AI on Board? How Directors and CEOs Can Leverage Real-Time Data and AI to Power Business Transformation

“Some successful companies fail because they over-invest in what they are good at today and under-invest in what they should be good at tomorrow,” says Venkat Venkatraman, David J. McGrath Jr. Professor in Information Systems at the Questrom School of Business, Boston University. He is the co-author along with Vijay Govindarajan from Dartmouth College’s Tuck School of Business, of Fusion Strategy: How Real-Time Data and AI Will Power the Industrial Future.

Venkatraman studies how established companies recognize and respond to digital technologies. The first wave of transformation affected asset-light industries such as music, media and entertainment. As traditional analog products such as photographs, movies, music and financial transactions became apps inside a smartphone, some incumbents clung to legacy business practices and had no counter to the digital threat. Many did not survive the sector shift from product to application. Think Kodak to Google Pixel, Capitol Records to Apple Music, Sony Pictures to Netflix, credit cards to Apple Pay.

We now stand at the brink of a new frontier defined by the fusion of industrial engineering and information sciences. It will lead to a physical and digital transformation in asset-heavy, information-rich sectors including automotive, logistics, healthcare and agriculture. As asset-light sectors moved from analog to digital in the 2010s, in this decade asset-heavy sectors will shift from purely physical to physical and digital and from linear to exponential.

To avoid the fate many suffered during the 2010s, incumbents must adapt to the digital future. Technology transformation will not be enough to remain relevant. To navigate the shift successfully, businesses must shift their business models from economies of scale (decreasing the unit cost of production) to economies of expertise (generative artificial intelligence [AI] capabilities with realtime data), recalibrate strategic scorecards using new metrics, and rethink the organization and talent strategy.

While different sectors and industries face different horizons of impact, they will all inevitably reach what Venkatraman calls “the fusion frontier.” Business leaders must learn to read the digital signals to know when it is time to improve the current business model, and when it is time for reinvention.

Nvidia CEO Jensen Huang recognized a coming sea change in the computing industry in 2012 when he saw researchers use graphics processing units (GPUs) to perform tasks with human-like accuracy. In preparation for the shift from general-purpose computing, where central processing units (CPUs) were the primary engines, to accelerated computing, which requires GPUs, Nvidia transformed its business model to focus on data center GPUs and AI processors and cultivated relationships with server vendors and cloud service providers. (CPUs are designed for general-purpose processing tasks, while GPUs specialize in handling parallel processing, making them ideal for graphics and large-scale computations.)

When OpenAI’s ChatGPT premiered in 2022, revealing the enormous potential of generative AI applications and kicking off massive investments in AI capabilities, Nvidia was ready to meet the demand. The years spent building a comprehensive and integrated stack of chips, systems, software and services for accelerated computing paid off. Having recognized the AI trend early, Nvidia had effectively tailored its business model to become a one-stop shop for AI development.

By contrast, Intel, which had long been the number one chip maker in the U.S., largely missed out on the AI boom and is struggling to stay relevant. Intel is now the worst-performing tech stock in the S&P 500 while Nvidia is the second-best performer in the index.

As the world continues to digitize, differentiation will be increasingly based on generative AI and access to the real-time data needed to train it. Companies must elevate data and AI to their strategic core and be proactive about data or risk being left behind. Datagraphs — real-time data on everything from individuals to industrials — will be both digital disrupters and digital advantages as they become the integrative connective tissue that drives value creation.

Timing is everything; companies that have the data first will be at a competitive advantage. Unique information a company has about its customers or industry, or its signature datagraph, can be propelled by AI and used to improve products or services. Examples of companies that have already unlocked the value of their unique customer knowledge include Facebook (social datagraphs), Google (search datagraphs), Spotify (music datagraphs), Netflix (entertainment datagraphs) and Amazon (purchase datagraphs).

Companies that lack access to diverse data at scale can purchase datagraphs or consider strategic partnerships that give the parties the ability to navigate multiple ecosystems. This can be done without full-scale reinvention of either’s business architecture, like the alliance between Waymo, an autonomous driving technology company, and Jaguar Land Rover (JLR). The two companies agreed to a long-term partnership for a Waymo fleet of up to 20,000 roboticized I-Pace SUVs.

Waymo wanted to add luxury electric cars to its expanding autonomous ride-sharing fleet. As a car manufacturer, JLR knew how to manufacture the all-electric Jaguar I-Pace SUV, which met Waymo’s requirements.

JLR was interested in developing self-driving vehicles. It did not, however, have access to the data about how cars are driven which it needed to train generative AI. Waymo is developing “The World’s Most Experienced Driver” by training generative AI using safety datagraphs developed from billions of real-world and simulated miles driven.

By partnering with a premier self-driving player, JLR gained access to Waymo’s technology and datagraphs, saved on R&D costs and increased brand exposure for the I-Pace. In return, Waymo could offer its ride-share customers a luxury electric vehicle experience while maintaining its singular focus on improving self-driving technology.

Increased reliance on partnerships, the value chain, technology and real-time data is not without risk, and the risk-averse are not likely to be first movers. Venkatraman’s advice to boards is to provide oversight of the management and mitigation of these risks, including cyber security risks, anti-trust regulations and data governance and sovereignty issues, the same way they do other business risks.

Strategic questions all CEOs and corporate boards should ask themselves include:

• What are potential existential threats to the business?
• What technology will disrupt what the business does today and make the company obsolete? Look at second- and third-order consequences.
• What is the company’s response to a new architecture that turns its business model into a commodity?
• Where does the business want to play in the technology stack?
• How can the company access diverse real-time data at scale?
• When should the company double down on the current business model and when should it cut toward the future?
• What is the organization’s tolerance for risk?
• What unique information does the company have about its customers that can be leveraged? To whom does this signature datagraph have value? How can it be propelled with AI?
• How can the company change the landscape through partnerships with historic competitors or by taking an ecosystem approach?
• How will the company respond over the next three to five years when data and AI become pervasive in its industry and new competitors enter the market or existing competitors are squeezed out?

This article is part of the 2024 Directors Dialogue Digest series, Changing Leadership for a Changing World. Join the Institute’s mailing list for early access to valuable research, industry updates and more.