AI Understanding is Now Non-Negotiable
Most companies pouring money into AI don’t see the return they expected. That’s not surprising. The reality is, big bets in AI often fail because leaders treat technology as the main variable. But it isn’t.
What matters is using AI in ways that fit how your people actually work and make decisions. The difference between the successes and the failures has less to do with the AI itself, almost everyone has access to similar underlying technologies now, and more to do with how leaders build routines around it, how much open experimentation is allowed, the data they use, and the quality of that data, and how honest teams are about what works and what doesn’t.
When we built our own Strategy & Consulting AI, the system failed more times than it worked. We started over, redid it from scratch, sometimes painfully. Every cycle gave us something to measure, a point of reference we could use to get better.
Over time, we saw the benefits not in “quick wins,” but in gradual, tangible improvements: higher engagement from our team (including me personally using our own tools for certain use cases) and clients leaning into the tools because they found them valuable.
Executives tend to focus on grand visions. That’s important. But, in practice, the organizations that do well in this next phase of AI are those where frontline teams have room to tweak what’s not working, where “failure” is just another word for feedback.
As you very well know, the most important actions are often unglamorous: giving teams clear priorities, narrowing the scope until something does work, and resisting the urge to chase every new tool. Just as in the Chinese startup arena, where relentless testing and local adjustment beat the “one big bet” mindset, corporate AI is about learning fast, iterating faster, and keeping the human element at the center.
For executives and CEOs, AI literacy is table stakes now. Boards are hiring for it. A few days ago, I had a conversation with Byron Loflin, who advises boards globally as Nasdaq’s Head of Board Advisory. He said the expectations for the CEO role are evolving fast. Today, boards expect leaders not just to know what AI can do, but to understand how it works, what questions to ask, what risks to watch out for, and how to translate potential benefits into realized benefits (what we call banked benefits).
Byron also emphasized how competitive and transformational pressure is moving so fast that digital and AI literacy are now table stakes for senior leaders. Boards want leaders who can absorb new ideas, apply them effectively, and put together teams who can do the same. He’s seen boards pick outsiders over great, proven internal candidates simply because the outsider had stronger digital skills and could challenge the status quo faster. The board saw a lower risk of selecting an outside, unproven candidate than of selecting an internal, proven candidate who is not as AI-literate as the outside counterpart.
To step into those top roles, you have to be able to self-assess, stay curious about tech shifts, ask the right questions about how AI might reshape your industry, and take quick and deliberate action.
Digital and technical fluency is no longer a nice-to-have, but the baseline. More and more, it’s not the people with the fanciest resumes who are picked for top jobs; it’s those who are AI-literate and have some relevant experience as it relates to AI adoption.
So, what do you do next?
Consider the following:
Take inventory of where AI is adding value, however small, in your business today.
Invest a bit more there. Kill off the parts that are not delivering.
Once every few weeks, bring the “failed” ideas back to the table for reconsideration. Some of them may suddenly be viable as the technology changes.
Focus on building habits, not launching massive programs.
Allow your teams to experiment, own their outcomes, and revise as needed.
Ultimately, what separates sustainable success from what people call hype is consistency, diligence, good critical thinking skills we teach in depth on StrategyTraining.com, combined with the willingness to run pilots, keep learning, and keep tweaking. The incentive is not to chase AI for its own sake, of course, but to get good at asking:
How does this help our people work better today?
How do we redesign the business given the current capabilities of technology?
How does AI contribute to the success of particular projects where we use AI?
Leaders who do this set themselves apart. And no matter what the next wave of technology brings, they will be more prepared to take advantage of opportunities and to handle challenges.