Irving Wladawsky-Berger

Honest Conversations - The Key to a Winning Transformational Strategy

“Nearly every organization - whether private, social, or governmental - is grappling with huge strategic challenges, often with a need to reimagine its very purpose, identity, strategy, business model, and structure,” writes Harvard emeritus professor Michael Beer in his recently published book Fit to Compete.  “Most of these efforts to transform will fail.  And in most cases, they miss the mark not because the new strategy is flawed, but because the organization can’t carry it out.”

For over three decades, professor Beer and his various collaborators have been analyzing the reasons why so many organizational transformations fail.  In general, the common root cause has been the failure to align the organization’s culture and management processes with the strategic directions they’re after.  The book argues that two interrelated reasons explain these failures.

First, “the whole system of organizing, managing, and leading has to be transformed if organizational behaviors and underlying mindsets are to be changed.”  A transformational strategy that aims to take advantage of new technology and market innovations must involve the whole organization.  But, we often forget that such transformations are truly disruptive, not only to the marketplace but also to your own organization.  You cannot transform a large complex organization through business-as-usual objectives, - e.g., growing revenue and profit, cutting expenses, improving quality, developing better products and services.

Any major company-wide initiative must have the strong and visible support of the CEO and other top executives in the company.  The initiative must be carefully nurtured and protected in its early phases until it’s strong enough to stand on its own.  In any organization, there’s a natural competition for resources between new ventures and the existing lines of business.  Human nature being what it is, managers in the existing units will often feel that any new investments are best given to them to grow their businesses, rather than invested in a new, unproven area. This is one of the many challenges that leaders of the new initiative must skillfully navigate, otherwise the inevitable cultural and political – that is, human – issues will slow them down and potentially kill the new venture.

Can Democracy and Free Markets Survive in the Coming Age of AI?

Can technology plan economies and destroy democracy? is the provocative title of an essay in the end-of-2019 issue of The Economist.  “The success of market- and semi-market-based economies… has made the notion of a planned economy seem like a thing of the past.”  But, given all the data that can now be gathered by billions of smartphones and 10s of billions of sensors, - with many more to come, - could our increasingly intelligent and powerful AI systems “replace the autonomous choices on which the market is based?… And if technology can outperform the invisible hand in the economy, might it be able to do the same at the ballot box when it comes to politics?” 

As we’ve already seen, AI applications “can provide not just data on what people want and will tolerate, but also the means to manipulate those desires.  When such means are available to actors within or outside a state the struggle to gain, or for that matter retain, a democracy which reflects a genuine popular will might become even harder than it is.”  Could this all mean that free-market economics and liberal democracy will become obsolete over the coming century?

These questions were often raised and repeatedly tested throughout the 20th century, as various governments attempted to redesign their economies and societies in accord with what were believed to be scientific laws.  But, most such schemes, especially those carried out by authoritarian states, ended up as complete failures.  And some went tragically awry, - like Stalin’s collectivization of agriculture in the Soviet Union and Mao’s Great Leap Forward in China, - both of which brought death and disruption to millions.

The State of AI Adoption - High Performers Show the Way

For the past few years, the McKinsey Global Institute has been conducting a yearly survey to assess the state of AI adoption.  Its 2017 survey of over 3,000 AI-aware executive found that outside the technology sector, AI adoption was at an early, often experimental stage.  Only 20% of respondents used any AI-related technology in a core part to their business.  A common theme throughout the report was that the same players who were leaders in the earlier waves of digitization and analytics were the early leaders in the AI wave.

The McKinsey 2018 survey garnered responses from over 2,000 participants from a wide range of company sizes across 10 industry sectors.  Overall, the 2018 survey found that while the business world had begun to adopt AI, few companies had in place the foundational building blocks that would help them to generate value from AI at scale.  30% were conducting AI pilots.  Nearly half, 47%, had embedded at least one AI capability in their standard business processes, but only 21% reported using AI across multiple business functions.  AI investments were still quite small, with almost 60% of respondents saying that less that one-tenth of their digital budgets went toward AI.

The most recent Global AI Survey was published in November of 2019.  McKinsey garnered responses from over 2,300 participants, nearly 80% of whose companies were using AI in some capacity.  Overall,  the survey showed that “Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce.”  AI is becoming more mainstream, with 58% of respondents using AI in at least one function, - a nearly 25% increase over the previous year; and 30% reported using AI across multiple business units, - a nearly 50% increase.

One of the survey’s major findings is that a small share of high performers, around 3% of companies using AI, are already achieving outsize business results.   High performing companies are much further along in scaling the use of AI in their organizations, with an average of 11 different use cases compared an average of 3 among the other companies.

Conceptualizing AI in Human Terms is Misleading and Potentially Harmful

“We speak of machines that think, learn, and infer.  The name of the discipline itself - artificial intelligence - practically dares us to compare our human modes of reasoning with the behavior of algorithms,” writes Oxford doctoral candidate David Watson in his recently published article The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence.  “Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous.  The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies.”

What do we mean by intelligence?  In 1994 the Wall Street Journal published a definition which reflected the consensus of 52 leading academic researchers in fields associated with intelligence: “Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience.  It is not merely book learning, a narrow academic skill, or test-taking smarts.  Rather, it reflects a broader and deeper capability for comprehending our surroundings - ‘catching on,’ ‘making sense’ of things, or ‘figuring out’ what to do.”  This is a very good definition of general intelligence, - the kind of intelligence that’s long been measured in IQ tests, and that, for the foreseeable future, only humans have.

On the other hand, specialized intelligence, - the ability to effectively address well-defined, specific goals in a given environment, - is the kind of task-oriented intelligence that’s part of many human jobs.  In the past several years, our increasingly smart machines have become quite proficient at handling a variety of such specialized intelligent tasks.  AI technologies are approaching or surpassing human levels of performance in vision, speech recognition, language translation, the early detection and diagnosis of various forms of cancer, and other capabilities that were once viewed as the exclusive domain of humans.