AI Can Produce the Answer. Who Decides Whether It Is Good Enough?

An AI-assisted draft rarely becomes a business commitment through a formal decision. It happens through an ordinary action. Someone forwards the document, pastes a paragraph into a proposal, or quotes its recommendation in a planning meeting. From that moment, the organization treats the content as something it knows, says, or intends to do. The basis for that reliance, and the person who owns it, may never have been examined.

Short answer: the leader accountable for the business activity owns the standard for when AI-generated work is good enough to use, and the people closest to the work help design how that standard is applied. For a customer proposal, that typically means the commercial leader working with operations. This standard is the working core of AI governance: it defines what must be verified, who checks it, and who can stop the work.

Jean Dorff, founder of Jean Dorff Consultancy, has spent thirty years translating between technical and business worlds and now advises leadership teams on decision criteria. He argues that executives should settle one question before asking how AI can produce more work: what makes that work fit to use, who checks it, and who stands behind it when it carries consequences.

The concern has external support. On October 1, 2026, Google updated its Search Central guidance on using generative AI content, and its documentation changelog lists the change. Search Engine Journal reported that the page now calls it critical to manually fact-check and review all AI-generated content before publishing, because generative models predict likely word sequences rather than retrieve facts, and their outputs may contain inaccuracies. The review extends to titles, meta descriptions, structured data, and image alt text. The page also keeps an earlier warning that predates this update: generating content at scale without adding value for users may violate Google's scaled content abuse policy, so added value belongs in the same review. Google has not announced a ranking change or penalty, but its choice of the word “critical” in official documentation shows how seriously it now treats verification. The deeper issue sits inside the organization.

When “Reviewed” Means Different Things to Different People

Consider a hypothetical, offered as illustration rather than a client case.

A customer receives a proposal containing a six-week implementation timeline. The customer accepts it. The operations director asks who confirmed the schedule.

The person who sent the proposal understood it had been reviewed. The reviewer checked the wording and the commercial offer. The person who prepared the draft says the timeline came from the AI-generated version and looked reasonable.

Several people handled the document. Nobody confirmed the delivery commitment.

That is the moment the ambiguity becomes visible. The leader discovers that “reviewed” meant different things to different people. Reviewing language, verifying facts, assessing feasibility, and authorizing a commitment are distinct responsibilities, and nobody had sorted out which ones were done.

The Decision Lab, a behavioral science publication, defines this as accountability diffusion: responsibility for AI-influenced decisions spreads across people, teams, and systems until no one feels fully answerable for the outcome. A Boston University Questrom School of Business article on moving beyond AI pilots names unclear ownership among the obstacles when organizations try to scale AI beyond experiments, a condition that also appears at the level of a single document.

Why Adding Another Approval Step Does Not Fix AI Review

Once a leader sees the problem, the first instinct is usually to add a step. Another approval. Another sign-off.

Dorff cautions against this. His argument is that if "reviewed" was ambiguous, "approved by a manager" relocates that ambiguity to someone more senior. Unless that person receives something different, they see the same document, with the same missing evidence, and less time to examine it. Their signature then gives the work greater authority without improving its basis.

Meaningful accountability requires three things working together:

A defined question. What exactly must this reviewer establish before the work moves forward?

Relevant expertise. In the six-week example, the missing check concerns delivery capacity. Only someone who knows the delivery team can confirm the schedule.

Authority to withhold approval. The reviewer needs permission to return the work, qualify a claim, or stop it from going out. Responsibility means little if the person is expected to approve everything to keep the workflow moving.

The checks should also reflect consequences. An internal brainstorming draft and a customer promise do not require the same scrutiny.

Dorff offers a simple test for any approval step: what must this person establish, and what could make them withhold approval? If neither answer is clear, the organization has added a handoff rather than a meaningful control.

How to Tell Whether an AI Review Standard Works Under Pressure

A governance document can preserve a standard. Its value depends on how people apply it when application costs something.

Picture the moment. A polished proposal is ready to send. The deadline is close. Everyone wants to move forward. Someone notices that the delivery promise has not been confirmed. What the organization does next reveals whether the standard is real.

A leader who resolves the issue follows the standard into actual workflows: who checks the commitment, what information that person needs, what happens when the check fails, and whether the reviewer has the time and standing to act. A leader who only documents the issue produces a framework that sits in a folder while the same drafts move forward unexamined.

Leaders also need to examine their own responses. If a leader says "challenge unsupported claims" but becomes impatient whenever someone delays a promising proposal, people learn which instruction carries more weight.

Dorff's test: leadership has resolved the issue when informed judgment can change what the organization does, and the people exercising that judgment are supported when it does.

Why Internal Verification Mirrors External Legibility

This connects to Dorff's broader work on legibility. Outside the organization, people need to understand what the business offers and why its claims are credible. Inside it, people need to understand what they are approving and on what basis.

In both cases, clarity gives people something they can judge. An approval nobody can explain carries the same weakness as a claim nobody can verify.

How to Audit One Piece of AI-Assisted Work

Before commissioning a framework or assigning tasks, Dorff recommends a smaller exercise.

Take one recent piece of AI-assisted work your business actually relied on: a proposal, a customer communication, or a recommendation. Then ask one question.

Follow that question back through the work. What was checked? What remained an assumption? Who understood the consequences of using it? Was that basis clear at the time, or are you reconstructing it now?

You may find a sound process. You may find that people exercised good judgment without making it explicit. Or you may find that a polished draft moved forward because everyone assumed someone else had checked it.

Each situation calls for a different response. Establish which one you are in first. That gives you a position from which to decide what deserves attention next.

If AI is creating decisions faster than your organization is establishing the criteria to judge them, an independent strategic conversation can help clarify what deserves attention next. Jean Dorff Consultancy.

Sources

Google Search Central, Guidance on using generative AI content (updated October 1, 2026) · Google Search Central, Latest documentation updates (October 1, 2026) · Search Engine Journal, Google Tells Sites To Fact-Check AI Content Before Publishing · The Decision Lab, Accountability diffusion in AI · Boston University Questrom School of Business, Moving Beyond AI Pilots: What Organizations Get Wrong.

This article is last updated October 8, 2026