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Four steps to an AI strategy for your business

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Creating a strategy for artificial intelligence is top of mind for many executives. As competitive pressures heat up in virtually every industry, leaders are looking to AI to help accelerate their business growth.

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The adoption of artificial intelligence (AI) is rapidly taking hold across global business, according to a new McKinsey Global Survey on the topic. AI, typically defined as the ability of a machine to perform cognitive functions associated with human minds (such as perceiving, reasoning, learning, and problem solving), includes a range of capabilities that enable AI to solve business problems. The survey asked about nine in particular, and nearly half of respondents say their organizations have embedded at least one into their standard business processes, while another 30 percent report piloting the use of AI. Yet overall, the business world is just beginning to harness these technologies and their benefits. Most respondents whose companies have deployed AI in a specific function report achieving moderate or significant value from that use, but only 21 percent of respondents report embedding AI into multiple business units or functions. Indeed, many organizations still lack the foundational practices to create value from AI at scale-for example, mapping where their AI opportunities lie and having clear strategies for sourcing the data that AI requires.

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One critical factor of using AI effectively, the results confirm, is an organization's progress on transforming the core parts of its business through digitization. At the most digitized firms, respondents report higher rates of AI usage in more business functions than their peers, along with greater investment in AI and greater overall value from using AI. Another foundational challenge with AI is finding skilled people to implement it effectively. Many respondents say their organizations are addressing the issue by taking a diversified approach to sourcing talent. On the whole, despite reasonable concerns about AI being used to automate existing work, respondents tend to believe that AI will have only a minor effect on overall company head count in the coming years.

Adopting, deploying, and applying AI

How adoption of AI is progressing

The results suggest that most organizations have already begun to adopt AI in their businesses. Forty-seven percent of respondents say their companies have embedded at least one AI capability in their business processes-compared with 20 percent of respondents in a 2017 study who said their companies were using AI in a core part of their business or at scale -and another 30 percent say they are piloting AI. Still, there remains a lot more potential to use AI across the enterprise; as our previous research has shown, AI opportunities exist in every sector and business function. Just 21 percent of respondents say their organizations have embedded AI in several parts of the business, and so far, investments in AI are a relatively small fraction of companies' overall spending on digital technologies. A majority of respondents (58 percent) say less than one-tenth of their companies' digital budgets goes toward AI-though respondents overwhelmingly expect AI investments will increase in the coming years (71 percent say so).

Which AI capabilities have been deployed

Of the nine capabilities we asked about, robotic process automation, computer vision, and machine learning are most commonly deployed. For each of these, at least 20 percent of respondents say their companies have already embedded these technologies into their business processes. Physical robotics and autonomous vehicles are the least commonly deployed, largely because they are relevant only to companies in industries where there's a clear application; in those sectors, respondents report the outsize use of the capabilities. For example, half of respondents in automotive and assembly (compared with 16 percent of the total average) say physical robotics are embedded in at least one function or business unit.

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Where AI is being used

By sector, telecom, high-tech, and financial-services firms are leading the way in overall adoption. That said, looking across sectors and functions, the results suggest that companies are generally following the money when deploying AI, which seems to be gaining the most traction in the areas of the business that create the most value within a given industry (Exhibit 1). In retail, for example, the use of AI in marketing and sales processes is most common: 52 percent of retail respondents say they are using AI in marketing and sales, compared with 29 percent of all respondents.

Where AI is creating value

And while the adoption of AI is still in its early days, the results suggest that it's already reaping meaningful rewards. When respondents were asked about the value captured in business functions where they have deployed AI, only 1 percent say they have seen no or negative value from that use-compared with 41 percent reporting significant value and 37 percent reporting moderate value. Across business functions, respondents using AI in manufacturing and risk indicate they are seeing the greatest value (Exhibit 2). More than half of respondents report significant value from using AI in these processes, compared with 35 percent of respondents who report significant business value from using AI in marketing and sales.

The enablers and challenges of AI

To take advantage of AI's enormous potential, the results confirm, most organizations have a long way to go in developing the core practices that enable them to realize the potential value at scale (Exhibit 3). Just 17 percent of respondents say their companies have mapped out where, across the organization, all potential AI opportunities lie. And only 18 percent say their companies have a clear strategy in place for sourcing the data that enable AI work. Indeed, nearly one-quarter of respondents say their companies have not developed any of the 11 practices we asked about.

When asked about the biggest challenges to AI adoption, respondents indicate that the most common barrier is also strategy related. They most often cite a lack of a clear AI strategy (Exhibit 4), followed by a lack of appropriate talent, functional silos that constrain end-to-end AI solutions, and a lack of leaders who demonstrate ownership of and commitment to AI.

One critical enabler of AI is a company's progress on its digitization journey. The organizations that have made the most progress in digitizing core business processes are also on the leading edge of AI adoption. At the most digitized firms, 67 percent of respondents say their organizations have embedded AI into standard business processes, compared with 43 percent at all other companies. They are most likely to have adopted machine learning, for example: 39 percent say it is embedded in their processes, compared with 16 percent at all other companies (Exhibit 5).

The most digitized organizations have also deployed AI in more functions than other companies, though both groups say AI is most commonly used in service operations and in product development. These companies are also investing much more in AI: 19 percent at the most digitized companies say more than one-fifth of their overall digitization spending goes toward AI, while just 8 percent of other respondents say the same. On average, 52 percent of respondents at these firms report significant value from using AI, compared with 38 percent of all others.

The promise and challenge of the age of artificial intelligence

And while several of the barriers to AI adoption that we asked about are much less pressing for digitized companies (only 27 percent cite a lack of AI strategy, compared with 46 percent of all others), respondents at these companies are just as likely as their peers to say it's hard to find the right talent for AI. In fact, talent is the biggest challenge for the most digitized organizations, cited by 41 percent of those respondents.

What of the workforce?

AI raises two major questions about companies' workforces: Where will we find the knowledgeable talent to deploy AI? And to what degree will AI's ability to automate activities that we pay workers to do affect the size of the workforce?

With talent being one of the biggest challenges to AI, no matter how advanced a company's digital program, it's perhaps not surprising that companies are leaving no stone unturned when sourcing people and skills. Most commonly, respondents say their organizations are taking an "all of the above" approach: hiring external talent, building capabilities in-house, and buying or licensing capabilities from large technology firms. Across industries, even the ones leading the way in AI adoption (that is, those in telecom, high tech, and financial services) report a mix of internal and external sourcing-though they are more focused than others on developing their own AI capabilities. Respondents in these sectors are more likely than average to say they're building in-house AI capabilities, which requires internal talent with the right skills. In high tech and financial services, respondents are also much likelier to report retraining or upskilling. The same is true of the most digitized companies: respondents are more likely than others to report in-house development of AI capabilities and retraining or upskilling of current employees (Exhibit 6).

At the same time, the most digitized companies have done more than others to automate human labor via AI. By function, respondents report that their processes for customer service, IT, and service operations are most commonly automated-and the digitized companies are further along than their peers in automating all three. Yet respondents at these organizations, and overall, tend to expect that AI's future effects on total head count will be minor or positive. A plurality of respondents say that, three years from now, AI won't really affect the number of employees at their companies. Among the most digitized companies, respondents are more likely to expect head count will increase than decrease. They are also more optimistic than others that their workforces will grow: 31 percent say so, compared with 18 percent of their peers. These results, along with those from other McKinsey research, suggest that AI's biggest effect on the workforce could be changes in the work that people do, particularly ever-greater collaboration between machines and people, rather than overall workforce reductions.

Looking ahead

The survey results suggest that digitization and certain foundational practices are critical to creating value from AI at scale. Here are several steps companies can take to capitalize on AI's potential:

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Make progress on your digital journey. The results confirm that digitization is a prerequisite and critical enabler for deriving value from AI. The implications of continuing digitization are significant; for many companies, they involve transformation-level changes to the very business processes at the core of the enterprise and new ways in which people will work. But without a strong digital backbone, a company's AI systems will lack the training data necessary to build better models and the ability to transform superior AI insights into behavioral changes at scale.

Scale AI's impact across the enterprise. While most companies have already deployed AI to some extent, few have embedded it into standard operating processes in multiple business units or functions, and about one-third are only piloting the use of AI. While AI is still in its early days, getting stuck in "pilot purgatory" is a real risk. Achieving results at scale requires not only the diffusion of these capabilities across the enterprise but also a real understanding and commitment on the part of leaders to drive large-scale change, as well as a focus on change management rather than on technology alone.

Put key enablers in place. While the adoption of AI is happening fast, the survey suggests that organizations tend to lack many of the foundational enablers required to derive value from AI at scale. These enablers include top-management sponsorship, development of an enterprise-wide portfolio view of AI opportunities, action to close talent gaps, and the implementation of a sophisticated data strategy-all of which require more strategic thinking around AI programs and agendas. Business and technology leaders must work quickly to establish key AI enablers. Otherwise, they risk missing out on the current-and future-AI opportunity.

About the author(s)

The contributors to the development and analysis of this survey include Michael Chui, a partner of the McKinsey Global Institute who is based in McKinsey's San Francisco office, and Sankalp Malhotra, an alumnus of the New York office.

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AI adoption advances, but foundational barriers remain

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The adoption of artificial intelligence (AI) is rapidly taking hold across global business, according to a new McKinsey Global Survey on the topic.

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Arseniy Pertzovsky, Roie Zivan, Noa Agmon

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Journal of Artificial Intelligence Research

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The science and engineering of making intelligent machines, which can also refer to intelligence as exhibited by an artificial (man-made, non-natural, manufactured) entity.

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Project Management Templates

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Free Project Management Templates - Project Management Docs

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All of our free PM templates are developed by PMP certified Project Managers with extensive project management experience. Our templates are organized according to PMBOK® process group. Templates are one step in the how-to project management process and perhaps the easiest way to learn all about project management.

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~ By Tim Bryce

Those who do not do their homework do not graduate.
Bryce's Law

In its simplest form, a Feasibility Study represents a definition of a problem or opportunity to be studied, an analysis of the current mode of operation, a definition of requirements, an evaluation of alternatives, and an agreed upon course of action. As such, the activities for preparing a Feasibility Study are generic in nature and can be applied to any type of project, be it for systems and software development, making an acquisition, or any other project.

There are basically six parts to any effective Feasibility Study:

1. The Project Scope which is used to define the business problem and/or opportunity to be addressed. The old adage, "The problem well stated is half solved," is very apropos. The scope should be definitive and to the point; rambling narrative serves no purpose and can actually confuse project participants. It is also necessary to define the parts of the business affected either directly or indirectly, including project participants and end-user areas affected by the project. The project sponsor should be identified, particularly if he/she is footing the bill.

I have seen too many projects in the corporate world started without a well defined project scope. Consequently, projects have wandered in and out of their boundaries causing them to produce either far too much or far too little than what is truly needed.

2. The Current Analysis is used to define and understand the current method of implementation, such as a system, a product, etc. From this analysis, it is not uncommon to discover there is actually nothing wrong with the current system or product other than some misunderstandings regarding it or perhaps it needs some simple modifications as opposed to a major overhaul. Also, the strengths and weaknesses of the current approach are identified (pros and cons). In addition, there may very well be elements of the current system or product that may be used in its successor thus saving time and money later on. Without such analysis, this may never be discovered.

Analysts are cautioned to avoid the temptation to stop and correct any problems encountered in the current system at this time. Simply document your findings instead, otherwise you will spend more time unnecessarily in this stage (aka "Analysis Paralysis").

3. Requirements and how requirements are defined depends on the object of the project's attention. For example, how requirements are specified for a product are substantially different than requirements for an edifice, a bridge, or an information system. Each exhibits totally different properties and, as such, are defined differently. How you define requirements for software is also substantially different than how you define them for systems.

4. The Approach represents the recommended solution or course of action to satisfy the requirements. Here, various alternatives are considered along with an explanation as to why the preferred solution was selected. In terms of design related projects, it is here where whole rough designs (e.g., "renderings") are developed in order to determine viability. It is also at this point where the use of existing structures and commercial alternatives are considered (e.g., "build versus buy" decisions). The overriding considerations though are:

Does the recommended approach satisfy the requirements?

Is it also a practical and viable solution? (Will it "Play in Poughkeepsie?")

A thorough analysis here is needed in order to perform the next step...

5. Evaluation examines the cost effectiveness of the approach selected. This begins with an analysis of the estimated total cost of the project. In addition to the recommended solution, other alternatives are estimated in order to offer an economic comparison. For development projects, an estimate of labour and out-of-pocket expenses is assembled along with a project schedule showing the project path and start-and-end dates.

After the total cost of the project has been calculated, a cost and evaluation summary is prepared which includes such things as a cost/benefit analysis, return on investment, etc.

6. Review that all of the preceding elements are then assembled into a Feasibility Study and a formal review is conducted with all parties involved. The review serves two purposes: to substantiate the thoroughness and accuracy of the Feasibility Study, and to make a project decision; either approve it, reject it, or ask that it be revised before making a final decision. If approved, it is very important that all parties sign the document which expresses their acceptance and commitment to it; it may be a seemingly small gesture, but signatures carry a lot of weight later on as the project progresses. If the Feasibility Study is rejected, the reasons for its rejection should be explained and attached to the document.

Conclusion

It should be remembered that a Feasibility Study is more of a way of thinking as opposed to a bureaucratic process. For example, what I have just described is essentially the same process we all follow when purchasing an car or a home. As the scope of the project grows, it becomes more important to document the Feasibility Study particularly if large amounts of money are involved and/or the criticality of delivery. Not only should the Feasibility Study contain sufficient detail to carry on to the next succeeding phase in the project, but it should also be used for comparative analysis when preparing the final Project Audit which analyses what was delivered versus what was proposed in the Feasibility Study.

Feasibility Studies represent a common sense approach to planning. Frankly, it is just plain good business to conduct them. However, I have read where some people in the IT field, such as the "Agile" methodology proponents, consider Feasibility Studies to be a colossal waste of time. If this is true, I've got a good used car I want to sell them.
Tim Bryce is the Managing Director of M. Bryce & Associates (MBA) of Palm Harbour, Florida, a management consulting firm specialising in Information Resource Management (IRM). Mr. Bryce has over 30 years of experience in the field. He is available for training and consulting on an international basis. His corporate web page is at: http://www.phmainstreet.com/mba/

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The Elements of a Good Feasibility Study

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By Tim Bryce Those who do not do their homework do not graduate. Bryce's Law In its simplest form, a Feasibility Study represents a definition of a problem or opportunity to be studied, an analysis of the current mode of operation, a definition of requirements, an evaluation of alternatives, and an agreed upon course of action.

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As with any research project, data collection is incredibly important. However, several aspects come into play in the data collection process. The three most crucial aspects include: the cost of the selected data collection method; the accuracy of data collected; and the efficiency of data collection.

Despite the rise in popularity of online and mobile surveys, face-to-face (in-person) interviews still remain a popular data collection method. A face-to-face interview method provides advantages over other data collection methods. They include:

Accurate screening. Face-to-face interviews help with more accurate screening. Te individual being interviewed is unable to provide false information during screening questions such as gender, age, or race. It is possible to get around screening questions in online and mobile surveys.Online and mobile surveys that offer incentives may actually encourage answer falsification. Individuals may enter incorrect demographic information so they are able to complete the survey and gain the incentive. The answers the individual provides may all be truthful, but for the purpose of data analysis, the data will be inaccurate and misleading.

Capture verbal and non-verbal ques. A face-to-face interview is no doubt going to capture verbal and non-verbal ques, but this method also affords the capture of non-verbal ques including body language, which can indicate a level of discomfort with the questions. Adversely, it can also indicate a level of enthusiasm for the topics being discussed in the interview. Let's discuss an employee job interview, for example. Capturing non-verbal ques may make the difference between selecting an employee that is less skilled, but displays a tremendous amount of enthusiasm for the position. Capturing non-verbal ques is not possible in online or mobile surveys.

Keep focus. The interviewer is the one that has control over the interview and can keep the interviewee focused and on track to completion. Online andmobile surveys are often completed during time convenient for the respondent, but are often in the midst of other distractions such as texting, reading and answering emails, video streaming, web surfing, social sharing, and more. Face-to-face interviews are in-the-moment, free from technological distractions.

Capture emotions and behaviors. Face-to-face interviews can no doubt capture an interviewee's emotions and behaviors. Similar to not being able to capture verbal and non-verbal ques, online and mobile surveys can also not capture raw emotions and behavior.

As with any data collection method, face-to-face interviews also provide some disadvantages over other data collection methods. They include:

Cost. Cost is a major disadvantage for face-to-face interviews. They require a staff of people to conduct the interviews, which means there will be personnel costs. Personnel are the highest cost a business can incur. It's difficult to keep costs low when personnel are needed.

Quality of data by interviewer. The quality of data you receive will often depend on the ability of the interviewer. Some people have the natural ability to conduct an interview and gather data well. The likelihood of the entire interviewing staff having those skills is low. Some interviewers may also have their own biases that could impact the way they input responses. This is likely to happen in hot-topic opinion polls.

Manual data entry. If the interview is administered on paper, the data collected will need to be entered manually, or scanned, if a scannable interview questionnaire is created. Data entry and scanning of paper questionnaires can significantly increase the cost of the project. A staff of data entry personnel will need to be hired. Additionally, data entry can prolong the analysis process. Mobile surveys on iPads, tablets, or other mobile devices can cut-down on manual data entry costs and information is ready for analysis.

Limit sample size. The size of the sample is limited to the size of your interviewing staff, the area in which the interviews are conducted, and the number of qualified respondents within that area. It may be necessary to conduct several interviews over multiple areas, which again can increase costs.

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Advantages and Disadvantages of Face-to-Face Data Collection

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Take a look at the advantages and disadvantages of the face-to-face data collection method As with any research project, data collection is incredibly important. However, several aspects come into play in the data collection process. The three most crucial aspects ... Continue reading →

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Create and distribute surveys with our user-friendly yet advanced online survey tool, then analyse results graphically. UK-based team.

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Research Methodolgies

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Module 9 : Introduction to Research - Table of Contents

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9 Disadvantages of Questionnaires

We've gathered the 9 biggest disadvantages. Outweigh both the pros and cons of a survey before you make your decision.

1. Dishonesty

While there are many positives to questionnaires, dishonesty can be an issue. Respondents may not be 100 percent truthful with their answers. This can happen for a variety of reasons, including social desirability bias and attempting to protect privacy. Stop dishonesty in its tracks by assuring respondents that their privacy is valued and that the process prevents personal identification.

2. Lack of conscientious responses

Every administrator hopes for conscientious responses, but there's no way to know if the respondent has really thought the question through before answering. At times, answers will be chosen before fully reading the question or the potential answers. Sometimes respondents will skip through questions, or split-second choices may be made, affecting the validity of your data.

This drawback is tough to defeat, but if you make your survey short and your questions simple you're likely to get the most accurate responses.

3. Differences in understanding and interpretation

The trouble with not presenting questions to users face-to-face is that each may have different interpretations of your questions. Without someone to explain the questionnaire fully and ensure each individual has the same understanding, results can be subjective.

Respondents may have trouble grasping the meaning of some questions that may seem clear to the creator. This miscommunication can lead to skewed results. The best way to combat this situation is to create simple questions that are easy to answer.

4. Hard to convey feelings and emotions

A survey or questionnaire cannot fully capture emotional responses or the feelings of the respondents. Without administering the questionnaire face-to-face, there is no way to observe facial expression, reactions or body language.

Without these subtleties, useful data can go unnoticed. Don't get stuck trying to interpret emotion in data, instead go for a Likert scale, the response scale that often uses a rating scale from "slightly agree" to "strongly disagree." This allows for strength and assertion in responses rather than multiple choice.

5. Some questions are difficult to analyze

Questionnaires produce a lot of data. Multiple choice questions can be tabulated and graphed, but open-ended questions are different. Open-ended questions allow for individualized answers which cannot be quantified and must be reviewed by a human.

Too many open-ended questions can produce more data than can be analyzed. Fix this pitfall but choosing your question types carefully. If you have ten questions, you probably don't want more than one to be open-ended since these have no way to be quantified.

6. Respondents may have a hidden agenda

As with any sort of research, bias can be an issue. Participants in your survey may have an interest in your product, idea or service. Others may be influenced to participate based on the subject of your questionnaire. These proclivities can lead to inaccuracies in your data, generated from an imbalance of respondents who see your topic in an overly positive or negative light. Filter out a hidden agenda with a pre-screening. Come up with a few indirect questions that will remove those results wreckers.

Filter out a hidden agenda with a pre-screening. Come up with a few indirect questions that will remove those results wreckers.

7. Lack of personalization

Customization is the prevailing marketing theme. Any piece of marketing material is at risk of seeming impersonal unless time and care are taken to personalize it. If you're unable to add touches of personalization, some potential respondents may be put off and ignore it. This can be particularly difficult when the questionnaire or survey is taken voluntarily on a website, outside of a purchase or email.

Fix this by always sending emails containing respondents names. Use dynamic content on websites, and strive to use names, personal data and personalized content in all communication.

8. Skipped questions

When using questionnaires, there is a chance that some questions will be ignored. If questions are not required, there is always that risk they won't be answered. Online questionnaires offer a simple solution to this issue: make answering the question required.

Otherwise, make your survey short and your questions uncomplicated and you will avoid question skipping and get better completion rates.

9. Accessibility issues

No matter what form of delivery is used, lack of accessibility is a threat. Surveys may be unsuitable for users with a visual or hearing impairment, or other impediments such as illiteracy. This should be considered when choosing to do research in this manner. Always choose a questionnaire platform that has accessibility options built in.

Advantages of Using Online Questionnaires

There are numerous advantages to using online questionnaires. Firstly, as mentioned, they can be sent out quickly and turnaround can be relatively short. You're able to reach your audience in the moments that matter - while they're browsing your products, as they read your content, as soon as they've made a purchase.

Online questionnaires allow users time to consider responses - a distinct advantage over face-to-face or telephone methods. Besides these great perks, the cost of online questionnaires is low and actionable items produced by the data can produce a high return on investment.

There are additional benefits as well. User responses can be precoded, eliminating transcription errors. The data is already in an electronic format, allowing for easy analysis without the hassle of digitizing data. Lastly, guidance and/or software is available through providers such as Survey Anyplace at an affordable cost and with a wide range of possibilities to make it fit your brand and your requirements.

Why Selecting the Right Type of Questions is So Important

A questionnaire is only as effective as its questions. These questions need to be evaluated quickly and they need to produce data that can be acted upon. If you make questions too difficult or confusing to answer, you may end up with meaningless data. Choosing the wrong type of question can also lead to incomplete results or data that is hard to interpret.

The main question types are open-ended, closed-ended and semi-closed ended questions. Within these types, there is an abundance of ways to present your query from ratings to yes/no question. Learn how to select the right types of survey questions for your needs in our essential guide.

How to Motivate Your Respondents to Participate

Questionnaires have a bad rep and over the past years, many researchers saw their response rate decline due to their unpopularity. That's why we've compiled an extensive list of tips that can help you boost your response rate.

The most obvious way to do this is to reward respondents for their time through gifts, credits or payment. Survey Anyplace even offers fun reward tools like a digital scratch card or slot machine.

Rewarding a respondent is just one means of getting responses. Respondents also want to make sure their privacy is protected, make it clear that answers are confidential and make sure your privacy policy is up-to-date. Emphasize how the data will be useful to the user experience or helpful to society. People like to feel like they're doing a good thing.

They also want to know that they can complete your questionnaire without the hassle of dinner time calls. Respecting the respondent's time goes a long way towards showing you appreciate their participation.

Be sure to send reminders as well - many people forget anything that isn't at the top of their minds.

Find inspiration for your Questionnaire Design

Designing a questionnaire may sound simple until you sit down to write the questions. Planning, content creation and graphic design are all important. Not everyone has the time or the professional design skills to create a questionnaire.

That's why some software companies like Survey Anyplace offer templates to create questions in minutes.

Now that you know how helpful and effective questionnaires and surveys can be, get a free Survey Anyplace account!

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9 Advantages and Disadvantages of Questionnaires - Survey Anyplace

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We've gathered the 9 biggest disadvantages. Outweigh both the pros and cons of a survey before you make your decision. While there are many positives to questionnaires, dishonesty can be an issue. Respondents may not be 100 percent truthful with their answers.