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CEOs can’t know everything about gen AI. But understanding these six areas is fundamental.

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CEOs have questions about generative AI ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ 
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FRESH TAKES ON BIG IDEAS
A drawing of Alex Singla



ON LEADING WITH GEN AI
The six questions CEOs ask about gen AI


By Alex Singla



When it comes to generative AI (gen AI), many CEOs are learning how to manage and make the most of this exciting technology. I’ve met with scores of them, and they tend to ask versions of these six questions:

What are my company-specific opportunities?
 
CEOs need to start from a business-back perspective: What are my biggest business opportunities? How can AI, gen AI, and data help create value and solve those business problems? It is absolutely not tech for tech’s sake. So what areas will have the greatest impact on your P&L, employee engagement, and customer engagement and can be implemented at scale? One key: value comes not by deploying sporadic use cases but by end-to-end transformations of the most promising business domains.
 
How do we organize and govern gen AI?
 
Most organizations have data splattered everywhere in the company, and their technology is not consistent across the organization. So you have to think about how do you organize your data, what’s your data architecture, and what’s your road map? Which use cases are you going to work on first? Governing that process and organizing around it is not trivial. Finally, since you want to have reusability of data and code, you need to think about prioritization and organization from a technical perspective as well. It’s a combination of business priorities, technical feasibility, and speed and cost of execution.
 
Which player or players should we partner with?
 
It’s a complex ecosystem that needs to be managed carefully. You’ve got cloud providers, data providers, and large language model providers. You have application tools that sit on top of all that. You need all of those to be successful, and most of it you don’t own. Managing that ecosystem is complicated and important. To maintain your leverage, you don’t want to walk through too many one-way doors where you cannot change your mind. The winners today might not be the winners tomorrow. This doesn’t mean you should be constantly flipping companies. You need to hold them honest.

“Having a well-structured cloud platform, data architecture, and data governance can allow companies to scale solutions more cost efficiently.”

How do we balance risk and value creation?

The risks of AI and gen AI have become an everyday conversation for boards. There are all kinds of risks, from hallucinations and cyber risk to risks about regulation and intellectual property. Despite the need to move fast, companies should edge their way into accepting and managing their institutions’ risk tolerance. What I often say is, don’t start on the most complicated opportunities on day one. Those might create a ton of value, but they also might create a ton of risk. A cross-functional team can help cover specific risks by establishing ethical principles and guidelines for gen AI use and continually monitoring gen AI systems to address risk dynamically.

What are the talent and tech stack implications?

To do gen AI at scale and cost efficiently, the technology stack matters. Having a well-structured cloud platform, data architecture, and data governance can allow companies to scale solutions more cost efficiently. While this does not have to be perfect to get moving, over time you want to have it in order. For instance, if it takes forever (and is expensive) to pull your data together because it’s in 15 different spots, you’re never going to be able to do a real-time solution with real-time data feeds. AI models don’t live in a vacuum, and it takes top-down focus, the right tech and people capabilities, proper data access, modular architecture, and effective change management to have hundreds of AI-driven solutions work together continuously to create great customer and employee experiences, lower unit costs, and allow the organization to move faster than ever. It is hard work. This also has implications for current and future talent. You’ll have to adapt the way you think about recruiting. What are the requirements for your talent of the future?

How do we get going and learn fast?

This is a learning journey. No one can assert that they know every implication of gen AI from a business, IT, people, risk, and financial perspective. Therefore, you need to get going in a couple of areas to learn. You want to learn what’s required for your company to get change management and adoption to work. It’s easy to do things in a lab in a one-off, but it’s very hard to take a solution and scale it to 100 offices around the world with 10,000 people. Those are two very different things. So you need to get going and you need to start learning.

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ABOUT THIS AUTHOR

Alex Singla is the global leader of QuantumBlack, AI by McKinsey, and a senior partner in McKinsey’s Chicago office.

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