Amazon, Microsoft, Alphabet and Meta are on track collectively to invest more than $300 billion in AI infrastructure and R&D over just a few years. This wall of cash sends a clear message: Big Tech believes its big bet on artificial intelligence will pay off. But for that to happen, it must convince the customers it needs the most: the boards of the Global 2000 — the world’s biggest listed companies.
When I last interviewed this group in 2023, AI barely made the agenda. Fast-forward two years and much has changed. Intelligent boards are getting smart about artificial intelligence.
Leaders are under increasing pressure from shareholders and investors not only to articulate their AI strategy but to deploy what at first appears to be radically productivity-enhancing technologies. The shift from boards being passive observers to actively engaging with AI has been “notable”, according to Jenni Hibbert, global managing partner at executive search firm Heidrick & Struggles.
“It’s very clear that many companies have evolved from a period of either observing or piloting early forms of AI adoption, to now becoming laser-focused on aligning AI tools and technologies with their core business goals.”
With few board members having a background in engineering let alone machine learning, for many the first step has been to gauge the AI literacy of the board. In the past year we have educated CEOs trying to master AI jargon ahead of shareholder presentations; managers looking to learn from case studies of successful AI deployment; family offices wanting to assess their AI investments; and executives eager to get hands-on with large language models (LLMs) to understand how they are built and how data is trained. But above and beyond, boards want to understand the risks.
Risk is not something Big Tech likes to talk about, but managing risk is arguably the No 1 priority of the board. And the risks when it comes to AI are not just numerous, but only just beginning to be understood. They span the potentially unsolvable inaccuracy of LLMs (hallucinations); the damaging implications of copyright and IP infringement; the negative impact on employees’ wellbeing and retention, with recent research showing that individuals working alongside AI exhibit lower levels of motivation and satisfaction; the erosion of critical-thinking skills; increased loneliness of users, even those using chatbots for general purpose tasks; to AI downright lying to achieve its goals.
Some AI experts even warn that we have all been enthralled by a “stochastic parrot”, a highly deceptive mimicking machine, incapable of producing original content let alone thought. It’s no wonder intelligent leaders aren’t putting the parrots in charge yet.
One person the board is increasingly turning to, to guide and drive AI strategy, is the AI executive. Once a new-fangled role, the AI executive — such as a head of AI — has grown in maturity. Research by Heidrick in 2024 showed that 31 per cent of AI executives now report directly to the CEO, nearly doubling from 17 per cent in 2023.
The head of AI’s primary skillset is technical expertise, typically coming from a background in advanced analytics or machine learning. However, they also need to be able to communicate, manage cross-functional teams and decode how AI is likely to affect every facet of a company’s business strategy.
One of the people holding this rare combination of skills is Greg Ulrich, chief AI and data officer at MasterCard. Ulrich is responsible for driving AI strategy across the organisation and leads its AI Center of Excellence. Its objectives include model risk assessments, bias testing, developing secure data pipelines and engaging with global regulators.
If you are wondering how effective initiatives such as this can be, consider this: Mastercard was once a physical credit card business that has transformed into a global digital payments company, taking it from a market capitalisation of $30 billion in 2010 to more than $500 billion today. It might be on to something, and it is putting education and training at the heart of its AI strategy.
In some cases those education opportunities are offered to everyone in an organisation. Paul Hollands, chief data and analytics officer at Axa, pioneered the insurance industry’s first generative AI apprenticeship and launched Axa’s data and AI academy in 2024, providing learning opportunities to all employees in the UK. During the week it launched, 4,700 people registered. It’s an investment in human capital, a belief in human potential even, which flies in the face of the zeal with which many AI leaders discuss the potential of their technologies to automate the workforce.
Aside from educating boards on the risks of AI, training the workforce, and reading the tea leaves about what changes it will bring, the head of AI has another essential job: to demonstrate that AI does, indeed, have value — and how. A recent survey by IBM of more than 2,000 global CEOs revealed that only 25 per cent of AI initiatives had delivered their expected results and only 16 per cent had scaled across the business in the past three years. Until the technology becomes more mature, smart boards are right to move cautiously.
