistical checks for you so all you have to do is to register your development data and integrate the production data you're using for simulation. Select a particular statistical check and set a threshold.

Monitor the primary metrics as concluded in your needs analysis.

Agree on the media for the alert, so every service owner is comfortable with their medium. Slack? PagerDuty? Information radiators? Other team chats? E-mails?

Send context to the alert by including descriptive information and action by the primary service owner.

Make sure to set up a feedback loop that makes your monitoring better. In the case of data drift that has triggered an alert because it's greater than a set threshold, you might want to use a pipeline orchestration tool like Apache Airflow to kick-off retraining if data is automatically being logged. Or, the team might have to manually retrain on new data, or entirely remodel the problem off the new data.

Best practices for alerting

Ensure you and your team are clear on who gets what alert. If possible, data quality alerts should go mainly to the dataops/data engineering team, model quality alerts to the ML team or data scientist, and system performance alerts to the IT ops team.

Only set alerts when you know there's a condition that requires intervention. For example, set an agreed threshold for data quality and alerts should only be triggered when that threshold condition is not met.

Understand what has real business impact and alert for those only, not just anything that goes wrong with the application.

Indulge the team to characterize alerts they get (whether they were false positives, negatives, or true positives), document the actions they took as well as the outcomes.

Essentially, you want to avoid "alert hell"; a flurry of irrelevant alerts that may have you losing track of the real, business-impacting alert in the noise.

The best tools will also help you manage your alerts properly, so be on the lookout for this feature!

What if I told you that you could save a lot of time putting out a fire outbreak with your machine learning application in production by just knowing where the source of the fire is?

Enter-logging! In most cases, log monitoring can give you a massive head start in troubleshooting problems with any component of your entire system in production.

So why should you log?

Simple-because you want to make sure you can put out the fire easily when push comes to shove, identify the source of the fire, and take measures to prevent further fire outbreaks in the future.

Interpreting that in machine learning speak-you log because you want to be able to troubleshoot problems effectively by quickly identifying the cause or potential cause, taking the necessary measures to solve such problems, and patching that loophole in your system (or at least keeping it in check).

One more thing, you log for auditing and compliance! Being able to log your data and model events will help create data and model lineage that can be traced and audited for compliance requirements and standards by the relevant stakeholders.

So what should you log?

While it's fancy to log every activity of every component in your system (from data to model to application and everything else), the volume will always end up being a problem. By volume, I mean storage and the resources that will be used to host and process the log files generated in the process.

Be very strategic about what you log. You probably don't have the budget to log every activity of every component of your system. You should only log real problems-not everything. Essentially, what you log should have a somewhat consequential impact on the business value your application is serving.

Some of the objects for the data and model components you should log include:

Data pipeline events,

Production data (if possible, include the metadata alongside),

Model metadata; this includes the model version and configuration details,

Prediction results from the model,

Prediction results from shadow tests (challenger models); if applicable to your system,

Ground truth label (if available),

General operational performance (that is typical to standard monitoring systems).

The most important thing to note is to keep a close eye on the volume. Logged files can grow up to gigabytes and take resources to host and parse, so you want to make sure that it's included in the budget you set out with at the beginning.

Also, take into account what will cost more-regulator charge for not being able to audit your system for compliance requirements, or the amount of storage and resources logging the necessary objects will take.

Some useful logging tools include neptune.ai, New Relic, Honeycomb.io, and Datadog.

Best practices for logging

For your pipeline, you should be logging runs from scheduled time to start time, end time, job failure errors, the number of runs; all to make an unhealthy pipeline easier to troubleshoot.

For your models, you should be logging the predictions alongside the ground truth (if available), a unique identifier for predictions (prediction_id), details on a prediction call, the model metadata (version, name, hyperparameters, signature), the time the model was deployed to production.

For your application, you should be logging the number of requests served by the champion model in production, average latency for every serving.

For your data, log the version of every preprocessed data for each pipeline run that was successful so that they can meet audited and their lineage can be traced.

For storing the structure of your logs, consider using a JSON format with an actual structure so they can be easily parsed and searched.

Consider rotating log files for better management; delete old and unnecessary logs that you're sure you won't need again for auditing or other reasons.

According to Ernest Mueller, the best logs are formatted, have good time stamps and severity levels, and provide plenty of context to a user.

Oh! It doesn't stop at monitoring, either? 🙁

There's a reason you deployed your model to production and there's a reason you continuously monitor it there-to continue providing positive business value.

Do you know what negative business value is? When regulators or your customers demand to know how your system comes up with decisions and you can't provide it, you're canceled, customers churn, or worse, you're fined a hefty amount by regulators (just ask Goldman Sachs!).

Or when your recommendation system serves the same recommendation to all users for 30 days straight (real-life incident here).

For monitoring to be truly complete, you need to:

Continuously manage the model in production to make sure it doesn't slope towards negative business value,

Make sure the decision-making process of the model can be governed, audited, and explained.

Here's a great tutorial for learning more about model management in production.

Getting practical through a live sandbox

We've covered a lot of practical concepts in this article, it's time to put that into great use by checking out, working with, and/or configuring a monitoring solution of your own.

Try ML Monitoring With A SaaS Solution: Whylabs.ai offers a nice live sandbox you can use to check monitor metrics from events like data drift, track model performance, see alerts on data quality issues, and so on.

Try Hosting Your Solutions: If you want to use an open-source solution, Prometheus + Grafana is perhaps your best bet! Here's a good practical tutorial on how to monitor your containerized and served model (using FastAPI) with Prometheus and Grafana.

If you want to get a practical understanding of how a company approaches Machine Learning monitoring and observability, you can check out DoorDash's great article here.

Conclusion

Yup! Monitoring is something I didn't do, which is how I ended up with the DevOps guy trying to NOT get involved in all the insanity.

I hope you gained a couple of useful tips on how to monitor your machine learning models in production-and not end up like me! I hope you were able to understand why deployment is never the final step, and you now have enough clarity on where to start in terms of monitoring your models in production.

To conclude this article, I would like to give solutions to the challenges from an earlier section below:

Happy monitoring!

References and other resources on ML monitoring in production

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As organizations overcome the pandemic-induced disruptions, they need to focus more on strengthening their supply chain and logistics capacity. Leveraging AI can be an effective way of doing that. According to McKinsey, the successful implementation of AI has helped businesses improve logistics costs by 15%, Inventory levels by 35%, and service levels by 65%.

Another research by McKinsey estimates that logistics companies will generate $1.3-$2 trillion per year for the next 20 years in economic value by adopting AI into their processes.

In this article, we explore top AI use cases in the logistics industry and how they improve logistics operations.

Logistics Planning

Logistics requires significant planning that requires coordinating suppliers, customers, and different units within the company. Machine learning solutions can facilitate planning activities as they are good at dealing with scenario analysis and numerical analytics, both of which are crucial for planning.

1. Demand forecasting

AI capabilities enable organizations to use real-time data in their forecasting efforts. Therefore, AI-powered demand forecasting methods reduce error rates significantly compared to traditional forecasting methods such as ARIMA, AutoRegressive Integrated Moving Average, and exponential smoothing methods.

With improved accuracy in demand prediction,

manufacturers can better optimize the number of dispatched vehicles to local warehouses and reduce operational costs since they improve their manpower planning

local warehouses/ retailers can reduce the holding costs (opportunity cost of holding the item instead of investing the money elsewhere)

customers are less likely to experience stockouts that reduce customer satisfaction

You can also see our list of AI tools and services:

2. Supply planning

Artificial intelligence helps businesses analyze demand in real-time so that organizations update their supply planning parameters dynamically to optimize supply chain flow. With dynamic supply planning, businesses use fewer resources since dynamic planning minimizes waste.

Automated Warehousing

Figure 1. The MHI annual industry report projects that by 2026 the adoption of AI-powered warehouse solutions by businesses will reach 60+% as compared to 2020.

3. Warehouse robots

Warehouse robots are another AI technology that is invested heavily to enhance businesses' supply chain management. The warehouse robotics market was valued at USD 4.7 billion in 2021 and is expected to grow at a CAGR of 14% between 2021 and 2026.

For example, the retail giant Amazon acquired Kiva Systems in 2012 and changed its name to Amazon Robotics in 2015. Today, Amazon has 200,000 robots working in their warehouses. In 26 of Amazon's 175 fulfillment centers, robots help humans in picking, sorting, transporting, and stowing packages.

4. Damage detection / Visual Inspection

Damaged products can lead to unsatisfied customers and churn. Computer vision technology enables businesses to identify damages and ensure quality control in warehouse operations. Logistics managers can determine the size and type of damage and take action to reduce further damage.

5. Predictive maintenance

Predictive maintenance is predicting potential machine failures in the factory by analyzing real-time data collected from IoT sensors in machines. Machine learning-powered analytics tools enhance predictive analytics and identify patterns in sensor data so that technicians can take action before the failure occurs.

Autonomous Things

Autonomous things are devices that work without human interaction with the help of AI. Autonomous things include self-driving vehicles, drones, and robotics. We should expect to see more autonomous devices in the logistics industry due to the industry's suitability for AI.

6. Self-driving vehicles

Self-driving cars have the potential to transform logistics by decreasing heavy dependence on human drivers. Recent surveys suggest that real-life use cases of autonomous cars and trucks will be seen in the near future (see figure 2). Technologies such as platooning support drivers' health and safety while reducing carbon emissions and fuel usage of vehicles. Tesla, Google, and Mercedes Benz are investing heavily in the concept of autonomous vehicles, it is only a matter of time before autonomous trucks are seen on roads around the world. However, according to BCG estimations, only around 10 % of light trucks will drive autonomously by 2030.

Figure 2. L4 use cases for autonomous vehicles are expected to emerge by 2024 or 2025

7. Delivery drones

For the logistics of products, delivery drones are useful machines when businesses deliver products to places where a ground transfer is not possible, safe, reliable, or sustainable. Especially in the healthcare industry, where pharmaceutical products have a short shelf life span, delivery drones can help businesses reduce waste costs and prevent investments in costly storage facilities.

Analytics

8. Dynamic Pricing

Dynamic pricing is real-time pricing, where the price of a product responds to changes in demand, supply, competition price, and subsidiary product prices. Pricing software mostly uses machine learning algorithms to analyze customers' historical data in real-team so that it can respond to demand fluctuations faster by adjusting prices.

9. Route optimization / Freight management

AI models help businesses to analyze existing routing and track route optimization. Route optimization uses shortest-path algorithms in graph analytics discipline to identify the most efficient route for logistics trucks.

Therefore, the business will be able to reduce shipping costs and speed up the shipping process. For example, Valerann 's Smart Road System is an AI web-based traffic management platform that delivers information about road conditions to autonomous vehicles and users.

Route optimizers are also effective tools for reducing corporate carbon footprint.

Back office

Every business unit has back-office tasks and logistics are no different. For example, there are numerous logistics-related forms like a bill of lading from which structured data needs to be manually extracted. Most businesses do this manually.

10. Automating document processing

Invoice/bill of lading/rate sheet documents helps communication between the buyers, suppliers, and logistics service providers. Document automation technologies can be used to increase the efficiency of processing these documents by automating data input, error reconciliation, and document processing.

11. Automating other manual office tasks

Hyperautomation, also referred to as intelligent business process automation, means using a combination of AI, robotic process automation (RPA), process mining, and other technologies to automate processes in an end-to-end manner. With these technologies, businesses can automate several back-office tasks, such as

Scheduling and tracking: AI systems can schedule transportation, organize pipelines for cargo, assign and manage various employees to particular stations, and track packages in the warehouse.

Report generation: Logistics companies can use RPA tools to auto-generate regular reports that are required to inform managers and ensure everyone in the company is aligned. RPA solutions can easily auto-generate reports, analyze their contents and, based on the contents, email them to relevant stakeholders.

Email processing: Based on contents in auto-generated reports, RPA bots can analyze the content and send emails to relevant stakeholders.

For more RPA and hyperautomation use cases for businesses' back-office tasks, feel free to read our articles:

12. Customer service chatbot

Customer service plays an important role in logistics companies since customers will contact companies for any issue they experience in delivery. Customer service chatbots are capable of handling low-to-medium call center tasks such as:

Requesting a delivery

Amending an order

Tracking shipment

Responding to a FAQ

Chatbots are also valuable tech to analyze customer experience; chatbot analytics metrics enable businesses to understand their customers better so that they can enhance the customer journey they deliver.

To learn more about AI applications in customer service, feel free to read our article: 11 AI Usecases in Customer Service.

Sales & marketing

Sales and marketing activities of logistics service providers can also be enhanced by artificial intelligence. Some applications are:

13. Lead scoring

Lead scoring means enabling sales reps to focus on the right prospects. AI-powered tools can be used to help automatically assign scores to leads based on their profiles, behavior, and interests. AI-based lead scoring systems utilize machine learning algorithms to quickly process data and accurately determine which leads are most likely to convert into paying customers.

14. Routine marketing

AI can be used to help logistics service providers automate routine marketing tasks, such as email marketing and content creation.

15. Sales and marketing analytics

AI can offer more precise sales and marketing analytics. AI-powered tools can be used to help logistics service providers analyze customer behavior and use predictive analytics to better understand what their customers are likely to do next. AI-enabled systems can also be utilized to monitor changes in the market, enabling logistics service providers to stay ahead of the competition and make data-driven decisions that will result in greater efficiency.

For more AI applications in sales and marketing, you can check our articles:

To learn more

Leave us a comment if you know of other applications of AI in logistics. Here is a list of more AI-related articles you might be interested in:

If you still have questions about AI and/or logistics, we would like to help:

Find the Right Vendors

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Top 15 Use Cases and Applications of AI in Logistics in 2023

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Explore how AI is changing logistics industry by improving supply planning, demand forecasting or warehousing with AI analytics, warehouse robots & more

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Avoiding Overreliance - Especially as We Can't Rely on All of the Data

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Avoiding Overreliance - Especially as We Can't Rely on All of the Data

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Methods for Preventing Over-Reliance on Artificial Intelligence

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Artificial intelligence (AI) can indeed contribute to laziness and codependence in certain contexts, although it’s important to note that these effects are not inherent to AI itself but rather the way it is used and implemented. Here are a couple of ways in which AI can potentially contribute to the

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Business Considerations Before Implementing AI

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Business Considerations Before Implementing AI

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Business leaders and AI practitioners must ask the right questions before embarking on an AI project.

Artificial Intelligence (AI) is poised to transform many industries in the coming years, including augmenting human intelligence, powering automation, enabling optimization, offering decision support, paving the way for hyper-personalization and making possible natural interfaces to many business applications. Companies are actively exploring, experimenting and deploying AI-infused solutions in their business processes. Chatbots in customer support scenarios, doctors' assistants in hospitals, legal research assistants in the legal domain, marketing manager assistants in marketing, and face detection applications in the security domain are some early use cases of AI in enterprise.

Many things must come together to build and manage AI-infused applications. Data scientists who build machine learning models need infrastructure, training data, model lifecycle management tools and frameworks, libraries, and visualizations. Similarly, an IT administrator who manages the AI-infused applications in production needs tools to ensure that models are accurate, robust, fair, transparent, explainable, continuously and consistently learning, and auditable. This requires new tools, platforms, training and even new job roles. AI-infused applications should be consumable in the cloud (public or private) or within your existing datacenter or in a hybrid landscape. All this can be overwhelming for companies trying to deploy AI-infused applications.

To help determine if AI is the right choice for your company and your business problem, CompTIA's Artificial Intelligence Advisory Council developed questions and answers that business decision-makers and AI practitioners should consider before investing in AI. While both decision-makers and practitioners have their own points to consider, it's recommended that they work in tandem to make the best, most appropriate decision for their respective environments.

Considerations Before Implementing AI: Questions for Business Decision Makers

When determining whether your company should implement an artificial intelligence (AI) project, decision makers within an organization will need to factor in a number of considerations. Use the questions below to get the process started and help determine if AI is right for your organization right now.

Artificial Intelligence Project Planning

1. Do we have executive sponsorship to infuse AI within existing business processes?

Artificial intelligence (AI), when implemented properly, has the potential to optimize and automate business processes and provide decision support to humans. AI might even lead to new business opportunities via new business models. To experience the true benefits, organizations will need to be flexible and adapt. Executive-level sponsorship is critical before embarking on a new AI project. Executive buy-in must include financial and human resources support as well as cultural

2. Have we clearly defined the business objectives and outcomes to be achieved using AI?

Defining milestones for an AI project upfront will help you determine the level of completion or maturity in your AI implementation journey. The milestones should be in line with the expected return on investment and business outcomes.

For example, companies may choose to start with using AI as a chatbot application answering frequently asked customer support questions. In this case, the initial objective for the AI-powered chatbot could be to improve the productivity of customer support agents by freeing up their time to answer complex questions. A milestone would be a checkpoint at the end of a proof-of-concept (PoC) period to measure how many questions the chatbot is able to answer accurately in that timeframe. Once the quality of AI is established, it can be expanded to other use cases.

3. Have we set the right initial expectations about the potential benefits of AI?

AI involves multiple tools and techniques to leverage underlying data and make predictions. Many AI models are statistical in nature and may not be 100% accurate in their predictions. Business stakeholders must be prepared to accept a range of outcomes (say 60%-99% accuracy) while the models learn and improve. It is critical to set expectations early on about what is achievable and the journey to improvements to avoid surprises and disappointments.

4. Do we have the budget required to support near-term and long-term AI objectives?

Embarking on a new AI project or expanding from an initial pilot phase will require a separate budget to cover new tools and technologies as well as expertise that may not be present within your organization. Consider these tips when determining the scope of your budget:

Review your projected business value from the AI project over a 12- to 36-month period.

Consider the total budget including internal headcount, contract resources, and IT infrastructure (including application and cloud licenses) to calculate your total investment on an AI project.

Review cost of data acquisition (sourced internally or externally) as part of the total budget.

5. Do we understand the timeline needed to successfully deploy an AI project within our organization?

AI projects typically take anywhere from three to 36 months depending on the scope and complexity of the use case. Often, business decision makers underestimate the time it takes to do "data prep" before a data science engineer or analyst can build an AI algorithm. There are certain open source tools and libraries as well as machine learning automation software that can help accelerate this cycle.

Once you have identified a project or a business challenge, you can begin planning for a proof of concept (PoC), which will include data sources, technology platforms, tools and libraries to train the AI models leading to predictions and business outcomes. Depending on the use case and data available, it may take multiple iterations to achieve the levels of accuracy desired to deploy AI models in production. However, that should not deter companies from deploying AI models in an incremental manner. Error analysis, user feedback incorporation, continuous learning/training should be integral parts of AI model lifecycle management.

Lastly, nearly 80% of the AI projects typically don't scale beyond a PoC or lab environment. Businesses often face challenges in standardizing model building, training, deployment and monitoring processes. You will need to leverage industry tools that can help operationalize your AI process-known as ML Ops in the industry.

Nearly 80% of the AI projects typically don't scale beyond a PoC or lab environment. Businesses often face challenges in standardizing model building, training, deployment and monitoring processes.

6. Can we manage market or competitive pressures to accelerate AI infusion within our organization?

Despite the hype, in McKinsey's Global State of AI report, just 16% of respondents say their companies have taken deep learning beyond the piloting stage. While many enterprises are at some level of AI experimentation-including your competition-do not be compelled to race to the finish line. Every organization's needs and rationale for deploying AI will vary depending on factors such as fit, stakeholder engagement, budget, expertise, data available, technology involved, timeline, etc. No two projects between organizations will look similar.

IT Infrastructure and Data Management

7. Can we access the data that exists within our organization to meet our project goals?

Data often resides in multiple silos within an organization in multiple structured (i.e., sales, CRM, ERP, HRM, marketing, finance, etc.) or unstructured (i.e., email, text messages, voice messages, videos, etc.) platforms. Depending on the size and scope of your project, you may need to access multiple data sources simultaneously within the organization while taking data governance and data privacy into consideration. Additionally, you may need to tap into new, external data sources (such as data in the public domain). Expanding your data universe and making it accessible to your practitioners will be key in building robust artificial intelligence (AI) models.

8. Have we considered data governance issues as related to compliance and privacy?

Data preparation for training AI takes the most amount of time in any AI solution development. This can account for up to 80% of the time spent from start to deploy to production. Data is the longest pole in the tent. Data in companies tends to be available in organization silos, with many privacy and governance controls. Some data maybe subject to legal and regulatory controls such as GDPR or HIPAA compliance. Having a solid strategy and plan for collecting, organizing, analyzing, governing and leveraging data must be a top priority.

Large organizations may have a centralized data or analytics group, but an important activity is to map out the data ownership by organizational groups. There are new roles and titles such as data steward that help organizations understand the governance and discipline required to enable a data-driven culture. Additionally, as AI taps into multiple new and old data sources, you will need to ensure that privacy and compliance guidelines are adhered to during the model development and training or inference process (i.e., facial recognition data that can be tracked to an individual in retail or banking or patient data protected by HIPAA).

Data preparation for training AI takes the most amount of time in any AI solution development. This can account for up to 80% of the time spent from start to deploy to production.

9. Do we have the required skillset/domain expertise within the organization to execute on an AI vision?

Infusing AI into business processes requires roles such as data engineers, data scientists, and machine learning engineers, among others. Organizations should consider their current team and then determine a people strategy, which could include reusing or repurposing existing resources, upskilling and training current staff, hiring, or working with outside consultants or contractors. Some organizations might need to contract with a third-party IT service partner to provide supplementary, needed IT skills to model data or implement the software.

As the organization matures, there are several new roles to be considered in a data-driven culture. Depending on the size of the organization and its needs new groups may need to be formed to enable the data-driven culture. Examples include an AI center of excellence or a cross-functional automation team. Take into consideration the end-to-end requirements during your planning phase as getting the right skillset-whether it is building your own or utilizing outside expertise like consultants-will take time and impact your project delivery timelines.

10. Do I have the right IT infrastructure needed to deploy my AI solution? Do I know what questions to ask vendors when evaluating solutions?

Myth: I can run my AI project with open source tools and in the cloud today.

Reality: This is partially true only if you are running a small proof of concept (PoC) for kickstarting your AI project. AI projects by definition are complex; however, their implementation doesn't have to be. There are industry vendors available today to assist you in your AI journey. However, you need to be cautious in your selection of the right vendor(s). There are hundreds of startups and emerging vendors that may not have the resources or investment capital to sustain in the long run. Keep in mind that AI solutions are not cookie cutter. While most solutions available today may meet 80% of your requirements, you will still need to work on customizing the remaining 20%.

Begin by researching use cases and white papers available in the public domain. These documents often mention the types of tools and platforms that have been used to deliver the end results. Explore your current internal IT vendors to see if they have offerings for AI solutions within their portfolio (often, it's easier to extend your footprint with an incumbent solution vendor vs. introducing a new vendor). Once you build a shortlist, feel free to invite these vendors (via an RFI or another process) to propose solutions to meet your business challenges. Based on the feedback, you can begin evaluating and prioritizing your vendor list.

Checklist: Questions to Tech Vendors When Evaluating AI Solutions

How long have you been offering AI solutions?

Do you have any use cases/examples you can share that align closely with my industry or organization's needs?

Do you have alliances with multiple vendors and an ecosystem to deliver me a complete solution?

Do you have the resource bench to assist me in deploying this solution across the enterprise and across geographies (probe for roles like data scientists, data engineers, machine learning architects, service engineers, etc.)?

Can you integrate the AI solution within my existing IT footprint?

While most AI solutions available today may meet 80% of your requirements, you will still need to work on customizing the remaining 20%.

Implementation and Ongoing Management

11. Can we trust AI and make the business decisions based on the model predictions? How do I course correct if an AI model or prediction goes wrong?

Most artificial intelligence (AI) models will make prediction mistakes. No AI model, be it a statistical machine learning model or a natural language processing model, will be perfect on day one of deployment. Therefore, it is imperative that the overall AI solution provide mechanisms for subject matter experts to provide feedback to the model. AI models must be retrained often with the feedback provided for correcting and improving. Carefully analyzing and categorizing errors goes a long way in determining where improvements are needed.

A mature error analysis process should enable data scientists to systemically analyze a large number of "unseen" errors and develop an in-depth understanding of the types of errors, distribution of errors, and sources of errors in the model. A mature error analysis process should be able to validate and correct mislabeled data during testing. Compared with traditional methods such as confusion matrix, a mature process for an organization should provide deeper insights into when an AI model fails, how it fails and why. Creating a user-defined taxonomy of errors and prioritizing them based not only on the severity of errors but also on the business value of fixing those errors is critical to maximizing time and resources spent in improving AI models. It is important to select vendors who can offer the full AI model lifecycle management capabilities as opposed to just a model that can make initial predictions but are incapable of taking feedback or learning from feedback and self-reflection via error analysis.

12. Can we market our value proposition or differentiate our organization from competition using AI-infused solutions?

As a decision maker/influencer for implementing an AI solution, you will grapple with demonstrating ROI within your organization or to your management. However, if you plan the AI infusion carefully with a strategic vision backed by tactical execution milestones in collaboration with the key business stakeholders and end users, you will see a faster adoption of AI across the organization.

AI value translates into business value which is near and dear to all CxOs-demonstrating how any AI project will yield better business outcomes will alleviate concerns they may have.

13. Can the organization reconcile the impact of AI on staff resourcing and culture?

The goal of AI is to either optimize, automate, or offer decision support. AI is meant to bring cost reductions, productivity gains and in some cases even pave the way for new products and revenue channels. All this may result in change to the way people do their work. In some cases, people's time will be freed up to perform more high-value tasks. In some cases, more people may be required to serve the new opportunities opened up by AI and in some other cases, due to automation, fewer workers may be needed to achieve the same outcomes. Companies should analyze the expected outcomes carefully and make plans to adjust their work force skills, priorities, goals, and jobs accordingly. Managing AI models requires new type of skills that may or may not exist in current organizations. Companies have to be prepared to make the necessary culture and people job role adjustments to get full value out of AI.

The goal of AI is to optimize, automate, or offer decision support. AI is meant to bring cost reductions, productivity gains, and in some cases even pave the way for new products and revenue channels.

Considerations Before Implementing AI: Questions for Practitioners

It's critical for the practitioners of artificial intelligence (AI) solutions-those using and supporting the solutions and analyzing the data-to have a different but no less important understanding of the technology and its benefits and challenges. The following are some questions practitioners should ask during the AI consideration, planning, implementation and go-live processes.

1. What is the goal and purpose of the AI model I'm asked to build? Do I have representative training and test data for building this AI model? If not, is there is a data manager I can work with to acquire or prepare the data?

AI models must be built upon representative data sets that have been properly labeled or annotated for the business case at hand. Attempting to infuse AI into a business model without the proper infrastructure and architecture in place is counterproductive. Training data for AI is most likely available within the enterprise unless the AI models that are being built are general purpose models for speech recognition, natural language understanding and image recognition. If it is the former case, much of the effort to be done is cleaning and preparing the data for AI model training. In latter, some datasets can be purchased from external vendors or obtaining from open source foundations with proper licensing terms.

2. Is my data architecture adequate for leveraging AI?

A company's data architecture must be scalable and able to support the influx of data that AI initiatives bring with it. Data architecture involves instrumenting business processes, applications and infrastructure to collect data, building data connectors to observe, collect and store data, data lake strategy and implementation, data versioning, lineage management, data bias checking and normalization. If data is required to be collected or purchased from external sources, proper data management function is needed to ensure data is procured legally and that compliance standards are met in data storage, including GDPR-type of compliance management.

3. How can I ensure biased data won't skew results?

Biased training data has the potential to create not only unexpected drawbacks but also lead to perverse results, completely countering the goal of the business application. To avoid data-induced bias, it is critically important to ensure balanced label representation in the training data. In addition, the purpose and goals for the AI models have to be clear so proper test datasets can be created to test the models for biases. Several bias-detection and debiasing techniques exist in the open source domain. Also, vendor products have capabilities to help you detect biases in your data and AI models. Leverage those capabilities.

Biased training data has the potential to create unexpected drawbacks and lead to perverse results, completely countering the goal of the business application.

4. Will robotic process automation, or a cheaper, non-AI process deliver the same outcome?

AI has its time and place. Not every automation needs to be solved with AI. Some automations can likely be achieved with simpler, less costly and less resource-intensive solutions, such as robotic process automation. However, if a solution to the problem needs AI, then it makes sense to bring AI to deliver intelligent process automation.

5. What AI or machine learning (ML) methods will be relied upon and why?

AI and ML cover a wide breadth of predictive frameworks and analytical approaches, all offering a spectrum of advantages and disadvantages depending on the application. It is essential to understand which approaches are the best fit for a particular business case and why.

6. How long will implementation take?

Understanding the timeline for implementation, potential bottlenecks, and threats to execution are vital in any cost/benefit analysis. Most AI practitioners will say that it takes anywhere from 3-36 months to roll out AI models with full scalability support. Data acquisition, preparation and ensuring proper representation, and ground truth preparation for training and testing takes the most amount of time. The next aspect that takes the most amount of time in building scalable and consumable AI models is the containerization, packaging and deployment of the AI model in production.

7. Do I understand the legal, privacy, compliance, security implications of building AI solutions at this company?

Different industries and jurisdictions impose varying regulatory burdens and compliance hurdles on companies using emerging technologies. With AI initiatives and large datasets often going hand-in-hand, regulations that relate to privacy and security will also need to be considered. Data lake strategy has to be designed with data privacy and compliance in mind. Many vendor tools offer these capabilities. Companies must make decisions about and understand the tradeoffs with building these capabilities in-house or working with external vendors.

8. Will the solution offer explainable results and transparency into the decision-making process?

Consumers, regulators, business owners, and investors may all seek to understand the process by which an organization's AI engine makes decisions, especially if those decisions can impact the quality of human lives. Black box architectures often do not allow for this, requiring developers to give proper forethought to explainability. Data scientists must make tradeoffs in the choice of algorithms to achieve transparency and explainability.

9. Will the solution provide an adequate level of accuracy, given the application?

Depending on the use case, varying degrees of accuracy and precision will be needed, sometimes as dictated by regulation. Understanding the threshold performance level required to add value is an important step in considering an AI initiative. In some cases, precision and recall tradeoffs might have to be made. For example, when analyzing sentiment in social media context, precision might be more important than recall whereas in a security scenario recall is just important as precision since you may not want to miss out on any security violation incidents.

10. How can I make AI models more trustworthy for business decision makers? What are the tradeoffs to consider when building more transparent and explainable AI models?

AI continues to represent an intimidating, jargon-laden concept for many non-technical stakeholders and decision makers. Gaining buy-in from all relevant parties may require ensuring a degree of trustworthiness and explainability embedded into the models. User experience plays a critical role in simplifying the management of AI model life cycles.

AI continues to be an intimidating, jargon-laden concept for many non-technical stakeholders. Gaining buy-in may require ensuring a degree of trustworthiness and explainability embedded into the models.

11. What resources already exist instead of starting from scratch? What's the best tool available for automating the modeling that we need to do?

Large cost savings can often be derived from finding existing resources that provide building blocks and test cases for AI projects. There are many open source AI platforms and vendor products that are built on these platforms. Evaluating those and selecting those might be cost efficient.

12. What kind of expenses related to infrastructure, data acquisition, crowd/SME annotation and others should my organization think about to implement AI best practices?

AI initiatives require might require medium-to-large budgets or not depending on the nature of the problem being tackled. Therefore, it needs to be properly scoped. AI strategy requires significant investments in data, cloud platforms, and AI platform for model life cycle management. Each initiative could vary greatly in cost depending on the scope, desired outcome, and complexity.

13. How will the AI function when it encounters a previously unseen situation or data point?

Over a long enough period of time, AI systems will encounter situations for which they have not been supplied training examples. It is prudent to anticipate and account for these eventualities. It may involve falling back on humans to guide AI or for humans to perform that function till AI can get enough data samples to learn from.

14. What governance policies and controls have I prepared for preventing and dealing with "AI breaches"?

AI may act or be manipulated in unintended and undesired ways. It is vital that proper precautions and protocols be put in place to prevent and respond to breaches. This includes incorporating proper robustness into the model development process via various techniques including Generative Adversarial Networks (GANs). GANs simulate adversarial samples and make the models more robust in the process during model building process itself.

15. How will I protect my models and data from adversarial attacks and malicious input?

Stakeholders with nefarious goals can strategically supply malicious input to AI models, compromising their output in potentially dangerous ways. It is critical to anticipate and simulate such attacks and keep a system robust against adversaries. As noted earlier, incorporating proper robustness into the model development process via various techniques including Generative Adversarial Networks (GANs) is critical to increasing the robustness of the AI models. GANs simulate adversarial samples and make the models more robust in the process during model building process itself.

Artificial Intelligence Resources

There are multiple data sources and experts available in the industry including the CompTIA AI Advisory Council.

Analyst reports and materials on artificial intelligence (AI) business case from sources like Gartner, Forrester, IDC, McKinsey, etc., could be a good source of information. Gartner and Forrester publish quadrant matrices ranking the leaders/followers in AI infusion in specific industries. Descriptions of those leaders/followers can give a sense of the strengths and weaknesses of the vendors. This helps in knowing what to look for from a business case perspective. Most vendors publish client case studies and success stories. Read them-with a pinch of salt-as they can be overselling, but still helpful.

CompTIA Resources
Practical Insights on AI
Emerging Business Opportunities in AI
Read more about Technology Solutions. CompTIA Use Case Library
Top AI Solutions
Accelerators and Barriers to AI Business Growth
About the CompTIA AI Advisory Council

CompTIA's AI Advisory Council brings together thought leaders and innovators to identify business opportunities and develop innovative content to accelerate adoption of artificial intelligence and machine learning technologies. The council is committed to building the strategies and resources necessary to help companies leverage AI to be more successful and collaborates with CompTIA's other industry advisory councils to further study the IT channel, blockchain, drones, business applications and internet of things.

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Business Considerations Before Implementing AI | Technology Solutions | CompTIA

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Considering an AI solution? Business decision-makers and AI practitioners should consider these questions before investing in artificial intelligence.

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The topic of artificial intelligence (AI) couldn't receive more hype. After decades of being an eccentric technology topic, it has suddenly jumped to the top of everybody's list, with AI-washing permeating every aspect of technology.

But organizations need to be wary: the risks of adopting AI are high and probably underestimated - both on the business and societal levels. And most organizations will have to proceed carefully. Setting aside the possible risk to all humanity, decisions about how AI is implemented have large financial and staffing implications that organizations will need to weigh carefully.

Beyond the larger ethical and existential questions about the threats of AI - technology leaders have warned of possible human extinction - there are more practical considerations such as the cost, the skills required, and the business risks. Let's dive in and consider the top five risks of AI to your business.

Risk #1: Accuracy and Accountability

This may be the largest current problem with AI. Many AI applications have serious errors with sourcing, accuracy, and accountability.

Where does the information come from? How do you verify it? What is the data set upon which the AI is based? Most AI-driven applications are non-transparent and non-verifiable. Using a term adopted by the intelligence community - "trust but verify" - most AI sourcing can't be verified. It's a black box.

In many cases, there are outright errors. Many of the mistakes made by chatbots have been documented. Here's just a few:

Google's Bard AI got off to a rocky start by making some well-known mistakes and "hallucinations," a phenomenon in which AI models make up facts. Some of the hallucinations it made in the field of astronomy led a leading astrophysicist, Grant Tremblay, to state that while Google's Bard is "impressive," AI chatbots like ChatGPT and Bard "have a tendency to confidently state incorrect information."

·Microsoft's Bing chatbot failed to differentiate key financial data in a basic comparison of vacuum cleaners and clothing. Humans have to clean this up.

OpenAI's ChatGPT hallucinations are common - such as the documented case in which it made up fictional court cases when a lawyer used it for legal research. Hallucinations strike again.

AI largely failed to help diagnose COVID-19 in order to aid clinical assessment of the pandemic. In an examination of 415 such AI tools, none of them was fit for clinical use.

Online real-estate data company Zillow incurred a $300 million write-down because its Offers program couldn't accurately price homes with an AI-driven algorithm.

These are just a few examples - there are plenty more. Some of them present risks to financial and physical health. The question organizations need to ask: Do you trust AI without the appropriate supervision and testing?

Risk #2: Skills Gap

As organizations consider adopting AI, they need to ask whether they even have the skills or capabilities to do so. Putting AI into the wrong hands could be weaponized destruction. As one Silicon Valley source at an IT integrator told me: "Our customers barely understand cloud, how are they going to understand AI?"

Good point. With the threat of financial mistakes, hallucinations, or errors that we have already demonstrated can cause serious harm, do most organizations have the AI expertise to use the technology? The answer is probably no.

McKinsey has identified hosts of risks of AI, including technical challenges such as data difficulties and issues with technology process. The key to all AI is data - and how this data is collected, stored, and used is critical. Without the proper understanding, organizations incur many risks, including reputational risks, data risks, and security risks.

Risk #3: Intellectual Property and Legal Risks

If a person or an organization does something wrong, they are held accountable and liable for mistakes via rule of law. What about AI?

There are a host of legal issues in the application of AI. Will the AI be treated like humans when it makes mistakes? Identifying the origins of an AI error or source of data is particularly difficult to trace (i.e. in the case of hallucinations). And then there are the huge intellectual property (IP) questions. If AI is using data models borrowing from IP such as software, art, and music - who owns the IP? AI disintermediates the owners of IP. If Google is used to search for something, typically it can return a link to the source or the originator of the IP - such is not the case with AI.

There's a host of other issues: data privacy, data bias, discrimination, security, and ethics. Deep fakes. Who owns the rights when somebody creates a deep fake of you?

Imagine a large organization in which AI tools are being adopted across a spectrum of employees and divisions in a "shadow IT" methodology. This is a legal and liability nightmare that has led dozens of companies to ban the adoption of AI tools such as ChatGPT - including big names such as Apple, JPMorgan Chase, Citigroup, Deutsche Bank, Wells Fargo, and Verizon.

MORE FROM FORBES Apple Joins A Growing List Of Companies Cracking Down On Use Of ChatGPT By Staffers-Here's Why

Risk #4: Costs

Every technology is ultimately assessed by its financial return on investment (ROI). Some technologies have been invented with unique promise but have ultimately failed because they cost more than they produced. Some examples I can think of are Google Glass, Betamax, the Segway, and fuel-cell technology - all of which either failed outright or did not live up to their expected market gains.

A specific example of where AI fails to deliver an ROI, or even incurs a loss, is Zillow's misguided (and arrogant) attempt to implement automated purchases of houses based on an AI-driven pricing algorithm, which ended up not working and costing the company hundreds of millions of dollars.

The scale of AI's implementation in terms of investment, training, and potential dwarfs perhaps any technology in the past. Accenture has already announced a $3 billion investment in AI, and the largest cloud providers are dumping tens of billions into new infrastructure for AI. More traditional businesses in the Fortune 500 will have to spend enormous amounts of money to train staff and invest in new AI technologies.

Will it pay off? This will come down to specific results in myriad applications, but there are going to be some train wrecks along the way. A study by the Boston Consulting Group and MIT Sloan indicated that only 11% of companies say they see a significant ROI on AI.

Risk #5: The End of Humanity

I've left the largest risk of AI for last. It could nuke us all, deciding that it's smarter and more powerful than us - and that humans aren't even needed.

There has always been a debate about the impact of the singularity, or the point at which AI takes on a life of its own, and what it will mean for humanity. There are, after all, times in which we don't seem that smart.

The less dramatic question about AI is about its soul. For example, does generative AI have a soul when it writes or creates music? Will humans embrace a soulless machine?

The questions get more deep when we ask about our safety, or what happens when AI does reach the singularity. What's the impact on global security when terrorists or hostile nation-states use AI to attack others? Elon Musk has warned the United Nations about autonomous weapons controlled by artificial intelligence.

The wide range of risks to humanity recently led 1,000 technology leaders and experts to sign a letter warning about a vast threat to human existence.

This question is probably too big for the average CIO or CEO, but it's certainly being taken up by global governments, with many already taking steps to restrict or outright ban AI.

This risk, as well as the rest of the risks we have listed, should lead you with only one conclusion: Your organizations may have to take a slow and steady approach to adopting AI - and you need a strategy to contemplate the risks right out of the gate. It may not make human extinct, but it could pose enormous risks to your business.

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The Top Five Real Risks Of AI to Your Business

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After decades of being an eccentric technology topic, it has suddenly jumped to the top of everybody's list.

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DLI | Webinar | AI Powered Customer Education: Strategies for Success

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Deep Dive into Understanding Data Monitoring

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Deep Dive into Understanding Data Monitoring

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A Comprehensive Overview Of Data Quality Monitoring

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A detailed overview of why data quality monitoring is crucial for building successful data and machine learning systems and how to approach it.

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Factors to Consider When Looking at How AI Makes Decisions

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Factors to Consider When Looking at How AI Makes Decisions

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Here, we discuss how AI systems arrive at a decision. It involves more than data, maths and 'black box' models. In fact, all kinds of value judgments will be made during the development and deployment of AI systems.

It is not always very straightforward how to define goals, mutually exclusive categories, and relevant attributes. But algorithms do need this. Moreover, AI systems also require energy, and have a physical dimension, and involve human labour, and politics. We challenge you to think about what kind of choices are made and by whom.

Before an AI system will make any decision, many decisions have already been made. Explicitly or implicitly; What is the system intended to do? Who gets to decide what acceptable error rates are? Whose values are informing the system? Who does it affect? How can one check whether the system is accurate and fair? How can users dispute the decision? How was the system designed to be used? What if we change our minds about the specifications and values underlying the system?

Users of AI-systems may have a tendency to 'trust' AI-systems for making "rational" or "objective" decisions. And it can be very difficult to scrutinise a decision made by AI, because of its complexity. Moreover, once you are used to the convenience and accuracy of the outcome - its decision or recommendations - you might become less alert to spotting errors or less keen to review the results critically.

However, we still expect that someone can account for AI decisions - that we can go somewhere to get an explanation about why the system arrived at a certain decision. Accountability is a major theme in AI ethics.

Read more

Roos de Jong contributed to a piece of the Rathenau Instituut explaining how constitutional issues arise because AI systems sometimes operate in non-transparent ways and risk violating human rights. → Grip op algoritmische besluitvorming bij de overheid (Dutch only).

Another Rathenau-reports might be of interest as well in the context of this course. In Urgent Upgrade the Rathenau Instituut demonstrates that digitalisation (including AI) can lead to a wide range of social and ethical issues. Next to privacy and security, issues such as control of technology, justice, human dignity, and unequal power relationships are also pivotal.

Kate Crawfort explains how AI-systems both reflect and produce social relations and understandings of the world in her book "The Atlas of AI". → Crawford, K. (2021). The Atlas of AI. Yale University Press.

Maranke Wieringa gives a systematic review of the work that has been done in the field of 'algorithmic accountability' → Wieringa, M. (2020). "What to account for when accounting for algorithms. A systematic literature review on algorithmic accountability." In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency: 1-18.

Salon Barocas and Andrew D. Selbst address sources of unintentional discrimination. → Barocas, S. en A. Selbst (2016). "Big Data's Disparate Impact." Californian Law Review 104, 671.

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It involves more than data, maths and 'black box' models. In fact, all kinds of value judgments will be made during the development.

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Deep Dive into the Challenges of Using AI for Management

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Deep Dive into the Challenges of Using AI for Management

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In recent years, the world has witnessed a remarkable rise in Artificial Intelligence (AI) and its n. As AI continues to evolve, its integration into visual arts is opening new doors for designers, streamlining workflows, and enhancing creativity. But the question remains: Can AI serve as a powerful ally rather than a replacement for designers?

In this blog, we will explore how AI is poised to shape the future of graphic design and how it will impact graphic designers.

The potential of AI in graphic design

1. Automated content generation

One of the most promising ways AI will impact graphic design is through automated content generation. AI-powered tools have the ability to analyze vast amounts of data, identify patterns, and understand design principles. By setting specific parameters, you can instruct AI algorithms to create layouts, compositions, and even entire designs. Generative AI tools like Stable Diffusion, DALL-E 2, Midjourney, Adobe Firefly, and such can significantly speed up the design process, freeing up creators to focus on more innovative aspects.

AI-generated image

Instead of spending hours on repetitive tasks, designers can rely on AI to generate multiple design options as a first draft, which they can then refine and customize according to their vision and the client's requirements.

Artwork Flow's creative automation tool lets you enhance and scale your creatives in a shorter time frame while staying aligned with the brand guidelines. Moreover, AI's ability to generate designs based on user preferences and data-driven insights can lead to more personalized and engaging visual experiences.

2. Enhanced creativity

AI algorithms can serve as valuable collaborators, providing designers with suggestions and ideas they might not have considered. By analyzing an extensive library of design elements, styles, and trends, AI can offer valuable insights and inspiration, helping designers explore new concepts and push the boundaries of their creativity.

For instance, a designer working on a logo design can use AI-powered tools to generate different versions. AI's input can serve as a valuable source of inspiration to bring new ideas to the table.

3. Personalization

Personalization plays a vital role in graphic design. Here, AI can analyze user behavior, preferences, and demographics to create designs tailored specifically to individual users or target audiences.

Imagine a website interface that dynamically adjusts its layout and color scheme based on each visitor's preferences. AI algorithms can analyze user interactions and adapt the design in real-time, creating a more immersive and user-centric experience.

Also read: The Importance of User Experience (UX) Design in Graphic Design

A creative intelligent software can help you in deriving such data by analyzing how each color, font, and other brand elements work out with the audience. To be precise, AI tells you how to improve your designs to increase conversions. By offering solutions that resonate with users on a personal level, your company can forge deeper connections with your audience, leading to increased engagement and brand loyalty.

4. Improved image editing

Image editing is an integral part of graphic design, and AI is proving to be a game-changer in this aspect. AI-powered image editing tools like Canva and MS Designer can automatically retouch and enhance images, eliminating the need for manual adjustments and saving designers significant time and effort.

Moreover, AI can help designers work with low-resolution images by using upscaling techniques, maintaining visual integrity even when enlarging images. This advancement is particularly valuable when working on projects that involve print media or large-scale graphics.

Image source

5. Smart layouts

As the digital landscape continues to thrive, designers face the challenge of creating designs that seamlessly adapt to various screen sizes and formats. AI can come to the rescue by optimizing layouts and ensuring that designs look visually appealing across different devices and platforms.

6. Brand design and analysis

A brand's visual identity plays a crucial role in shaping its perception among consumers. AI can help design brand logos, colors, and visual elements that resonate with the target audience. By analyzing user preferences and market trends, it can recommend design choices that align with the brand's personality and values.

Furthermore, it can examine the performance of various brand elements over time, providing valuable feedback to designers and marketers. This data-driven approach allows for continuous improvement of a brand's visual identity, ensuring it remains relevant and impactful.

7. Real-time collaboration

With AI facilitating real-time collaboration, you can work together seamlessly, irrespective of your physical location. AI-powered collaboration tools can simplify communication, streamline workflows, and enable efficient teamwork. This enhanced collaboration boosts creativity, and efficiency, and ensures that the ultimate design reflects the combined effort of the team.

8. Accessibility and inclusivity

AI can support designers in making their creations more accessible to individuals with disabilities. For instance, AI algorithms can analyze color palettes and suggest combinations with sufficient contrast for better readability. Additionally, AI can generate alternative text for images, making the content accessible to visually impaired users who rely on screen readers.

AI in graphic design: An epic win or fail?

The impact of AI on graphic designers can be seen as a double-edged sword, with both positive and negative aspects. Whether AI is useful or flawed depends on how it is used and integrated into the design process. Let's explore the arguments on both sides:

Benefits

1. Increased efficiency: AI automates repetitive tasks, such as image editing, layout optimization, and content generation, allowing you to complete projects faster and more efficiently.

2. Enhanced creativity: It provides you with inspiration and new design ideas, sparking creativity, and pushing the boundaries of your work.

3. Personalization: AI analyzes user data and preferences, enabling designers to create personalized designs that resonate with specific target audiences, leading to higher engagement and customer satisfaction. After all, getting the attention of the audience is a very important thing!

4. Streamlined workflows: AI-powered tools streamline the design process by assisting with tasks like prototyping, data visualization, and brand analysis, making the overall workflow smoother and more organized.

5. Consistency and quality: AI ensures consistency in design elements and maintains a high level of quality across various projects, resulting in a professional and polished output.

6. Time and cost savings: By automating certain design tasks, you can save time and reduce costs, making work more efficient and cost-effective.

Drawbacks

1. Job displacement: One of the primary concerns of AI integration is the fear of job displacement. As AI automates certain design tasks, there may be concerns about job opportunities for designers, particularly for those involved in more repetitive and standardized design work such as creating templates for ad creatives or setting the same blueprint for every product design. Otherwise, AI cannot find a way to replace the designers.

2. Loss of creativity and originality: Relying too heavily on AI-generated suggestions may lead to lesser clarity and originality in designs. Overly depending on AI-generated elements may result in saturated and repetitive designs that lack a uniquely human touch.

3. Ethical concerns: AI-generated content can raise ethical concerns, particularly in cases of deepfake technology or AI-generated misinformation. Designers need to be cautious and responsible while using AI tools to secure the integrity of their work.

AI presents both benefits and challenges for graphic designers. While AI can significantly enhance efficiency, creativity, and user-centric design, it also has pitfalls. To make use of the full potential of AI in graphic design, you must strike a balance between leveraging AI tools for efficiency and maintaining your creative prowess and unique human perspective.

Designers can do what AI can't

1. Originality

Designers are inherently creative beings who can imagine and conceptualize original ideas. They can think outside the box, infuse emotion into their designs, and create unique visual experiences that evoke feelings and connections with users. AI lacks the human capacity for imagination, relying on existing data and patterns which sometimes could mislead the viewers.

2. Empathy and emotional intelligence

Designers can empathize with users and understand their needs, preferences, and pain points. This allows designers to get a step ahead and create designs that are not only aesthetically pleasing but also meaningful and user-centric. AI lacks emotional intelligence and cannot truly comprehend the emotional aspect of design.

3. Critical thinking and problem-solving

Designers possess critical thinking skills that enable them to analyze complex design challenges and find innovative solutions. They can adapt their approach based on context and feedback, making decisions that align with project goals. AI follows predefined algorithms and lacks the contextual understanding that human designers possess.

4. Design ethics

Designers are conscious of ethical considerations in their work, ensuring that their designs respect user privacy, cultural sensitivities, and social impact. They actively consider the consequences of their designs and strive for responsible innovation. AI, being algorithmic and data-driven, may lack it.

In all, designers possess a unique set of skills, emotions, and ethical considerations that set them apart from AI. While AI can significantly enrich the design process, it cannot replicate the depth of human creativity, empathy, critical thinking, and intuition that designers bring to their work.

A designer's note

Here's what Devika Bhansali, design manager at Bizongo Desworks thinks about the integration of AI and graphic design.

"AI's impact on graphic design is speeding up the creative process and introducing new ways of thinking. It is changing how we work by helping us create content much faster. It also helps from the initial stages of ideation and turning those ideas into finished designs.

Even basic tools like Adobe Photoshop and Illustrator now have AI algorithms that can automatically remove backgrounds from images and generate new visual designs based on given instructions. This is incredibly useful for projects that involve complex image editing and composition.

AI also helps to suggest color variations based on the designer's input. This not only makes the design process faster but also provides clients with a range of choices. To add to that, we also use ChatGPT to generate storytelling ideas to set the creative process.

Overall, AI helps us designers add a fresh perspective, speed up certain processes, and tap into wider areas, but human creativity is what adds a special touch to our work!"

Conclusion

As AI continues to advance, its integration into graphic design offers exciting opportunities for the future. Rather than replacing designers, AI serves as a valuable ally, streamlining workflows, enhancing creativity, and opening up new avenues for personalized and innovative design solutions.

By embracing AI as a tool to augment your design skills, you can take your craft to the next level. As technology and creativity intertwine, the future of graphic design will be a harmonious collaboration between human originality and artificial intelligence.

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How AI Will Impact Graphic Design in the Future

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Explore the impact of AI in the graphic design field. Learn how artificial intelligence will revolutionize creativity and efficiency, opening the door to bigger opportunities.

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AI decision making serves as the future of intelligence in businesses. For several decades, the backbone of modern business has been data. It has helped businessmen and entrepreneurs shape their decisions.

Besides, it allowed them to learn in-depth about their customers. However, with all the data comes a price; the human brain cannot process it all. That is where artificial intelligence (AI) and its decision making in business intelligence come in.

AI decision making allows businesses or companies to make faster, accurate, and consistent decisions by capitalizing on datasets with AI. Artificial intelligence can analyze large datasets without error. This helps business teams to focus better on work relevant to their field.

Decision makers of companies believe in AI available in terms of machine learning, natural language processing, and computer vision. They are the most trusted aspects of any business ground today to increase profits and reach the set goals. Make business decisions more effective with AI. Learn when and how to complement human decision-making powered by data and analysis and artificial intelligence.

What is artificial intelligence?

Artificial Intelligence or AI refers to a machine's ability to replicate human learning and its cognitive functions. AI aims at prompt problem-solving and decision-making processes without human errors. In technological terms, they refer those machines to as intelligent bots.

With the advancements in technology, the world of AI has broadened over the years. Today, AI can be experienced in three recognized levels of the AI spectrum.

Assisted intelligence: It focuses on automating basic tasks. For instance, machines in the assembly lines.

Augmented intelligence: With augmented intelligence comes symmetry. It is a two-way process. The aim is for machines to learn from human input. Humans in turn base their decision accuracy on intelligent information.

Automation intelligence: Simply put, it includes machines automating the complete process, with humans out of the loop. For instance, autonomous robots and self-driving cars.

What is AI decision making?

A popular question is what is decision making in AI? AI decision-making is of utmost importance when data processing is carried out completely or in part by an AI platform. With no human in the picture, this process helps to quantify data, making accurate predictions and precise decisions.

AI can handle anomaly detection, data crunching, complex analysis, and spotting trends. The final decisions are then either completely automated or taken over by the human end.

Take a look at the model of AI decision making:

Degrees of decision making

Decision support - With the help of predictive, diagnostic, or descriptive analytics, humans can decide with accuracy. The advantage lies in combining human intelligence with data-driven insights. Common sense and expertise when combined may make the best out of AI in businesses.

Decision augmentation - It makes use of predictive or prescriptive analytics to recommend multiple decision alternatives. The synergy of AI capability and human knowledge leads to rapid analysis of large data volumes, reducing complexity.

Decision automation - Like decision augmentation, decision automation relies on prescriptive or predictive analytics. Humans benefit from its scalability, speed, and consistency in decision-making.

When take advantage of AI in decision making?

Whether a decision has to be AI automated, augmented, or supported depends on two factors. First is the time limit within which the organization will need to decide. The second factor is its complexity.

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The dimension of time shows the span between when an organization detects an opportunity or threat and when it acts on it. The period varies for the work at hand. For instance, high-frequency stock trading may just take a few microseconds. Pay decisions may take a few weeks to make relevant decisions. It may even stretch up to a few months or years for strategic acquisition or merger.

Based on the Cynefin framework, the complexity may vary on a continuum, as stated below:

Simple situations are usually predictable and simple. They operate on a simple cause-and-effect relationship. For instance, processing payrolls.

Complicated circumstances require more expertise in problem-solving or analysis to determine the cause and effect. For instance, marketing campaigns and asset management.

Complex situations encompass multiple relationships and interdependence. An effective analysis may therefore require a holistic or systemic approach. Besides, simulations are necessary to understand how decision making in business intelligence affects widespread elements. For example, supply chain distributions.

Chaotic situations are caused by unclear causes and effects or dynamic interdependencies. Even the smallest changes may cause unreasonable affects. For instance, natural disasters or stock market crashes. Therefore, taking accurate decisions requires more practical learning and experimentation.

How AI may change decision making?

Artificial intelligence is the epitome of businesses because of its learning capability. Higher data-driven decisions are equivalent to better learning. AI is capable of training itself to build models of data collections. These models may make accurate decisions and categorization over provided data. Likewise, the models work on data in real-time making predictions, categorizations, and recommendations. This in turn helps to make better commercial decisions.

Well-known companies like Amazon make use of customer transaction data. Using this approach, companies can learn better about the segment of customers buying certain products together. This model helps to recommend complementary products on the websites as well. It allows websites to provide better customer recommendations and increase purchases.

Until now, the central point of decision-making has been humans. Human analyzed data used to decide which customers to target, or what cost would a product launch have to bear. Besides, marketing campaigns were too risky in this scenario.

Decoding the impact: Key statistics on harnessing AI for decision making

Based on the McKinsey survey, the adoption of artificial intelligence (AI) has seen remarkable growth in recent years. Since 2019, the number of organizations embracing AI has more than doubled. However, the proportion of companies utilizing AI has plateaued between 50 and 60 percent for the past few years. Despite this, a set of companies that have effectively harnessed AI are experiencing higher financial returns, establishing a significant lead over their competitors.

Looking at the numbers, in 2017, only 20 percent of respondents reported adopting AI in at least one business area. Today, in 2023, that figure has risen to 50 percent. Alongside the increased adoption, the average number of AI capabilities used by organizations has also doubled. In 2018, companies utilized an average of 1.9 AI capabilities, which has now risen to 3.8 in 2022. Robotic process automation and computer vision have consistently remained the most widely deployed purpose of AI, with natural-language text understanding advancing to a prominent position just behind computer vision. The numbers make it clear which AI system will continue to analyze a problem.

When it comes to the top use cases for AI, the rankings of AI for decision-makers have remained relatively stable over the years. Optimization of service operations has consistently held the top spot, followed by use cases such as the creation of new AI-based products, customer service analytics, customer segmentation, and customer acquisition. These use cases highlight the diverse ways in which AI is leveraged to drive operational efficiency and improve customer experiences.

Furthermore, the level of investment also shows the prominence of artificial intelligence decision-making. According to the survey, the level of investment in AI has increased alongside its growing adoption. In the past, only 40 percent of organizations using AI allocated more than 5 percent of their digital budgets to AI. Now, over half of the respondents report this level of investment. Looking ahead, 63 percent of respondents anticipate increased investment in AI over the next three years, reflecting the growing recognition of AI's potential.

The areas where companies see value from AI have also evolved. While manufacturing and risk were prominent in 2018, marketing and sales, product and service development, and strategy and corporate finance are now the functions reporting the biggest revenue effects. Supply chain management stands out as the area where companies have realized the highest cost benefits from AI.

Despite the progress in AI adoption and investment, there have been no substantial increases in reported mitigation of AI-related risks since 2020, which is another important factor when it comes to artificial intelligence for decision-making. Organizations still need to address concerns and challenges associated with AI implementation to ensure its responsible and ethical use.

In summary, Artificial intelligence in decision-making has witnessed significant growth, with organizations increasingly leveraging AI capabilities to drive operational efficiency and generate financial returns. Investment in AI has also risen, and the areas where companies see value from AI have expanded. However, mitigating AI-related risks remains an ongoing challenge that organizations need to address to fully capitalize on the benefits offered by this transformative

technology.
Benefits: artificial intelligence & decision making

Artificial intelligence is extremely important for the future. It forms the foundation of computer learning. Through artificial intelligence, computers can harness massive amounts of data. Besides, the learned intelligence may make optimal decisions in a few fractions of time, unlike humans. Today, AI is becoming responsible for everything; be it medical breakthroughs on cancer research or innovative research on climatic change.

Let's get an overview of the advantages of AI before moving on with its significance in different fields:

Enhances the decision-making process in businesses: It allows businesses to take immediate decisions by a quick analysis of large datasets.

Boosts sales and marketing campaigns: Decision-making applications in artificial intelligence like Natural Learning Process allows businesses to understand how customers interact with different brands, tone, and words that are most appealing to them.

Better understand target customers: AI tools like algorithms with machine learning and chatbots offer a better understanding of customer satisfaction and expectations.

Improves decisions of companies dealing with complex data: AI uniquely positions itself to make sense out of large data quantities. It is especially true when the outcome is clearly defined and measurable.

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Develop custom AI solution to impact decision making profoundly.

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How does AI help in decision making?

Read further to learn how artificial intelligence helps in decision making.

AI and Business Decision

Before the revival of AI and its commercial application, executives used to rely on inconsistent and incomplete data. With AI came data-based models and simulations to look up to. Today, the AI system begins from scratch, feeding on a regular big data business diet.

Businesses put augmented intelligence into action. Eventually, it offers executives, revolutionary models, as the basis of their decision-making process. Several AI applications enhance this decision-making capability. Listed below are some of them. AI makes use of automated cognitive as well as physical tasks. It allows humans to make faster and more accurate decisions. Simply put, it automates decision-making with some human intervention.

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AI enhances automation and reduces human-intensive labor and tedious tasks. For instance, smart weather forecasting. Today, AI-powered tasks are known to bridge the gap between climate and data scientists.

With the help of AI, companies are better equipped to fight disasters using AI decision-making algorithms. On the bright side, the world has witnessed the groundbreaking impact of AI on humans and the economy. AI is now working as a capital hybrid, aiding the growth of the economy and humans.

Research was set by the McKinsey Global Institute. It suggested the delivery of 13 trillion dollars of additional output by 2030. This is estimated to boost the global GDP by 1.2 percent per year. This, alongside other researchers, believe AI to have a revolutionary impact on the man-made decision-making process.

Marketing decisions

Companies often face several complexities when making certain marketing decisions. Precisely, customer-driven market complexities included in decision-making are increasing every day. The customer needs and desires must first be understood. Eventually, the products must be aligned with the relevant needs and desires.

Grasping the dynamic consumer behavior is also crucial for long-term and short-term marketing decisions. Appropriate AI modeling and simulation methods may help people to gain better insights into the buyers' viewpoints.

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Artificial intelligence techniques for rational decision-making, like decision support systems, may help predict customer behavior better. Using this system allows AI systems to support real-time decisions and updated gathering, forecasting, and analysis of market trends.

Customer relationship management

AI has improvised and added more flexibility to customer relationship management. It has automated several functions including, data recording, contact management, data analysis, and lead ranking.

Besides, the buyer's persona model by AI can predict a customer's lifetime value. These features help marketing teams to work more fluently and efficiently.

This AI system was first put to use on music content sites. Ever since the recommendation system has extended to different industries. It works by first learning about a user's content preferences.

Subsequently, it pushes the content forward that fits the preferences. This in turn, reduces the bounce rate. In addition, the information learned by the AI system may better target relevant content.

Source: Netflix

Automation efficiency

AI-generated automation efficiency has gone past assembly lines in business processes. Business functions, such as marketing and distribution, involve automated efficiency. AI has hastened processes and provided decision makers with reliable insight.

In marketing, humans may get reliable insights on customers. This enhances customer interaction. Automated market segmentation and campaign management have enabled effective decision-making.

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One of the main features of a good CRM application is distribution automation. This clubbed with AI has been of key importance for several retailers. With the support of AI, retailers may monitor and control the market. Besides, they may predict and respond to demand products more accurately.

Social computing - AI can simulate, predicte and analyze human behavior. It allows marketing professionals to understand the social behavior and dynamics of the target market. These applications may data-mine and understand social media networks.

Opinion mining - It is an AI-assisted data mining that focuses on finding and understanding opinions and feelings through a web search. This allows marketers to better understand how the targeted audience views a product.

Source: Unsplash

Manual help and manual mining are pretty time-consuming. AI functions to reduce reliable analysis and search functions. This method is highly used in search engines that rank customer interest on specified websites, products, or web pages.

In addition, hyperlink-based AI is also employed. The intelligent bots seek out clustered linked pages and are perceived as groups sharing common interests.

Future impact of AI

Technically, there is no major industry other than modern AI. Today, narrow AI is known to perform objective functions through data-trained models. It either falls into the category of machine learning or deep learning.

In the last few years, robust IoT connectivity, quicker computer processing, and the proliferation of connected devices have ramped up data collection and analysis. Some sectors are exploring AI whereas others are veteran travelers in this field. There is a long way to go.

The impact of artificial intelligence continues to make its way through our daily lives. Let's look at some of their impacts.

When it comes to automating transportation, self-driving cars top the chart. In a decade or two, autonomous cars will take over transport optimization.

AI-fueled robots work along the human workforce to perform a limited range of tasks. For instance, stacking and assembling. Besides, predictive analysis sensors help to run the equipment more efficiently and prevent failures.

In the era of the AI-incipient field of healthcare, diseases are diagnosed more accurately and quickly. Besides, it has sped up and streamlined the process of drug discovery. Virtual nursing assistants are taking over round-the-clock patient monitoring. Not to mention, big data development and consulting have given rise to more personalized patient assistance.

Source: Unsplash

AI research has revolutionized learning with digitized textbooks and teachings. Human instructors are assisting early-stage virtual tutors. Besides, facial analysis gauges emotions felt by students. This helps to determine and tailor-make experiences to suit individual needs.

Today, journalism is also harnessing AI and is continuing to benefit from it. Decision making applications in artificial intelligence like the natural learning process are used to make quick sense of automated insights and financial reports.

Source: Unsplash

AI assistants and chatbots are better at understanding nuance and context. For instance, it helps to quickly make human-like call appointments with ease.

Can artificial intelligence replace executive decision making?

Correctly collected and analyzed data is capable of providing unparalleled insights into every business. Modern companies, however, face one issue - they drown in overflowing data. The human force cannot keep up with the collected data.

It's not a surprise that databases and spreadsheets don't cut anymore. Now, the question is whether humans will be in charge or will machine take over data-driven decision-making.

Can AI make better decisions than humans? Can AI make moral decisions? This is what Harvard Business Review had to say in this matter.

Humans aren't capable of leveraging all the data.

Human force accumulates every aspect of a dataset. For instance, relationships, insights, and patterns. They manage large data amounts by summarizing them. It is humanly impossible to process millions or billions of records. Human minds cannot link relations among data elements, which are considered as the key to any decision making.

Humans allow biases to take control over them.

Humans prioritize important data rather than what it states. Contrary to AI, humans consider data as linear and hence cannot process them in aggregates. Besides, AI can collate data without letting emotion cloud their judgments and make errors. However, it is not a smart business move to leave all the work to machines.

In reality, human decision-making has a major influence on making decisions. Be it a great marketing copy, an innovative marketing strategy or a dedicated customer support decision. A decision-making model powered by human intelligence and AI together can make the cut.

Conclusion

AI decision-making is revolutionizing business intelligence by analyzing vast datasets to improve decision-making processes. Machine learning, natural language processing, and computer vision are key components of AI that aid in faster and more accurate decision-making.

From automating basic tasks to completely autonomous decision-making, AI offers various levels of assistance depending on the complexity of the situation. Organizations are leveraging AI capabilities in areas like marketing, sales, product development, and strategy to enhance operational efficiency and generate financial returns. However, addressing the risks associated with AI implementation remains a challenge that businesses must overcome to fully capitalize on its benefits.

Author Bio

Olivia is a content strategist at Outreacheo. She has been working in social media and content marketing for five years. She specializes in the health, tech, innovation, and travel sectors. When she is not writing, you will find her teaching math, and trying new recipes.

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Artificial Intelligence in Decision Making - Big Overview

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Learn about artificial intelligence in decision making, what technologies stand behind it, about the future impact of AI on healthcare, education, manufacturing, media, customer assistance, and transportation, and more.

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What Is AI Automation? Explore Applied Uses and Productivity

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What Is AI Automation? Explore Applied Uses and Productivity

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Have you leveraged on intelligent automation for your Marketing and Sales processes?

According to a survey done by Semrush, 71% of marketers find IA could be useful for personalization. When it comes to Marketing and Sales, it is important that businesses craft strategies to have better engagements with their customers and prospects. With the large amount of data that needs to be processed, employees may benefit with the help of IA through tasks automation.

Read more: 5 Signs Why Your Business Need a Digital Worker

How can Intelligent Automation helps processes in Marketing and Sales, and bring more productivity and efficiency?

1. Collect data faster and easier with web scraping

In Sales and Marketing, data holds a very important role. It can be data of your competitors, which needs update frequently for you to stay competitive. It can also be data of what is trending in the market now, which also needs frequent management and update for you to stay in the business. As the customers has dynamic needs, it is important to always refresh your data. Data helps you to build effective campaigns and marketing/sales strategy, thus you can better aim your customers by launching the product or service that fills their need. You can also leverage on data to evaluate your product or service.

But, how do you manage data? How do you collect data, that sometimes may vary and reside in different sources such as documents, systems, social medias, platforms?

Intelligent automation can help you to collect data faster and easier, with web scraping.

What is web scraping?

Web scraping is the automation of structured web data extraction. You can automatically collect publicly available at a large scale that resides in website, social medias, or e-commerce platforms. By this, the cognitive bots can collect data and store it on your end for you to use or analyze it accordingly. This method will help marketing and sales department to have automated data collection. Instead of spending so much time on collecting and gathering data, marketing and sales department can save time to build better strategies leveraging on that data to aim at their customers better.

Read more about how to automate data collection faster with web scraping

Take a peek on how cognitive bots do web scraping:

2. Better understanding of customer satisfaction with sentiment analysis

It is important to measure and keep tracks of customer satisfaction (and their feedbacks) towards your product or services. Having a thorough knowledge of how customer feels about your product or service can help you to evaluate and do improvement. Here again, data plays a very crucial role - collecting and analyzing data is very important to do. This sentiment analysis is also helpful for you if your marketing department wants to do research on the trend in the market, or if they want to launch a product - but before they want to know how the public sentiment is towards similar product.

With the AI features in Intelligent automation, it is possible to do text analytics. This capability is possible through A.I. Natural Language Processing (NLP). NLP allows cognitive bots to intelligently assess, analyze, and identify text (text analytics) e.g. sentiment analysis, comparing text (fuzzy compares), classifying text, or interpreting the text. With A.I NLP, cognitive bots can also understand and interpret human-natural language (even in other languages than English!)

In this automated process of understanding customer, marketing department can have a better confidence in releasing products or services that aims the heart and loyalty of their target customers.

Take a peek on how to automatically gain customer feedback and analyze their sentiments:

3. Find customer insights with AI-driven data analytics for better engagement

Again, it is data and data.

As intelligent automation helps to collect data simultaneously with accuracy guaranteed, enterprise can optimize data to generate customer-driven insights. Using A.I. Machine Learning, cognitive bots can identify the trend and pattern of data that is fed to it. This helps the enterprise to have the data analyzed and optimized to generate insights that the enterprise would like to explore. Thus, this will unlock the key to a better understanding of the market trend and what the customer needs.

With the ease of data collection and analytics, marketing and sales department can have understand better what their customers need, especially the newest trend alive in the market. Using the right automation tools, enterprise can engage their customers better among the saturated business environment. Data-driven insights are the key to greater innovation. This will help the business to tackle the conundrum of what is the game changer. This will improve the competitive values of the business itself.

Take a peek on the example of how to do predictive analysis with Machine Learning:

4. Messaging at scale and personalized chats to customers

Imagine you can reach out to hundreds (or even thousands) of your customers in less than an hour! Say you are about to release new promotions or discounts of your products and you want to make sure that this message gets to your customers effectively. Reaching them through email via newsletter subscription is one thing, but who checks their email inboxes that often? They might check their inbox and receive your message when your promotion period is over. The challenge is, how to reach them better in time - more effectively?

With the help of this cognitive bots, it is possible! You can customize your message, let it be text, videos, images or GIFs! You can target based on groups with different messages or contents. Cognitive bots just have to send it! It's so reliable that it can actually send different messages to different targets accordingly, just like how you want it. Cognitive bots can get the customers phone number from your system, automatically add them, and send messages. You can also send in different languages!

Take a peek on how cognitive bots send customized message:

5. Automate data entry and consolidation

Have a seamless and streamlined data entry and consolidation with intelligent automation. If you are digitizing data manually, update and enter data between systems manually - you need to stop and find a solution to help you.

Intelligent automation helps to improve your productivity by automating your data management. Cognitive robots can help to read and extract data from documents, this means you don't have to manually digitize them. The robot can also retrieve data you need and pull them from any sources - let it be systems, platforms, or paper documents, and consolidate it in one master spreadsheet.

See how intelligent automation collects data from paper documents.

Updating CRM and ERP systems can also be a mundane and time-consuming. Imagine having cognitive bots to do this for you, allowing you to do more strategic tasks that have more value for your business.

See how intelligent automation helps to consolidate data.

Take a peek how cognitive bots help to enter, update, and integrate data between systems:

Marketing and sales department must have numerous type of reports to generate and process each day. This process involves retrieving data from various sources, consolidating and compiling them, and sending this report to a managerial level for a review. Also, this process can be recurring weekly, monthly, and yearly.

But how if you can automatically generate cross-functional reports in seconds? Streamlined report generation of your advertisement performance? Metrics report? Analytical report of customer engagement via social media performance?

Generating reports is one of the most recurring tasks any department has, including and especially marketing and sales department with a lot of variables to measure periodically. Recurring tasks, that mostly include processes that deal with data, are burdening employees to spend - on average - 19 workdays every year on this type of task.

Find out about how much time are you wasting on recurring tasks

Take a peek how you can automate reports generation with intelligent automation:

Benefits of Intelligent Automation for Marketing and Sales

Helping employees for better productivity and business efficiency

A lot of the times employees are met with repetitive and high volume tasks in Marketing and Sales, there might be a variety of paper-work and data to process. Intelligent automation can help employees decrease their work load by automating these tasks. Not only this will save a significant amount of time, employees can also use the saved time to up-skill and complete higher value added work.

Intelligent automation can help auto a wide variety of work processes, including payment processing, directing customers' enquiries and many more. With digital transformation being essential for businesses to thrive in the market, IA will be able to help businesses keep their competitive advantage.

Increase Revenue

With an increase in work efficiency and time saved, this will lead business in securing more revenue. Incorporating intelligent automation is you business strategy can help automate repetitive work processes with reduced errors, decreased processing time and increased ROI. Artificial Intelligence technologies can help businesses target their customers' needs without wasting resources on demographics that will not be interested in the products or services.

Instant Customer Insights

Intelligent automation paves more efficient and effective way to gather and use data. This will unlock the key for Marketing department to have a better understanding of the market trends and what the customer needs.

Intelligent Automation transforms Marketing and Sales

In conclusion, the implementation of intelligent automation in Marketing and Sales is transforming how customers and business interact.

Without the technology a few years back, these functions were once considered as impossible. Nowadays, businesses is able to hyper customize their interaction for each customer and prospect.

However, with the powerful AI algorithm continue to advance, we have not unlock the full potential. AI will only be more powerful and helpful for business in the future of digital transformation.

Read more: All You Need to Know about Artificial Intelligence - Why and How AI is Transforming the World?

Written by: Kezia Nadira & Reiko Anjani

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6 Ways Intelligent Automation Can Transform Marketing and Sales

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Intelligent Automation unlocks the key for Marketing and Sales to better understand market trends and what the customer needs.

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How automation, AI and machine learning can improve performance