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Three Reasons All Corporate Boards Need Someone Who Understands Both Analytic Innovation and Analytic Strategy

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According to a 2019 report from CB Insights [1], between 2010 and 2019 there were 635 AI acquisitions. The acquisitions break into three groups, as can be seen in the visualization below (Figure 1) from CB Insights. Facebook, Apple, Google, Microsoft, Amazon (FAGMA) and Intel accounted for 67 acquisitions, each making 7 or more acquisitions during the period 2010 to August 2019 (Group 1). Fifty two companies made between 2 and 6 acquisitions during this period (Group 2), and 431 companies made a single acquisition (Group 3).

Figure 1 This histogram from CBInsights show the number of AI acquisitions that a company made during the period 2010 – August 2019. Facebook, Apple, Google, Microsoft, Amazon and Intel accounted for 67 acquisitions, each making 7 or more acquisitions during this period. Fifty two companies made between 2 and 6 acquisitions during this period, and 431 companies made a single acquisition. Source: CBInsights, retrieved from: https://www.cbinsights.com/research/top-acquirers-ai-startups-ma-timeline/ [1]

Reason 1. Analytic and AI strategy is too important for a company not to have someone with board level experience in this area.

Whether it is to ask critical questions about the single AI acquisition that 431 companies did between 2010 and 2019 or to ask critical questions about a company’s own analytic and AI efforts, a board member who has experience overseeing deployed analytic and AI applications is important.

It is important to note here the difference between someone who has experience in the entire life cycle of analytics from project start to deployment versus someone who only has experience developing analytic models. This is because most analytic projects don’t get deployed and don’t bring the expected value when deployed.

I taught a course at the University of Chicago’s Booth School of Business for three years called the Strategy and Practice of Analytics. One of my favorite case studies was HP’s acquisition of the AI company Autonomy in 2011 for $11.1 billion. As the New York Times reported a year later:

“Last week, H.P. stunned investors still reeling from more than a year of management upheavals, corporate blunders and disappointing earnings when it said it was writing down $8.8 billion of its acquisition of Autonomy, in effect admitting that the company was worth an astonishing 79 percent less than H.P. had paid for it [2].”

Reason 2. Board members with experience developing, deploying and operating complex analytic projects have critical experience using technology to innovate, not just operate.

All boards understand the importance of having board members that understand how to manage operations and risk to align with corporate strategy and drive financial performance. On the other hand, these days using technological innovation to align with corporate strategy and drive financial performance is also important. Successful senior leaders in analytics and AI generally have a deep understanding of technology innovation and how to use it to drive financial performance. See Figure 2.

In contrast, it is common for many CIOs to spend their time as CIOs managing IT operations, reducing IT costs, and using IT to quantify and control risks, rather than using IT to drive technology innovation and drive financial performance.

When successful, good analytic leaders find ways to use data, analytics and AI to change a company, not just run it. Having this perspective on a board is very valuable, as is experience with analytic projects that leverage continuous improvement and analytic innovation.

Figure 2. Traditional CIOs who serve on boards can help boards understand how to use IT to improve the efficiency of operations and reduce risk. Senior technical leaders who understand data and analytics can help boards understand how technical innovation can align with corporate strategy and improve financial performance.

Reason 3. An analytic perspective for a board member helps with evaluating cybersecurity and digital transformation, both critical topics for many boards.

A 2017 Deloitte study found that:

“high-performing S&P 500 companies were more likely (31 percent) to have a tech-savvy board director than other companies (17 percent). The study also found that less than 10 percent of S&P 500 companies had a technology subcommittee and less than 5 percent had appointed a technologist to newly opened board seats. … Historically, board interactions with technology topics often focused on operational performance or cyber risk. The Deloitte study found that 48 percent of board technology conversations centered on cyber risk and privacy topics, while less than a third (32 percent) were concerned with technology-enabled digital transformation.” Source: Khalid Kark et al, Technology and the boardroom: A CIO’s guide to engaging the board (emphasis added) [3].

A senior analytics executive with experience supporting cybersecurity is a double win for a board. Even without this experience, behavioral analytics plays an important role in the cybersecurity for a large enterprise, and senior analytics executives almost always have experience in behavioral analytics.

The remote work caused by the COVID-19 pandemic has accelerated the importance of board level understanding of digital transformation. As a Wall Street Journal article from October 2020 states it:

“If you didn’t have a digital strategy, you do now or you don’t survive,” said Guillermo Diaz Jr. , chief executive officer at software firm Kloudspot, and a former chief information officer at Cisco Systems Inc. “You have to have a digital strategy and digital culture, and a board that thinks that way,” he said. Source: Angus Loten, Many Corporate Boards Still Face Shortage of Tech Expertise, Wall Street Journal [4].

It is hard to image a digital strategy without an analytic strategy. Chapter 8 of my Developing an Analytic Strategy: A Primer [5] describes seven common strategy tools that can be easily adapted to develop an analytic or AI strategy, including SWOT, the Ansoff Matrix, the experience curve and blue ocean strategies.

References

[1] CBInsights, The Race For AI: Here Are The Tech Giants Rushing To Snap Up Artificial Intelligence Startups, CB Insights, September 17, 2019. Retrieved from: https://www.cbinsights.com/research/top-acquirers-ai-startups-ma-timeline/.

[2] James B. Stewart, From H.P., a Blunder That Seems to Beat All, New York Times, Nov. 30, 2012. Retrieved from: https://www.nytimes.com/2012/12/01/business/hps-autonomy-blunder-might-be-one-for-the-record-books.html

[3] Khalid Kark, Minu Puranik, Tonie Leatherberry, and Debbie McCormack, CIO Insider: Technology and the boardroom: A CIO’s guide to engaging the board, Deloitte Insights, February 2019. Retrieved from: https://www2.deloitte.com/us/en/insights/focus/cio-insider-business-insights/boards-technology-fluency-cio-guide.html

[4] Angus Loten, Many Corporate Boards Still Face Shortage of Tech Expertise: But more CIOs are expected to earn a seat as the pandemic forces companies to lean on digital, Wall Street Journal, Oct. 12, 2020. Retrieved from: https://www.wsj.com/articles/many-corporate-boards-still-face-shortage-of-tech-expertise-11602537966

Continuous Improvement, Innovation and Disruption in AI

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Figure 1. Some of the differences between continuous improvement and innovation in analytics and AI.

It’s important for managers and leaders in analytics and AI to know the difference between continuous improvement, innovation and disruption in their field and to know how it applies to their projects and to their organization.

Continuous improvement is about encouraging, capturing and using individual knowledge about current processes and how to improve them from the people actively involved. Good examples of continuous improvement applied to complex engineering problems includes: W. Edwards Deming improving the quality of automobile manufacturing in Japan (the Kaizen Process); Bill Smith at Motorola trying to reduce the defects in the manufacturing of computer chips (leading to Six Sigma); and Admiral Hyman G. Rickover improving the safety of nuclear reactors on nuclear submarines [1].

Innovation is about developing new processes, products, methodologies, and technologies. It is usually done by those not directly involved in the day to day to work. Often there is a challenge transitioning innovations from the lab to a product or into a deployed process in production. These days, it’s claimed more than it’s produced. True innovations are usually recognized by experts relatively quickly, but by others over a longer period of time due to clutter in the markets [2, Chapter 3]. Also innovative technology can take a while for companies to deploy for a variety of reasons, including the agility of the company, the lock-in of current vendors, and the sometimes complex motivations and incentives of decision makers [2, Chapter 4].

Disruption occurs when a new technology fundamentally alters the price-benefit structure in an industry or market segment [3]. An example from AI is the use of deep learning software frameworks, such as TensorFlow and PyTorch, along with transfer learning from large pre-trained models, such as ImageNet, Inception, and ResNet, which allows individual scientists using modest computational resources to build deep learning models, without the large computational infrastructure and very large datasets that would be required otherwise.

Some differences

Continuous improvement is about improving something that exists. Innovation is about creating something that doesn’t exist. Innovation can take months or years, while continuous improvement can often be done in days or weeks. See Figure 1 for some more differences.

Best practices in analytics

Best practices for continuous improvements in analytics include:

  • A champion-challenger methodology, where you use a formal methodology to frequently new models and compare them using agreed upon metrics to the current model in production.
  • Weekly model reviews, where all the stakeholders meet each week to review the model’s performance, what additional data can be added to the model, the actions associated with the model, and business value generated from the actions and discuss how these can be improved. Weekly model reviews are part of the Model Deployment Review Framework that I cover in my upcoming book, the Strategy and Practice of Analytics.
  • Model deployment frameworks. A third best practice is to use a model deployment framework so that models can be deployed quickly into production. This might involve PMML or PFA , a DevOps approach to model deployment, or one of the providers of specialized software in this area.

Best practices for supporting the development of innovation in analytics include:

  • Setting up a structure to develop innovative projects. This can be a separate group (a R&D lab, a Future Groups, or an Innovation Center) or supporting regular time (such as Google’s 20% time) devoted to innovation. For example, in our Center we set aside 1-3 days per month for the entire team to work on selected projects that have been proposed.
  • Setting a process to select and support meritorious projects. Innovation takes times and requires support. It cannot be done in a simple brain-storming session.
  • Setting up and fine tuning a process to move useful innovations from the lab into practice. Finally, it is all too common for innovation in large organization never to leave the lab. A number of large organizations have over time developed good processes for transitioning innovation to create new products, services and processes. IBM is quite good at this [3]; a recent example is the investment they put into bringing holomorphic encryption into practice, which took sustained investment over more than a decade. Overtime, this will have an important impact on analytics and AI.

The Power of Simple Process Improvements

It is worth emphasizing the tremendous power of simple process improvements, such as transitioning from letting the data scientists that build models decide when and how to deploy them to weekly model reviews involving all stakeholders, including the business owners. In these weekly meetings, the model is reviewed end-to-end, including the data available, the performance of new models (the challenger in the champion-challenger methodology), and discussing developing potentially new actions associated with the model (see the post on Scores, Actions and Measures (SAM)).

Here is another simple example of the power of continuous improvement that is not related to analytics. For many years, I took notes using emacs in outline mode. Recently, after reading about the Zettelkasten method, I switched to using emacs in org mode and adopted a few of the ideas used in digital Zettelkasten. This small change has made it much easier for me to find technical information that I need. You can find a nice introduction to Zettelkasten in lesswrong.

References

[1] Dave Oliver, Against the Tide: Rickover’s Leadership Principles and the Rise of the Nuclear Navy. Naval Institute Press, 2014.

[2] Robert L. Grossman, The structure of digital computing: from mainframes to big data, Open Data Press, 2012. See Chapter 3, Technical Innovation vs. Market Clutter and Chapter 4, Technology Adoption Cycles. Also available from Amazon.

[3] Clayton M. Christensen, The innovator’s dilemma: when new technologies cause great firms to fail, Harvard Business Review Press, 2013.

[4] National Research Council. 1995. Research Restructuring and Assessment: Can We Apply the Corporate Experience to Government Agencies?. Washington, DC: The National Academies Press. https://doi.org/10.17226/9205. See https://www.nap.edu/read/9205/chapter/6.