Dhiraj Adya is the COO of Tech Mahindra Global Chess League (GCL) and Head, Americas Advisory Relations at Tech Mahindra.
getty
One of the most honest conversations happening in enterprise technology right now isn’t really about which AI to deploy or how quickly to scale it. It’s really about what happens after deployment, when the system has generated a recommendation, the data looks compelling, the window to act is narrow and the leadership team is still working out how much confidence they need before committing. That question, deceptively simple on the surface, is turning out to be one of the more defining organizational challenges of the AI era.
The reason this matters so much is that organizations that figure out how to answer it well can pull ahead in ways that are increasingly difficult for others to close. That’s not because they have better technology but because they have built something harder to acquire and easier to underestimate.
This isn’t a story about gaps in technology. The AI systems being deployed across large enterprises today are genuinely capable, often remarkably so. The more interesting challenge is an organizational one: How do leaders build culture and decision-making processes that can keep pace with what these systems are now able to offer? And how can they do so in environments where waiting for complete certainty is itself a strategic choice—and not always the right one?
Why The Old Playbook Needs Updating
For much of modern business history, the decision-making sequence in large organizations was clear and well understood. Information came in, analysis happened, a view was formed and then a commitment was made with enough time between each step to allow deliberation. That sequence worked well enough when information moved at a pace that allowed for it and the cost of taking a bit more time was relatively contained.
What the organizations I work with are finding is that AI changes this dynamic in ways that require a genuinely different kind of leadership response. Recommendations are arriving faster than governance cycles were designed to evaluate them, and leaders developing a real advantage from AI aren’t necessarily those with the most sophisticated models. They tend to be those who’ve built the organizational confidence to act on what those models are telling them before every uncertainty has been resolved. That capability turns out to be harder to build than it sounds, and it has almost nothing to do with technology itself.
What Formula One Reveals About This
The clearest illustration I’ve come across of this challenge comes from somewhere most enterprise conversations don’t naturally go. In Formula One, a strategy call during a live race has to be made in seconds, with incomplete data, tires degrading in real time, competitors yet to show their hand and weather models still updating lap by lap.
The teams that win consistently aren’t necessarily the ones processing the most data. From my observations, they’re the ones that have built the clearest shared sense of how to act decisively when the picture is still forming. A call made three seconds too late in that environment isn’t a cautious decision; it could be a losing one.
From my view, enterprise leaders are navigating a recognizably similar dynamic, just at a different scale and pace. The question isn’t really whether to wait for certainty before acting. Its about what you build in place of certainty when the window won’t wait for it to arrive.
Confidence As An Organizational Capability
The distinction I keep coming back to in these conversations is between certainty and confidence because I think they get conflated in ways that create real problems for how organizations approach AI decisions. Certainty is a property of information. You either have enough of it or you don’t. Confidence is a property of judgment, and it reflects how clearly a leader understands what they’re trying to achieve, what they’re genuinely willing to risk and where the line sits between a decision that can be course-corrected quickly and one that really cannot.
What I’ve observed in the organizations developing this capability well is that it didn’t come from better tooling but from deliberate practice over time. Leaders make consequential calls under genuine uncertainty, review those calls honestly afterward and build a shared institutional understanding of how to act when the model has given you everything it has but the remaining gap has to be crossed by human judgment.
That institutional confidence, earned through experience and not something you can purchase through a platform, is where a meaningful and growing part of enterprise AI advantage actually lives.
What Organizations Can Start Doing Differently
For organizations to build confidence amid uncertainty, I recommend treating it as a genuine capability-building challenge rather than a technology-procurement one. Rethink how decisions get made and how quickly mistakes can be identified and corrected. In environments where AI generates recommendations faster than traditional review cycles were designed to handle, speed of recovery matters as much as quality of the original call.
Invest in the judgment of your people alongside the capability of your systems as well. The two genuinely need to develop together.
Finally, be honest about what you’re actually asking AI to do. Generating better analysis and changing the pace of consequential decisions are fundamentally different challenges requiring fundamentally different organizational responses.
The Window
In Formula One, the pit lane window opens and closes within a handful of laps, and the teams with genuine strategic advantage have built the confidence to act in that window long before it appears because once it does, there is no longer time to build it.
I believe the AI era is creating a similar dynamic across enterprise technology. It’s at a different pace but with the same underlying logic: The organizations compounding real advantage right now aren’t waiting for the picture to become complete before they move. They’ve built the judgment, the culture and the decision-making processes that allow them to act well before it does, and that capability is becoming one of the more durable competitive edges available.
The data will never be complete. The question worth sitting with is whether your organization has built the confidence to act on it anyway.
Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
