When AI Speeds Everything Up… Except Decision-Making
AI makes production instantaneous. Decision-making, however, remains irreducibly human — and increasingly exposed.
Across most organizations, artificial intelligence now delivers on a clear promise: speed.
Analyses that once took weeks can be produced in a matter of hours. Deliverables multiply. Options abound.
And yet, a paradox is taking hold in many executive committees — and in partner meetings alike:
decisions are not accelerating. In some cases, they are becoming harder to make.
This is not a tooling problem.
It is a governance problem.
The illusion of speed
AI radically accelerates production. But producing faster does not mean deciding better — or deciding faster.
On the ground, many leaders observe a new form of friction:
more analyses, but less clarity,
more deliverables, but weaker alignment,
more options, but greater hesitation at the moment of commitment.
The reason is simple: AI has not eliminated uncertainty.
It has compressed it.
When analyses took time to produce, uncertainty was distributed over time and across teams. Iterations, reviews, and progressive trade-offs acted as shock absorbers.
When analyses are produced almost instantaneously, uncertainty resurfaces abruptly where it is most sensitive: at the moment of decision — and accountability.
In other words, AI has not removed risk.
It has concentrated it.
Why this has become a governance issue
This shift in risk is no longer merely a practitioner’s intuition. It is now visible at the highest levels of organizations.
According to a study by The Conference Board, more than 70% of S&P 500 companies now explicitly identify artificial intelligence as a material risk in their annual reports.
This figure deserves close attention.
It does not mean these companies fear AI as a technology.
It means AI is now understood as a factor that reshapes how decisions are constructed, justified, and ultimately owned over time.
In other words, the issue is no longer operational.
It is institutional.
The real shift in value: from answers to reasoning
Many organizations still approach AI primarily through the lens of output quality.
This is a mistake.
In high-stakes decisions, the problem is almost never producing a convincing analysis.
The real challenge is being able to:
explain the reasoning behind it,
defend its assumptions when they are challenged,
and stand by it over time, long after the initial delivery.
An impressive but opaque deliverable weakens trust.
An imperfect but explicit line of reasoning strengthens it.
Consultants experience this tension particularly acutely. Every deliverable is designed to be defended — sometimes long after the engagement ends — before committees that did not witness its genesis. Where a signature directly engages credibility, even minor grey areas become visible.
As AI accelerates production, value therefore shifts:
away from generating answers,
toward the ability to carry and govern a line of reasoning.
The dilemma AI imposes on decision-makers
AI creates a new dilemma for leaders, partners, and committee members alike:
it makes production easier,
but ownership and accountability more difficult.
The faster the pace, the higher responsibility travels.
And the more it concentrates in the hands of those who sign.
The question is no longer:
Can we produce this analysis?
But rather:
Are we prepared to sign it — and live with its implications?
Three questions leaders should now be asking
Organizations that truly benefit from AI no longer treat it as a simple productivity lever. They reframe it as a governance issue.
That reframing begins with three simple — and uncomfortable — questions:
Which assumptions embedded in our AI-assisted analyses are we explicitly willing to defend?
Where does accountability sit when these analyses are reused, adapted, or challenged over time?
What decision standards must apply before speed becomes a liability rather than an advantage?
These are not technical questions.
They are leadership questions.
An uncomfortable truth
AI does not turn decision-making into a mechanical process.
It reveals a long-standing reality: leadership has always involved making imperfect choices under constraint — and standing behind them.
What AI changes is not the nature of responsibility.
It is its visibility.
Accelerating analysis is relatively easy.
Governing reasoning is not.
When reasoning becomes faster to produce but harder to assume, the real breaking point no longer lies in the decision itself, but in what circulates before it: knowledge — what is reusable, what is defensible, and what should never travel without context.
