A Battle of Wits in the Digital Era

Leveraging data, analytics and AI can remove traditional constraints on business growth.

It is game seven of the World Series: bottom of the ninth; winning run on third. The batter due up is a .280 hitter, but the manager substitutes Skinny Joe, sporting a .279 average, as a pinch hitter. The TV commentator wonders aloud, “Why bother?”

The pitcher throws a hanging curve, and Skinny Joe hits it into the gap to win the game. In the postgame interview, the manager explains: Even though his batting average is only .279, Skinny Joe bats .600 when the pitcher throws a hanging curve, and this pitcher’s curveball will hang 95 percent of the time when the humidity is high and his pitch count is over 90 pitches, both conditions prevailing during the key at-bat.

We can imagine artificial intelligence (AI) at work in this scenario. Armed with sufficient data, an algorithm can offer decision support by analyzing a myriad of conditions and circumstances far beyond our own ability to sift through the data.

This simple illustration shows how AI might improve our decision-making: Better game-time decisions lead to winning more games and enhancing fan engagement, which drives ticket sales.

There are only so many players, teams, games and stadiums before it becomes prohibitive or impossible to expand the production of baseball entertainment. And to stop here is to miss the point of Competing in the Age of AI, which delves into the characteristics of digital systems that make them so powerful.

In another illustration, author Karim Lakhani compares in a recent webinar the beginning of AI to the introduction of paved streets, citing Boston (a chaotic pattern) and Manhattan (an orderly grid). Boston used the new (at the time) “technology” of pavement to embed the pattern of the existing, already trodden cow paths, whereas Manhattan took the moment of technological transition to ask, in today’s language, “What would be a better operating system?”

And so it is with AI. It would be one thing to view AI as a way to improve the functioning of every system inside the silos where operations and data are organized, such as general ledgers, customer lists, distributors and marketing automation.

Lakhani and Marco Iansiti’s book goes much further. How can AI transform all business and operating models to fundamentally alter a company’s growth? And why would you want to do this? The accepted wisdom among business leaders is to stick with businesses they know, in industries they understand. But opportunities now available through algorithms and data flows are quickly erasing industry boundaries.

Back to the baseball analogy: An MLB team is not merely a manufacturer of baseball games. It is an entertainment company that creates a locus of activities around a happy pastime — the crowd, the concessions, the pageantry, the TV experience, the merchandise sales and more.

Major professional sports leagues have already made this conceptual leap from vertical focus (better baseball drives more ticket sales) to horizontal value creation (monetizing the ancillary values connected to the game). Baseball teams and other sports have already harnessed the power of data analytics. The additional leap for AI is found in understanding the power of digital systems and algorithms.

With the collection of massive data, initially collected into silos of “team and player stats,” “food sales,” and so on, and combined with data on location, affiliated activities, and electronic apps, we discover the keys to an exponential growth horizon:

• Scalability: Digital systems are highly scalable, with virtually no marginal cost to expansion.
• Scope: Digital systems can widen across any type of product line.
• Learning: Digital systems do not deteriorate with time but rather spur increasing insights and analytic power the larger they become.
• Networking Connectivity: Network value increases as nodes are added. After reaching critical mass, everyone uses a network because everyone uses it, similar to the phenomenon of Facebook and Instagram. This further reduces friction across products, scope and scale.

Iansiti and Lakhani encourage leaders to “rearchitect” their firms along lines that will create the mentality necessary to go from the traditional to the digital. To punctuate the importance of making the necessary conceptual leap, the chapter on rearchitecting the firm begins with a memorandum from Jeff Bezos to his development staff, establishing a development mandate:

“All teams will henceforth expose their data and functionality through service interfaces. … All service interfaces, without exception, must be designed from the ground up to be externalizable. That is to say, the team must plan and design to be able to expose the interface to developers in the outside world. No exceptions. Anyone who doesn’t do this will be fired …”

The directive above contrasts sharply with an approach that considers a firm’s systems to be proprietary, like its “secret sauce.” Such a policy would lead any leader to ask, “Who knows what this is going to connect to? Who knows how this is going to affect our proprietary ‘value proposition’?”

Competing in the Age of AI contains many excellent discussions of business models through history to put the current AI disruption into context. Using Amazon as a case study, the chapter on architecting sets the stage by recalling the great evolution from a handmade “fit and finish” craftsman model to the scale and efficiencies of standardization and specialization.

This book explores its subject using a wealth of case studies, among them Ant Financial, Tencent, Google, Amazon, Kodak and Nokia. For those who want to get up to speed on AI, its importance and its impact on planning, Competing is a must-read. Readers only mildly interested in AI will find the book offers a superb conceptual analysis of operating models, product strategy and overall business design.

The Ethical Challenge
We recommend reading the Age of Surveillance Capitalism, by Shoshana Zuboff, reviewed by us in the initial issue of In the Lead, in tandem with Competing.

Zuboff lays out the existential questions regarding the invasion of privacy and the massive assembly of billions of dossiers on every member of our population.

She asks, “Who knows? Who decides? Who decides who decides?”
We might be complacent because this surveillance is conducted by companies, but we are blind to the violation of our privacy rights.

Interestingly, Competing raises numerous ethical questions, and it raises these questions in the practical context of how to approach data. But in Lakhani and Iansiti’s world, it is presumed that the surveillance data exists, it is readily accessible, it is not by the individuals surveyed but by the companies that collect it, by right of conquest.

Some critics raise the concern of AI becoming so powerful that it develops its own agency, an out-of-control enemy, like the computer HAL in the movie 2001: A Space Odyssey or Skynet in the Terminator movies.¹

The New York Times asked several experts, “Is there something about AI that keeps you up at night?”

Roman Yampolskiy said, in part, “Everyone dying … and that’s not even the worst. You have suffering risks where AI learns how to make us immortal and then forever you have a very unhappy existence.”²

In our present phase, Competing lays out multiple ethical issues leaders face:

• Digital Amplification: Algorithms can be misused to tailor, optimize and amplify inaccurate and harmful information. Competing cites the example of Airbnb, where people with distinctly African American names were 16 percent less likely than those with European-sounding names to be accepted as guests. “With no organized effort to discriminate, the digital systems simply amplified the impact of the implicit … bias of homeowners.”³
• Algorithmic Bias: Both selection bias and labeling bias are virtually unavoidable. The algorithmic process itself is a form of collapsing an otherwise incomprehensible data set into a “representative” sample. Algorithmic “choices” of what is representative are subject to both myopia and malintent.
• Cybersecurity: “Every day Alibaba Cloud blocks 200 million brute force attacks, 20 million web hacking attacks, and 1,000 DDoS (distributed denial-of-service) attacks.”⁴ Competing asserts, “Leaders have a fundamental legal and ethical duty to protect the information obtain from customers, employees, and partners.”⁵
• Platform Control: As Mark Zuckerberg said during U.S. Senate hearings in 2018, “Across the board we have a responsibility to not just build tools, but to make sure that they are used for good.”⁶ But because the power of digital scale, scope and learning derives from the openness and connectedness of platforms, the potential for abuse is beyond managers’ imagination and eludes mitigation. Competing echoes the concerns raised by Zuboff in the Age of Surveillance Capitalism concerning the potential for violating privacy and the thorny constitutional protections of free speech. “For many content platforms that are open to anyone, the question of control and curation gets uncomfortably close to censorship. Executives and company stakeholders will increasingly face the issue of private actors governing public action, and few are equipped to deal with these questions or generate appropriate solutions.”⁷
• Fairness and Equity: As firms like Apple and Amazon expand and prosper, we can think of their platforms as ecosystems, where app creators or online retailers join in, giving the platform many characteristics of a public square. The robust health of these platforms becomes a collective good, not only available to all who enter the “square” but increasingly important and necessary to wide constituencies of consumers and vendors. The potential for excessive and abusive market power is great.

Iansiti and Lakhani write, “The leaders of modern firms cannot afford to ignore this generation of ethical challenges.”⁸ They compare key digital platform companies to “keystone species” in an ecosystem — those whose presence, behaviors and health create effects on the entire ecosystem, beyond the specific business “space” they occupy.

Competing endorses an approach proposed by Jack Balkin and Jonathan Zittrain called information fiduciary.⁹ Companies are bound to act as “information fiduciaries, an innovative approach in the face of the new realities stemming from the incidental generation of data by social media and information technologies:

“’There is an opportunity for a new, grand bargain organized around the idea of fiduciary responsibility. Companies… would agree to a set of fair information practices, including security and privacy guarantees… promise not to leverage personal data to unfairly discriminate against or abuse the trust of end user …’”¹⁰

In conclusion, the authors urge a broad mandate upon business leaders: “As digital firms increasingly shape our global economy, their management will be held accountable to a different standard. Despite competing as individual businesses, each will benefit or suffer from collective accomplishments (emphasis added) such as improving privacy, removing news bias and manipulation, or even creating effective systems to encourage and retain displaced labor.”¹¹

---

1. Critics of AI systems and their potential dangers include Stephen Hawking, “The Future of Artificial Intelligence,” (TED Talk, 2014); Nick Bostrom, Superintelligence: Paths, Dangers, Strategies (Oxford University Press, 2014); Max Tegmark, Life 3.0: Being Human in the Age of Artificial Intelligence (Knopf, 2017) and Elon Musk, “AI is More Dangerous than Nuclear Weapons” (New York Times, 2017). 
2. New York Times, “8 Big Questions About A.I.”, June 1, 2023 
3. Page 180 
4. Page 184 
5. Page 186 
6. Page 189 
7. Page 193 
8. Page 196 
9. Page 197. See A Grand Bargain to Make Tech Companies Trustworthy by Jack Balkin and Jonathan Zittrain, The Atlantic, October 3, 2016. Available at www.theatlantic.com/technology/archive/2016/10/information-fiduciary/502346/ 
10. Page 198 
11. Page 227

Categories: Business, Science and Technology