Before Giving AI More Authority, Decide What It Can Decide

An AI agent performs well during a pilot. It analyzes information faster. Its recommendations are useful. Employees begin to trust it. The business can point to measurable time savings.

The next move can seem obvious: Give it more authority.

That is where the harder management problem begins.

As AI moves from assisting people to recommending actions, making decisions, and executing them, companies are transferring more than work. They are transferring decision rights. Recent organizational research makes this increasingly explicit, examining how firm control and decision rights change as AI gains greater autonomy and independent judgment.

So, the question for executives is, "What are we giving the AI permission to decide?"

The Productivity Numbers Hide A Bigger Question

McKinsey's 2026 global survey found that 80% of respondents said AI had improved their individual productivity. Yet only 37% attributed at least some EBIT impact to AI, and only about 6% qualified as AI high performers under McKinsey's definition ( McKinsey ).

Those findings do not prove that AI autonomy causes the gap. They reveal something more basic: Local productivity and enterprise value are different outcomes.

An AI system can make a task dramatically faster without improving the performance of the larger business system.

That matters because executives can look at a successful pilot and conclude that the next logical move is wider adoption or greater autonomy.

But sometimes the organization is scaling the wrong thing.

Imagine that an AI deployment delivers impressive task-level productivity, but revenue, cycle time, quality, or another enterprise outcome barely moves.

It is tempting to conclude that the model needs improvement, employees need more training, or the AI needs better instructions.

But several very different problems could create the same result.

The first is a preference problem. The people with the authority and responsibility to make the decision have not resolved what the organization wants when important priorities conflict.

The second is a representation problem. The trade-off has been resolved, but that judgment did not carry through adequately in the goals, constraints, metrics, uncertainty, or escalation rules available to the AI.

The third is a flow problem. The AI is performing correctly, but it is improving a part of the workflow that does not constrain overall performance.

The fourth is a feedback problem. Individual decisions may seem reasonable, but their effects can accumulate or interact in ways the organization cannot see or correct quickly enough.