Line-of-business leaders are in a tough spot. Their managers are demanding fast AI adoption, presumably in service of efficiencies. But it’s too soon. Best practices haven’t emerged yet. They might not even be feasible yet: the technology is changing too fast.
Still, mindful leaders are encouraging their teams to embrace AI. Experiments proliferate. Overnight, teams find themselves grappling with dozens of “agents” that cover similar ground. There’s more competition than collaboration among team members.
There’s little coherence. Most of these efforts don’t solve strategic business or customer problems. At best, they’re compelling — yet ad-hoc — proofs-of-concept.
Worse, they’re emerging in a context of generalized anxiety. Team members are trapped in a “damned if they do/damned if they don’t” conundrum: Their jobs are at risk if they don’t adopt AI, but media constantly reminds them AI might replace their jobs.
The anxiety is understandable, but also a bit sad. There’s so much potential. AI is by far the most exciting tech development I’ve experienced in my three-decade career.
Even so, many people have bad expectations of what the technology can deliver now. These things take time. It took several years — and expensive mistakes — for businesses to learn where and how the web could be used most effectively.
It’s too early to expect efficiencies from AI. The goal right now should be learning, not optimizing.
The best we can do is run directed experiments: fast, small, iterative projects that explicitly aim to move the business forward while developing essential new skills. Not just bottom-up, but building toward a directed vision.
How do you define that vision? You consider the big picture. What’s the organization’s strategy? How does the business unit serve that strategy? What are its key information flows? Where are the bottlenecks? Which can be best addressed using AI?
Throwing agents against the wall won’t answer these questions. Real progress requires top-down direction and visibility: understanding the big picture well enough to determine how AI might best unlock new possibilities. But it also requires experimenting to learn how the technology can actually work within your particular context.
These things aren’t in tension. A mindful balance is called for.
Ultimately, it’s an architectural problem. The organizations that benefit most from AI won’t be the ones that burn the most cycles and tokens. Instead, it’ll be those who understand the big picture well enough to drive advantage by architecting intelligence.
This post first appeared in my newsletter.