Plausibility Is Becoming a Competence Signal

Professional consulting has always relied on a fragile coupling: the link between producing an answer and being able to stand behind it later.

Deliverables were never the actual product. They were signals—evidence that judgment, context, and an understanding of consequences existed somewhere behind the work. Speed mattered only insofar as it correlated with experience. Faster delivery usually implied familiarity with constraints that were rarely written down.

AI weakens that correlation.

Output can now be produced without traversing the path that once made it meaningful. Artifacts look complete even when the reasoning behind them is thin, fragmented, or absent. The system does not fail when this happens. It continues to deliver. What changes is the signal it emits.

For consulting organizations, this is not a tooling problem. It is a selection problem.

When output becomes cheap, the system loses its ability to distinguish between learning and delivery. Plausibility begins to substitute for competence—not because anyone intends it to, but because existing incentives reward motion more reliably than understanding. Billable work does not encode depth. Delivery pressure does not wait for explanation. Review practices rarely scale at the same speed as production.

AI aligns perfectly with these conditions.

The visible distance between junior and senior work compresses. Juniors can contribute earlier, which looks like progress. Seniors become less distinguishable by artifacts alone, which looks like efficiency. The gradient flattens, and with it, one of the system’s informal feedback loops disappears.

What remains is responsibility—but it relocates.

Consulting competence is rarely tested at the moment of delivery. It is tested later, when assumptions are challenged, when scope shifts, when something breaks quietly rather than catastrophically. In those moments, the question is no longer what was produced, but whether it can be explained, adapted, and owned without reconstructing intent after the fact.

AI does not participate in that reconstruction.

As a result, the system begins to accumulate a specific kind of risk. Not incorrect work, but work whose rationale cannot be recovered independently of the tool that assisted it. Explanation becomes optional. Accountability becomes diffuse. Trust becomes harder to repair because it no longer rests on shared understanding.

None of this requires misuse.

It emerges wherever speed is rewarded without an accompanying requirement to externalize reasoning. Where promotion tracks delivery milestones rather than explanatory capacity. Where review processes validate results but not the path that led to them. AI amplifies these dynamics by making it easier for the system to accept output without insisting on the competence that once justified it.

Over time, the system adapts.

Consultants advance with less exposure to failure modes that require reconstruction rather than correction. Expertise does not vanish, but it becomes thinner, harder to locate, and increasingly concentrated in fewer individuals. The organization appears productive while its capacity to absorb uncertainty quietly degrades.

AI does not cause this outcome. It removes the friction that once delayed it.

What remains scarce is not skill in the abstract, but skill that can be defended when conditions change. Consulting has always depended on that scarcity, even when it avoided naming it. AI makes it visible by eliminating everything else that once masked it.

The system will not correct for this on its own.

It will continue to reward what moves fastest, promote what looks complete, and defer the cost of explanation until it becomes unavoidable. As always, its priorities will be revealed not through intent, but through accumulation.

That accumulation is already underway.