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Innovation doesn't stop at the idea stage; you need purpose, structure and talent to deliver true value

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Executives from companies that treat purpose as a core driver of strategy and decision-making reported greater ability to drive successful innovation and transformational change.

As part of our drive to understand how - and more importantly why - companies are employing purpose to guide and lend impetus to their transformation, the EY Beacon Institute sponsored Harvard Business Review Analytic Services (HBRAS) to conduct a global survey of 474 executives from across different sectors and geographies.

Launching this September, here is a snapshot of the survey's findings and key messages from the report.
Download the PDF

Purpose is a powerful though underutilized asset

Most executives believe purpose matters.

Eighty-nine percent of executives surveyed said a strong sense of collective purpose drives employee satisfaction; 84 percent said it can affect an organization's ability to transform, and 80 percent said it helps increase customer loyalty.

... but only a minority of executives say their company currently runs in a purpose-driven way.

Forty-six percent said their company has a strong sense of purpose while another 44 percent say their company is trying to develop one.

Companies with a strong sense of purpose are able to transform and innovate better.

Executives from companies that treat purpose as a core driver of strategy and decision-making reported greater ability to drive successful innovation and transformational change. More than half (53%) of executives at companies with a strong sense of purpose said their organization is successful with innovation and transformation efforts, while less than one-fifth (19%) report success at companies who have not thought about purpose.

A definition of purpose:

The survey defined organizational purpose as "an aspirational reason for being which inspires and provides a call to action for an organization and its partners and stakeholders and provides benefit to local and global society."

Prioritizers, Developer and Laggards

The survey revealed that most companies in the survey fall into three categories with respect to purpose: prioritizers, companies that already have a clearly articulated and understood purpose (39 percent); developers, companies that do not yet have a clearly articulated purpose, but are working to develop one (48 percent); and laggards, companies that have not yet begun to develop or even think about purpose.

Purpose and growth

Prioritizers are reaping the rewards of embedding purpose: in those organizations where purpose had truly become a driver of strategy and decision-making, executives reported a greater ability to deliver revenue growth and drive successful innovation and ongoing transformation.

58 percent of prioritizers said they experienced growth of 10 percent or more over the past three years, compared to 51 percent of the developers and 42 percent of the laggards.

Purpose-or the lack of shared understanding of purpose-seems to have a direct impact on the bottom line. Forty-two percent of laggards reported flat or declining revenue over the past three years, compared to 19 percent of developers and only 15 percent of prioritizers.

Purpose-led transformation and innovation

Companies that treat purpose as a core driver of strategy and decision-making are much more likely to focus on innovation and continuous transformation, and are more successful in these efforts.

Purpose and meaning

Executives at prioritizers' companies say their corporate purpose includes inspiring innovation and positive change, providing employees with a sense of meaning and fulfilment, creating value for the customer, and making a positive impact on their community.

"The sense of being part of something greater than yourself can lead to high levels of engagement, high levels of creativity, and the willingness to partner across functional and product boundaries within a company, which are hugely powerful," said Rebecca Henderson, the John and Natty McArthur University Professor at Harvard Business School.

"Once they're past a certain financial threshold, many people are as motivated by intrinsic meaning and the sense that they are contributing to something worthwhile as much as they are by financial returns or status."

With so much potential, why is purpose not more widely embedded?

What to improve: the executives surveyed said that companies need to do a better job embedding their purpose in the organization, particularly in leadership development and training, in employee performance metrics, targets and rewards, and in operations.

Respondents also cited a number of barriers including short-term shareholder pressure, systems and infrastructure that are not aligned with long-term purpose.

One key difference between the prioritizers and laggards stood out in the survey: communication. The laggards reported poor communication from top leadership as the most significant challenge in activating purpose in their organization.

While most companies surveyed recognize the value and potential of purpose, a majority of companies have yet to embed a shared a sense of purpose throughout their organizations that would allow them to see it benefits.

Better communication from CEOs and senior executives, leadership development and training, a better fit between employee performance metrics and rewards, and better integration into operations all contribute to more successful purpose-led transformation.

"It is essential that companies develop the kind of leaders that can communicate and align the whole organization around purpose. It's easy to state a purpose and state a set of values. It's much harder to enact them in the organization because it requires you to continually search for consistency across many disciplines and many activities."

- - Professor Michael Beer, Harvard Business School.

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As part of our drive to understand how - and more importantly why - companies are employing purpose to guide and lend impetus to their transformation, the EY Beacon Institute sponsored Harvard Business Review Analytic Services (HBRAS) to conduct a global survey of 474 executives from across different sectors and geographies.

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EY's megatrends report

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CEO and other C-level decision makers

Transformation Sponsors and key Transformation stakeholders

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If you have a transformative mindset, you are able to pivot quickly in the face of disruption

But then you have to deliver the transformation

True transformation is about understanding the journey you are on, not just the process

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Intelligent Automation and the shifting role of humans

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Intelligent Automation and the shifting role of humans

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Artificial intelligence and the role of data

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Artificial intelligence and the role of data

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Can AI transform the way organizations transform themselves?

Will transformation management be fully automated?

Will transformation leadership be part-automated or even fully automated?

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Make purpose a priority

Engage with your customers to build loyalty

Leverage human and technology assets to drive greater efficiency and insights

Drive business agility through a value-led transformation approach

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Think bigger: managing value holistically

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Think bigger: managing value holistically

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Value is no longer judged on a purely financial basis

Organizations are increasingly under pressure to demonstrate their contribution to society at large

Will we see the end of the primacy of quarterly results?

Refer to the Inclusive Capitalism research, looking at long-term value

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Download the white paper for information on how to account and report for Long Term Value

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Refer to EY Megatrends research framework (primary forces, weak signals, etc)

Refer to surveys across different industries

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The upside of disruption - Megatrends shaping 2016 and beyond

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As disruption becomes an everyday occurrence, we explore its primary causes and the megatrends that are shaping our future.

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Organizations need to develop business agility to drive successful  Transformation

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Highlight EY's holistic approach to transformation, based on Adaptive Transformation

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EY 2016 Adaptive Transformation Approach

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Reference EY's PoV on the impactof automation

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Automation offers tremendous potential for leaders looking to drive transformation in their organization: from cost savings and increased delivery speed, to new operating models, to higher value activities for employees. It's this latter point that we feel is the most important - something that has just been confirmed by the World Economic Forum's recent Future of Jobs Report 2018.

Business leaders understand that successful implementation of automation is complex and likely to have far-reaching effects on their organizations. With "business as usual" no longer possible post-automation, leaders must navigate risks unrelated to algorithms, such as maintaining employee morale, building support for change and helping teams adapt to new ways of working throughout the reshaped organization. Getting automation right requires a strong focus on people strategies, transforming the business while maintaining an engaged, motivated and appropriately skilled workforce.

Earlier this year we undertook proprietary research aimed at providing actionable insights to leaders ready to take advantage of automation and effectively drive business transformation. As part of this effort, we mapped Frey & Osborne automation scores to nearly 2,000 occupations in four economies (US, UK, Canada and Australia); categorized those occupations into 15 business functions and 50 subfunctions across 16 industry sectors. This detailed mapping allowed us to understand how applicable automation was to different economies, sectors and business functions. Then, using transformations of ONET work activity data, we derived the amount of time that workers spend on individual tasks. When linked to the business function data above, the task-level analysis allows leaders to zoom in from a business function all the way down to an individual task, to identify the highest ROI automation candidates.

Our analysis uncovered a few surprising findings. The potential to automate tasks differs by more than 2X across sectors and up to 7X between functions as varied as finance (heavily rules-based, where 80% of tasks hold potential for automation) and learning and development (with only 12% of work potentially subject to automation). While it is important to understand the applicability of automation in your sector, we found that every sector can transform roughly a third of its work.

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The future workplace: How to automate intelligently

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Automation offers tremendous potential for leaders looking to drive transformation in their organization: from cost savings and increased delivery speed, to new operating models, to higher value activities for employees. It's this latter point that we feel is the most important - something that has just been confirmed by the World Economic Forum's recent Future of Jobs Report 2018.

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Background reading to see competitors' positioning

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See more articles like this one in the following categories

Deloitte, EY, PricewaterhouseCoopers (PwC) and KPMG are among the largest service providers in the accounting industry- collectively referred to as the Big Four. Many of the financial and consulting services offered by these firms - such as advising on investment decisions - involve finding patterns in very large sets of data. This data is often beyond the understanding of a single person (or team of persons) and is often "noisy" and without a set format.

The Big Four all seem to have artificial intelligence initiatives - and the goal of this article here is to compare and contrast their AI-related efforts (and purported results). A few of the Big Four have invested heavily in innovation labs and claim to embrace a new era of automation and augmentation.

In the article below we aim to answer the following three questions:

What are the different AI technologies the Big Four are implementing and how?

Which services are most likely to be automated first?

What are the similarities and differences in the AI initiatives and strategy for each of the Big Four?

We'll explore the AI investments and initiatives of these companies one-by-one, starting with Deloitte.

Deloitte

It is one of the biggest accounting brands under which lie several professionals and firms that offer auditing, strategy consulting and tax preparation related services.

As with most large consulting firms, Deloitte vies for market share and press through thought leadership - and like the other Big Four - it has increasingly focused it's white papers and research on the topic of artificial intelligence. A recent Deloitte article breaks down AI technologies into the following discreet categories:

1. Product: AI technology embedded into the product or service itself to provide all the associated end-customer benefits.

2. Process: AI technology to streamline daily workflow and operations to automate and increase day-to-day productivity. Automation can happen in two forms: by either reducing manpower directly or augmenting a worker's ability to perform a task better/faster.

3. Insight: AI is used to make informed and strategic decisions to provide targeted advice to a company to new or existing businesses.

Based on our assessment, most of Deloitte's own AI-related initiatives seem to fall into the categories of Process and Insight, as we'll see below.

A concrete example of an AI-enabled process is the document-review platform developed by Deloitte US i nnovation that went live in 2014. Deloitte claims that this platform has automated the process of reviewing and extracting all the relevant information from contracts, and reducing meticulous, laborious human efforts.

The company claims that this technology has helped reduce the time spent on reviewing legal contract documents, invoices, financial statements and board meeting minutes by up to 50 percent or more.

Deloitte has recently partnered with IBM Watson. The Deloitte touts the partnership as a way to provide end users with cognitive-technology-enhanced solutions for their businesses. Following are some business use-cases as a result of this partnership:

LeasePoint- It is powered by IBM TRIRIGA and utilizes Deloittes knowledge in leasing industry to teach the AI-enabled system to develop an end-to-end leasing portfolio

Visual inspection of assets- Using IBM's Maximo technology Deloitte claims to be improving asset inspection on wind farms. Learn more in this video.

Deloitte Catalyst - another recent AI initiative from the global consulting giant - is a network of startups, that are working together to translate AI technologies into practical business solutions for client firms.

Deloitte offers Catalyst companies funding, and access to Deloitte's client base in order to partner and flesh out their business applications. Most AI firms are still figuring out exactly how to drive business value with their technology, and Deloitte hopes to forge partnerships with these cutting-edge firms to help bring the latest AI innovations to their clients. The video below explains the initiative (starting around 55 seconds):

Ayasdi, Narrative Science, and Kira are claimed to be among the businesses partnering with Deloitte in their Catalyst program.

"Technology like AI can help make these processes more efficient, accurate and comprehensive, helping us get better insights to clients sooner, and empowering our professionals to deliver high-value services to our clients". - Craig Muraskin, Managing Director, Deloitte U.S. Innovation Group

EY

Formerly known as Ernst & Young, EY is one of the largest professional service providing firms in the world. It operates as a network of firms which function independently in different countries, collectively offering assurance, audit, tax, consulting and advisory services to companies.

The company has recently applied AI to the analysis of lease contracts. The company claims that the use of AI has made it easier to capture relevant information from contracts such as lease commencement date, amount to be paid, and renewal or termination options.

According to an article in Accounting Today, EY is working to automate the auditing process. They claim that this reduces the administrative time spent on reviewing audit documents and gives employees more time to participate in the judgment and analytical part of the process. These applications appear to be in pilot phase (which is to be expected).

as sifting through the auditing or lease documents can be extremely time consuming. According to EY Global Assurance Innovation Leader Jeanne Boillet in a June 2017 report:

"These pilots show that AI tools would make it possible to review about 70%-80% of a simple lease's contents electronically, leaving the remainder to be considered by a human. With more complex leases (in real estate, for instance), that figure would be more like 40%, but as the tools improve, and the machines learn, it is likely that more complex contracts and data can be read, managed and analyzed."

Another area where EY has applied AI technology is the automation of routine tasks, such as auditing (which we covered in greater depth in our full article on AI in insurance), by using it's own proprietary Robotic Process Automation (RPA) system. EY claims that this technology, known as helps the firm deliver more accurate, efficient audits for its clients.

EY Australia has already adopted this digital auditing technology, claiming that 50% of its bank audit confirmations were lodged using the AI-enabled system. This AI-enabled system can accept and confirm audit requests, process and provide the auditors with the relevant documentation for final analysis and judgment.

EY has also launched an AI proof-of-concept, using computer vision to enable airborne drones to monitor inventory during the auditing process. For example, this drone can count the number of vehicles in a production plant under audit, and communicate this data directly into the EY Canvas - the global audit digital platform.

According to EY, use of drones allows more data to be captured in the process of auditing. It also allows auditors to focus on risk areas rather than taking stock of inventory manually. This drone initiative seems to be in research and development mode at the time being and doesn't seem to be a service offered broadly to EY's client base.

While overtly embracing the developments in AI, EY is also aware of the threat AI poses to white-collar work, and they appear to be addressing automation seriously in their thought leadership. EY's recent report on the future of work highlights some of the societal and regulatory concerns of job automation in the decade ahead.

Predictably, much of EY's thought leadership touts the benefits of augmentation instead of automation. EY Global Assurance Innovation Leader Jeanne Boillet says that Artificial Intelligence "has the potential to transform audit, but it will never replace the auditor".

We find the word "never" to be downright ridiculous, but we cannot blame EY leadership from having to tout this opinion, and there is no reason to blame Mrs. Boillet for being so black-and-white about the matter, anyone in her shoes would say the same thing.

This belief in an "augmented", not an "automated" future is a common theme among large, people-heavy legacy businesses. Many would argue that businesses of this kind (chock full of knowledge workers working in spreadsheets) are among the biggest "low-hanging fruit" for automation, and it behooves older people-heavy businesses to dispel disturbing thoughts about massive layoffs.

By no means are we suggesting that the Big Four are inherently doomed to perish by nimbler, less human-laden competitors. Rather, we want to address the fact that these firms can be expected to have only one opinion on this matter.

The impact on employee morale and shareholder value would likely be tragic if EY's leadership (or the leadership of Accenture, Deloitte, or any other large consulting firm) were to state openly that many of their current jobs may be fully automated, and that some of these jobs would not be replaced. Hence, large firms of this kind are only permitted to give one opinion, an inherently self-serving one, one with a commitment to stave off risk and ensure it's own survival.

We don't consider this immoral, it's the same kind of self-preservation we'd see in any other sector or industry. It may not be blameworthy, but it is our job here at TechEmergence to make it known that these opinions should not be taken at face value.

Whenever a company has no choice but to tout a single perspective, we're likely to lay those facts outright so that business readers (like yourself) can make more informed decisions. We should expect to see large, people-heavy legacy companies
(For more insight into the kinds of jobs that might be first - or last - to automate, read or listen to our previous interviews with Martin Ford, Kevin LaGrandeur, and Marshall Brain - all of which are focused on the impact of automation on the job market.)
PwC

The second largest professional services firm by revenue, PwC, claims to have begun adopting AI as well. The International Accounting Bulletin awarded the 'Audit Innovation of the year' for PwC's technology GL.ai in October 2017. GL.ai was developed in collaboration with H2O.ai, a Silicon Valley company that developed an AI-enabled system capable of analyzing documents and preparing reports.

Using AI approaches like reinforcement learning, PwC claims that GL.ai learns and becomes more capable with every audit (a common capability for ML applications). GL.ai has already been trained on audit data from Canada, Germany, Sweden and the UK. We were unable to find robust case studies of the technology in our analysis, and - like most of the AI applications at Big Four firms at this time - the technology seems to be in development.

They claim to have made a significant investment in Natural Language Processing (NLP)- an AI-enable technology for this purpose. NLP makes sense of complex lease agreements, revenue contracts, and board meeting minutes to form meaningful insights for clients.

Anand Rao - Global AI lead at PwC - told TechEmergence about another PwC AI use-case in the automotive industry:

"Using 'digital twin' technology and 'future power' we have helped large auto manufacturers visualize and navigate over 200,000 go-to-market scenarios to fine-tune their strategy and launch a new multi-billion dollar business in ridesharing and autonomous/electric vehicles. This particular application has won the Best Enterprise Award recently and is the wave of next generation of AI on 'Gamifying Strategy' - it takes the methods used to create AlphaGo and other game playing programs to the Boardrooms and Strategy Groups. We have done this at more than half-a-dozen clients".

Like the other Big Four firms, PwC is actively involved in publishing thought leadership (articles, white papers, and more) on the implications of artificial intelligence. A recent PwC analysis of the financial services sector identifies a number of automation and augmentation concerns related to AI - and advice from PwC on how firms might adapt to AI in the future.

PwC's " AI: Sizing the prize " report estimates the percent increase in GDP to be attributed to AI in various economic regions (see below). The image below is only a small snapshot, because of the complexity of the topic (and the inherent challenges in predicting the future), we advise the reader to assess multiple sources and develop their own conclusions.

"Artificial intelligence can help people make faster, better, and cheaper decisions. But you have to be willing to collaborate with the machine, and not just treat it as either a servant or an overlord". - Anand Rao, US Analytics Group Innovation Leader

KPMG

KPMG has built its own portfolio of AI tools called KPMG Ignite. The KPMG Ignite offering is designed to enhance business decisions and process on a digital platform.

Key features of KPMG Ignite include AI tools (a few examples of which are listed below), AI integrators to make these tools compatible with existing IT infrastructure, guidance for client firms and testing, prototype development, and innovation on emerging AI applications.

KPMG is also using AI to analyze and extract information from leasing or investment agreements. Following are some of the business process where KPMG claims to have applied AI-enabled technology-

Call Center Analytics Engine- U tilizes NLP to design a model to predict future events and even convert customer calls to unstructured text, which is then streamlined to identify keywords, customer sentiment and predict future trends.

AI Anomalous Event Predicting Tool- Uses AI techniques to develop a model to predict future business events.

Document Compliance Assessment Engine- Uses AI to read a documentation- contract, leasing and investment agreement- in its entirety and yield relevant information.

(Learn more about Business implications of AI from KPMG here)

KPMG signed a "Participation Agreement" with IBM in June 2016 - apparently with the intention of using IBM Watson to automate audit-related processes. It isn't clear if this "Participation Agreement" involved paying IBM, or if both firms are looking to prove out a use-case together in a kind of barter. Little seems to be available about the present progress of said agreement.

In a 2016 press release, KPMG's John Kelly (Senior Vice-President, Cognitive Solutions and IBM Research) states:

"Auditing and similar knowledge services are increasingly challenged with tackling immense volumes of unstructured data. Cognitive technologies such as Watson can transform how this data is understood and how critical decisions are made. By applying Watson, KPMG is taking a forward-looking approach to extending its expertise, helping professionals and clients gain new insights from critical enterprise information"

As we might expect, KPMG has joined the thought leadership game around artificial intelligence - with the intent of informing their clients and appearing ahead of the curve. Like Deloitte, they've created a number of layperson-friendly AI explainer videos to explain AI's potential impact on their work:

KPMG's "ethical compass for automation" highlights some of the societal concerns that they consider to be important in the years ahead. KPMG's Automation Practice Lead, Todd Loar, explains the fundamental findings of their report in the 5-minute presentation below:

On the Game of Thought Leadership for Consulting Companies

As analysts in the AI industry, we're tasked with sorting out truth from hype.

With the popularity of AI (and the growing sentiment of its inevitability in effecting all business sectors), it often behooves companies to seem ahead of the AI curve.

In the world of startups, companies increasingly see their competitors use "artificial intelligence" in pitch decks and on their "about" page - and the pressure mounts to do the same (even if they have no AI skills or talent, and even if the product is in no way predicated on artificial intelligence).

In the world of consulting firms, the same phenomenon occurs. Businesses must "keep up with the Joneses" in the perception game of AI competence, or else they'll lose business to someone who puts up a better front. As in the startup world, this leads to a vicious cycle of mutual one-upmanship without the prowess to back up the claims.

For some reason I find that business readers often know intuitively that a startup's pitch deck is self-serving, but they're often unable to apply that same "taking it with a grain of salt" mentality to white papers and reports, which - like pitch decks - serve the goal of filling a company's bank accounts, not of portraying truth for its own sake.

This disclaimer is important:

Thought leadership is perception management, not objective analysis.

This isn't to say that we don't believe that the Big Four are using AI. These companies have massive revenues and thousands of talented people - and based on our assessment, all four of the Big Four claim to have integrated AI into some of their service offerings, and we'd suspect that this is, in fact, the case.

Rather we imply they have no choice but to say that they are on the cutting edge of AI (or they risk losing business to their better-marketed competitors). This isn't blameworthy, it's business - and we aren't here to point fingers, but to lay the situation out as it is. Time will tell which of these companies is leveraging the technology most fruitfully, and we imagine that there will be a rather intense competition among these top firms to improve their service quality and profit margins through automation.

Indeed part of our job here at TechEmergence is to do the "digging" ourselves and aim to get behind the bluster of press releases, but we'd like to remind our readers to think like an analyst as they consume information that might affect their strategy and procurement.

Concluding Thoughts on AI in the Accounting Big Four

In the sections above, we reviewed how AI is being used by all the Big Four. Based on our research on these companies, two common trends seem clear:

First, they seem to be investing in AI technologies for accounting services. AI is being integrated into the core of the business and its use is showing a gradual increase.

Second, the Four also purport to see AI as a critical factor to give them an edge over others and are in some sense competing with each other to gain AI supremacy.

The use of AI holds many promises for the future of these consulting giants. It may take away the pain of flipping through hundreds of pages for hours and making reports. A lot of data collected during auditing and tax preparation is unstructured and prone to human error. AI-enabled technology, trained on this unstructured data is increasingly capable of recognizing the errors and streamlining parts of the process.

Next in line for augmentation and automation are advisory services. Using consulting and advisory data, firms are now training AI networks to learn how to interact with client firms looking for business solutions.

The decision to relying on AI solutions requires administrative streamlining and strategic planning on many levels. The firms have worked hard to define these strategies to implement and integrate AI technologies in the workplace. For example, Deloitte aims to create process maps to keep track of the organization's core business processes.

Our analysis of machine learning in the finance sector indicates that this industry is among the most likely to adopt more white-collar automation in the years ahead - a trend we see clearly in the analysis of the Big Four.

This will help identify business processes where AI might have the most impact. PwC, advises choosing areas that are operational or explanatory in nature and explore AI solutions to these problems before others (source). These strategies can help to facilitate a judicial use of budget, resources, and planning that goes into the use of AI in a business.

Given the impact that AI is poised to make, there has to be a discussion about the extent to which we rely solely on machine-based intelligence and decisions. Every industry embracing AI for a business solution is concerned with the impact that this technology can have on humans in the workplace. For the Big Four, this is reason for greater concern, as they operate in a people-centric business - and one that some experts would presume to be ripe for massive disruption through automation.

Our analysis reveals that the Big Four are investigating the concerns of workforce automation - though most all of their published thought leadership paints an optimistic future of augmentation, rather than one of automation. Time will tell when it comes to the effects of AI on the knowledge workers within the Big Four. My fingers are crossed that their industry - as with many others - have insight and ability to adopt. They're certainly working on it.
Header image credit: b4f.eu

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AI in the Accounting Big Four - Comparing Deloitte, PwC, KPMG, and EY

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See more articles like this one in the following categories Deloitte, EY, PricewaterhouseCoopers (PwC) and KPMG are among the largest service providers in the accounting industry- collectively referred to as the Big Four. Many of the financial and consulting services offered by these firms - such as advising on investment decisions - involve finding patterns in very large sets of data.

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Include a case study of how a large company has led with purpose

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How can you turn digital risk into a source of competitive advantage?

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How can you turn digital risk into a source of competitive advantage?

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In this transformative age, how risk is managed will become the key to unlocking the strategic upside of disruption.

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In my previous piece, I looked at how value and risk are entwined along all parts of an organization's value chain.

The simple truth is, you can't generate value if you don't take risks. Similarly, if you can't properly manage risks, organizations will stumble into catastrophe. However, it's not just about finding a Goldilocks zone of the right amount of risks to take, but understanding the difference between the kind of risks you should and should not be taking.

The stakes couldn't be higher. Business today moves at a breathtaking pace: according to one recent study, in 1964 the average life of a company in the S&P 500 was 33 years. That is predicted to drop to 12 years by 2027.

In such a fast-moving environment, understanding the nature and scale of different kinds of risk becomes a vital first step for any company serious about capitalizing on the volatility of the era of mass digital transformation.

Organizations that will succeed in this new transformative age will be the ones that can discern the differences between these three varieties of risk and chart a course between them that leads to real value. But to do this will require a more holistic and more nuanced understanding of how different types of risk are affecting not just your organization and its component parts, but your entire value chain. The risks come in these three distinct forms:

1. Downside risks: which only represent a negative outcome for a company. There is no value in taking these risks, only the potential to preserve value (or reduce harm) by eliminating, controlling, mitigating or transferring them. They have little or no potential to be seized as an opportunity (without some clever PR to showcase how well the organization has responded to them). These risks include things like information security and cybercrime, employee fraud and regulatory compliance. These risks must be well-understood and mitigated.

2. Upside risks: directly relate to an organization's ability to execute its business strategy and objectives, and provide organizations with a positive opportunity for value creation and growth - using risk to identify the best ways to allocate capital to grow the business. These include the potential for innovations to grow consumer bases, increasing market share, or acquiring, managing and deriving value from new assets and talent. These upside risks should be considered hand-in-hand with an organization's strategy - the organization should ask itself, "with which risk do we create the most value?"

3. Outside risks: can have positive and negative impacts but are unpredictable, as they are beyond the organization's control. These could include the actions of existing and emerging competitors, and geopolitical, economic, demographic and environmental megatrends that can impact your organization either directly or indirectly. Organizations must be well-aware of the upside or downside implications of these risks and be ready to respond more quickly and effectively than their peers to add value to the bottom line.

To effectively deal with these risks will require a change in both risk management tactics and cultural mindset.

In the past, risks have been evaluated on two dimensions, potential impact and likelihood. But in today's transformative age, a third dimension - velocity - is critical. Velocity is the speed and rate at which downside risks you fail to prevent can cause damage/loss, or upside risks that you seize can drive rapid growth.

From a mindset perspective, this means risk professionals must evolve their culture from a focus on complete risk avoidance to one of identifying upside strategic risks - combining looking for ways to learn from the past while enabling progress toward the future. And they will need to do this in real time to ensure their agility can help their organizations move fast enough to intercept opportunities no matter their velocity. This is where we see the greatest level of disruption coming to risk functions, with technology helping risk functions themselves transform for the transformative age.

For example, through the adoption of robotic process automation, internal audit and compliance functions will be able to become more efficient and examine more data in a much shorter period of time. Better data management will allow for better and more real-time reporting that will support better leadership decision-making. Artificial intelligence will be used to predict where things go wrong before they go wrong.

Through this disruption, organizations will be able to have greater confidence in their ability to at worst identify issues quickly, and ideally prevent them from happening in the first place. With this, the risk professionals of the future will have more time to focus on outside and upside risks, enabling their organizations to make better decisions to help achieve long-term strategic objectives.

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How can you turn digital risk into a source of competitive advantage?

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In my previous piece, I looked at how value and risk are entwined along all parts of an organization's value chain. The simple truth is, you can't generate value if you don't take risks. Similarly, if you can't properly manage risks, organizations will stumble into catastrophe.

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How will transformation be transformed in the future?

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How will transformation be transformed in the future?

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Respond quickly and appropriately to disruption

Lead with purpose

Consider Transformation as a journey

Appreciate Intelligent Automation (IA) and impact on humans

Understand Artificial Intelligence (AI) and relationship with data

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The concept of multi-speed IT transformations has been around for a while

But we see all transformations as requiring the agility to turn ideas into capability quickly, and also have the patience and resilience to guide longer-term change

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Will it be possible to automate the TMO (or equivalent) function?

Evaluate the different types of automation that can already be delivered

Ref: Gartner PPM in 2030 report

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More insightful leadership

Clear direction to all delivery effort

Focus (as defined in an organization's strategic objectives)

BUT...

Where is the line drawn between automation and executive control?

How do you build trust in AI?

How to build human skills required to implement and maintain AI? What about the traditional PM folks?

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Include a case study of how a large company has failed due to a lack of purpose and direction

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There are numerous types of disruption, not just technology

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There are numerous types of disruption, not just technology

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Business model

Value chain

Worker behaviour

Technological

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Adaptive transformation - success stories

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Adaptive transformation - success stories

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Include a case study of how a large organization has been through the full transformation journey

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Reference EY's Intelligent Automation work

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Intelligent automation (IA) refers to the integration of robotics with multiple components from different emerging technologies.

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Provide recommendations on the key functional areas to build AI prototypes

Focus on the data

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Disruption is no longer something that happens once and is responded to once.

Organizations need to respond rapidly to disruptive threats, through, e.g.

internal transformation

acquisitions

partnerships

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Over recent decades many CEOs have honed a primary focus on short-term earnings. While the resulting unprecedented growth has been received with praise from shareholders, investors and the press, today's business leaders increasingly understand that this approach is no longer sustainable in this transformative age, defined by disruption and innovation.

Companies must now become disruption ready -adept at both responding to disruption and initiating it. They must seize the upside of disruption, simultaneously optimizing one's current business while laying the groundwork to disrupt it.

The shift in mindset is illustrated by the results of EYQ's global institutional investor survey, part of the research shared in our recent report, The CEO imperative: seize the upside of disruption or be disrupted:

Contrary to conventional wisdom, 67% of institutional investors want companies to undertake potentially disruptive innovation projects even if they are risky and may not deliver short-term returns.

73% of institutional investors say that corporate disruption readiness will become more important to their investment decision-making over the next five years.

The new mindset is characterized by the notion of duality - the co-existence of imperatives that are in contrast or conflict with each other but together make up two essential parts of the total system or outcome. In the connected and accelerated digital economy, we must address these contrasting imperatives not only simultaneously, but together.

As one of the CEOs in our disruption study remarked, "We look at this as a 'here and now' and a 'tomorrow into the future.' And the trade-offs for the short term and the long term have to live together. It's not an 'either/or.' It is necessary to be able to do both at the same time."

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How can you be both the disruptor and the disrupted?

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Over recent decades many CEOs have honed a primary focus on short-term earnings. While the resulting unprecedented growth has been received with praise from shareholders, investors and the press, today's business leaders increasingly understand that this approach is no longer sustainable in this transformative age, defined by disruption and innovation.

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Failure with traditional transformation stories

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Failure with traditional transformation stories

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Include a case study demonstrating the pitfalls of seeing transformation as a point-in-time change initiative

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Intelligent automation - success stories 

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Intelligent automation - success stories 

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Include a case study of how a large organization has increased transformation efficiency and effectiveness through intelligent automation

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Artificial intelligence - success stories 

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Artificial intelligence - success stories 

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Include a case study of how a large organization has successfully implemented some form of AI to improve executive decision making

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Responding to disruption: success stories

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Responding to disruption: success stories

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Include a case study of how a large company has proved to be resilient and responsive to the pace of change in their industry

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Data poor and inefficient transformation stories

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Data poor and inefficient transformation stories

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Include a case study of how a large transformation can have a huge TMO team without adding any additional value

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Slow and ineffective transformation decision making stories

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Slow and ineffective transformation decision making stories

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Include a case study of how slow and cumbersome transformation governance can be without AI insights / decision support

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Failing to respond to disruption stories

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Failing to respond to disruption stories

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Include a case study of how a large company has failed to respond to the pace of change in their industry