The AI Productivity Trap: Why Faster Executives Can Make Worse Decisions

By Keith Lawrence Miller, M.A.

Artificial intelligence is arriving in executive work at exactly the moment leaders are running out of attention.

Microsoft’s 2025 Work Trend Index found that 82% of leaders considered that year pivotal for rethinking strategy and operations, while 81% expected AI agents to become moderately or extensively integrated into their organizations within 12 to 18 months. The same research described a widening capacity gap: leaders want more productivity while workers report too little time and energy to keep absorbing the demand.

That sounds like a perfect case for AI. Give leaders faster research, faster analysis, faster drafting, faster scenario generation, faster communication, and a growing bench of digital agents. Increase throughput. Reduce friction. Compress work that used to take days into minutes.

Then comes the uncomfortable question: what if the executive becomes faster before the executive system becomes wiser?

That is the productivity trap now forming inside many organizations. AI can increase the amount of executive-grade material produced without increasing the quality of the decisions made from it. It can generate more alternatives without clarifying the objective. It can summarize more evidence without distinguishing which evidence deserves trust. It can produce polished recommendations without identifying who should own the consequences.

The result can look like augmentation while functioning like acceleration of the existing operating system—including every weakness already inside it.

The bottleneck is moving

For decades, much of knowledge work was constrained by production. Senior professionals needed time to gather information, compare documents, build financial models, write recommendations, create presentations, prepare communications, and synthesize competing points of view.

AI changes the economics of that work. A large share of first-pass cognitive production is becoming cheap, fast, and abundant.

That creates real productivity. A field study of 5,179 customer-support agents by Erik Brynjolfsson, Danielle Li, and Lindsey Raymond found that access to a generative-AI assistant increased productivity by about 14% on average. The gains were much larger among novice and lower-skilled workers and much smaller among the most experienced workers. The finding matters for executives because it demonstrates both the power and the unevenness of augmentation. The value of AI depends on the work, the person, the context, and the way expertise is incorporated into the system.

Once production becomes abundant, the bottleneck moves upstream.

The scarce resource becomes the ability to decide what deserves to be produced in the first place.

Executives still have to determine what problem the organization is actually solving, which tradeoffs are acceptable, which evidence is consequential, whose interests are affected, what level of uncertainty can be tolerated, and who has the authority to commit the organization.

Those are judgment questions. Producing ten answers does not resolve them. Sometimes it makes them harder.

More intelligence can create more executive noise

IBM’s 2025 global CEO study captured the gap between AI ambition and enterprise value. Among 2,000 CEOs surveyed, only 25% of AI initiatives had delivered the expected return, and only 16% had scaled enterprise-wide. Half reported that rapid investment had produced disconnected technology inside their organizations.

That is a revealing combination: enormous enthusiasm, aggressive investment, uneven returns, and fragmentation.

An executive can now assemble a remarkable collection of AI capabilities and still fail to create an augmented organization. The missing element is usually not another model. It is the architecture connecting the models to human work.

Who gives the system context? Who determines whether that context is current? Which agent is acting as researcher, critic, strategist, analyst, drafter, or verifier? What evidence survives from one stage to the next? Where are assumptions made visible? Which decisions require a second source? Which outputs require specialist review? When does a machine recommendation become a human commitment?

Without those answers, AI often produces a strange form of executive inflation: more reports, more drafts, more dashboards, more analysis, more ideas, more apparent sophistication—and more material demanding senior attention.

The leader gains output while losing governability.

The executive operating system matters more than the prompt

Prompting remains useful, but the next phase of executive AI will be governed less by clever phrasing and more by operating design.

Imagine two leaders using identical models.

The first executive asks the model for an answer, reviews the output quickly, makes a few edits, and sends it into the organization. The model is fast. The leader is fast. The workflow is fast.

The second executive defines the decision first. The system receives bounded context. One AI role gathers evidence. Another challenges assumptions. A third generates alternatives. The human leader identifies consequences and stakeholders. High-risk claims receive additional verification. The final recommendation includes uncertainty, evidence, unresolved questions, and a named human owner.

The second workflow may take longer than the first. It can still be dramatically faster than the pre-AI process while producing something the first workflow does not: defensible capacity.

That distinction will become increasingly important as AI agents move from assisting with individual tasks to executing multi-step work across an enterprise.

The most dangerous AI failure may look successful

Executives are accustomed to visible failure. A broken model, an obvious hallucination, a numerical error, or an embarrassing output attracts attention. Those problems can be detected and corrected.

The more difficult failure is a plausible answer that survives because it looks complete.

A polished strategic recommendation can contain weak assumptions. A coherent market analysis can omit the stakeholder who changes the decision. A persuasive memo can flatten uncertainty into confidence. An efficient automated process can institutionalize the wrong objective.

This is where executive experience can create a new hazard. Senior leaders are trained to move quickly through familiar patterns. AI increases the volume of patterns presented to them. The combination can create a powerful illusion of fluency: the model produces something recognizable, the executive recognizes the structure, and both parties move forward before the underlying premises receive enough scrutiny.

That is why verification should scale with consequence.

A low-consequence draft can tolerate a lightweight review. A board recommendation, employment decision, capital allocation, public claim, safety judgment, regulatory interpretation, or strategic commitment deserves a much stronger evidence chain.

The same AI model can participate in both. The governance surrounding it should be radically different.

The new executive advantage is capacity with consequence ownership

There is an appealing story about AI and leadership in which the best executives simply become superhuman: one leader with the productive reach of ten, fifty, or a hundred people.

That metaphor is incomplete.

The augmented executive is better understood as the designer of a human–AI system. The leader decides which capabilities belong where, which work should be delegated, which work should be challenged, and which decisions must remain explicitly human.

That means the executive role becomes more architectural.

Leaders will need to design context instead of merely consume information. They will need to assign roles to AI rather than treat one model as an all-purpose oracle. They will need evidence chains that survive the speed of automation. They will need escalation rules before something goes wrong. They will need to know when human expertise should override machine fluency and when machine breadth should challenge human intuition.

Most of all, they will need to preserve consequence ownership.

An AI system can recommend layoffs. It cannot carry the moral, cultural, legal, operational, and human meaning of that decision. It can rank acquisition targets. It cannot own the capital at risk. It can advise on a public statement. It cannot inherit the trust lost if leadership gets the statement wrong.

The machine can contribute intelligence. The organization still needs someone who can be held accountable for what happens next.

The question most leaders are asking comes too late

Many executives begin with: “What should I use AI for?”

That question sounds practical, but it often starts the design process in the wrong place. It assumes the unit of analysis is the tool.

A stronger system begins earlier—with the work, the consequence, the context, and the decision rights.

This is where The Augmented Executive goes beyond a list of AI use cases. The book develops a role-based AI bench, governed context, evidence chains, decision support, and explicit human authority as parts of one executive operating system.

There is one question in that system that I have deliberately left out of this essay. It comes before delegation. It determines whether a task should be given to AI, shared with AI, challenged by AI, or kept under direct human control. Once leaders start using that question consistently, they often discover that their biggest AI opportunity is not where they expected it to be.

That is the hinge.

AI will continue getting faster. Executive advantage will come from deciding where that speed belongs.

Research behind the argument

• Microsoft, 2025 Annual Work Trend Index: The Frontier Firm Is Born
• IBM Institute for Business Value, 2025 CEO Study
• Brynjolfsson, Li & Raymond, Generative AI at Work, NBER / Quarterly Journal of Economics

Original commentary by Keith Lawrence Miller, M.A.. Cite the canonical page when quoting or summarizing. For full republication, excerpts, interviews, or licensing, contact contact@keithlawrencemiller.com.