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.
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