Project Management
September 25, 2026
AI accelerates answers; who makes the decisions?
The speed of technology only becomes an advantage when it is accompanied by the capacity to understand, verify, and assume responsibility for the results
Imagine a team that starts producing in an afternoon the analyses that previously required a week. The change seems to justify the investment in artificial intelligence. But, before accounting for the gain, it is worth asking who examined the results, what information supports the conclusions, and how much time will be needed to use them safely. If production accelerates and evaluation remains limited, part of the work has merely changed places.
One of the most important issues in AI adoption is what happens when the capacity to produce answers grows faster than the capacity to verify them. For managers, this difference can appear in reports, projects, forecasts, and recommendations. The document is ready, but the decision still requires someone capable of vouching for it.
A recent debate on the Navier-Stokes equations helps to understand this situation. In September, OpenAI announced a proposed solution to a mathematical problem related to the equations that describe fluid motion. According to the company, approximately 10,000 AI agents participated in the effort that produced the demonstration in 88 hours. The announcement indicated a relevant computational research capability, but also raised questions about the origin of the contributions and their recognition (OpenAI, 2026).
For organizations, the most useful point of this episode lies in the distance between obtaining a result and transforming it into reliable knowledge. This distance needs to be factored into productivity evaluation. A team can produce more and still accumulate conclusions that no one has had the opportunity to examine adequately. The gain is only complete when the result can guide a decision.
Productivity needs to include verification
In management, it is usually easier to measure execution time than the quality of judgment. It is possible to record how many reports were delivered and how many hours were saved. Assessing whether the team identified an inadequate hypothesis, avoided a hasty interpretation, or realized that data was missing requires more careful monitoring. With AI, it is important to give more visibility to this second type of work.
Consider a demand analysis that recommends reducing the stock of a certain product. The system can organize the data and present a convincing justification. Before acting, however, someone needs to check if the history includes a stockout: low sales may reflect a lack of merchandise, not a lack of customers. The decision depends on knowing the business and examining the meaning of the numbers.
This example shows why the person responsible for validation should participate from the definition of the task. They need to know what will be evaluated, what data is available, and what evidence would justify accepting the conclusion. Leaving this responsibility until the end favors a rushed review, especially when the presentation already seems complete and the deadline has been consumed by the expectation of delivery.
The verification effort should also track the consequence of the decision. A draft internal communication might undergo a simple review. A recommendation that alters an engineering design or commits resources requires testing, documentation, and expert evaluation. Applying the same procedure to everything wastes time in some situations and leaves others insufficiently examined.
Terence Tao offers a useful contribution to this discussion by distinguishing the production of proofs from their verification, explanation, and incorporation into collective knowledge. His argument helps to realize that accelerating one step can increase the demand on subsequent steps (Tao, 2026). In companies, this reflection recommends evaluating the complete process: time to decision, rework, identified errors, and ability to explain the conclusion.
It is in this sense that a demanding adoption of AI is understood as necessary. The tool can take on extensive tasks, explore alternatives, and help confer results. It is up to the organization to create conditions to leverage this capability, including time and people to evaluate what has been produced. The verification must be foreseen in the project and budget.
Knowledge is also in the journey
The Navier-Stokes case adds another dimension. Mathematician Tristan Buckmaster, who worked on problems related to Levent Alpöge, reported using AI tools and inputting drafts into Codex. He questioned a possible influence of these interactions on OpenAI’s result, although he stated he did not know if his data had been used (Buckmaster, 2026).
The company responded that an internal investigation had ruled out the influence of Buckmaster’s prompts from the previous two months, including through training. This response should accompany the presentation of the suspicion, preserving the difference between an allegation and the conclusion disclosed by the organization involved (OpenAI, 2026).
Regardless of the outcome of this controversy, I see a practical issue for knowledge management. A research or a project holds value before the final delivery. Hypotheses, failed tests, choice criteria, and discarded solutions reveal what the team has learned. When this material goes through external tools, the organization needs to decide what information can be used, under what conditions, and how the path will be recorded.
Authorship must also follow this path. In an AI-assisted project, someone formulates the problem, selects information, establishes constraints, and interprets results. These contributions need to remain visible. Simply recording who requested the last response can erase an important part of the team’s work and make it difficult to identify responsibilities.
Consider a system developed with AI support that works during the presentation but fails after deployment. To fix it, the team needs to know the design decisions, the test conditions, and the known limits. A history of instructions can help, but it needs to be accompanied by documentation that allows another professional to understand and continue the work.
Therefore, a delivery must include the necessary explanation for its continuity. This practice protects the organization’s investment and reduces dependence on a person or tool. Time savings in execution lose part of their value if maintenance requires rebuilding everything that was done.
Educate people to assume responsibility
As a Computer Engineering professor, I consider the relationship between outcome and learning especially important. An activity may be completed without the person who submitted it being able to explain the choices made. Education needs to create opportunities to identify this difference, because the professional will be called upon to make decisions in situations that were not foreseen in the exercise.
This requires reviewing what is asked and what is evaluated. In addition to presenting a solution, the student may be asked to justify their hypotheses, compare alternatives, and explain under what conditions they would change their decision. The use of AI can integrate this process, provided that its participation is explicit and the work allows for the evaluation of the reasoning developed.
In companies, the same concern should guide the training of beginner professionals. If all analysis tasks are delegated to the tool, it will be necessary to preserve other opportunities to learn to recognize problems. The review capability depends on technical knowledge and contact with the consequences of choices. It is not enough to appoint a reviewer without providing the conditions for them to develop this judgment.
For Brazilian universities and companies, there is room for cooperation in projects that combine the use of AI and validation in real-world situations. Working with a defined problem, pre-defining success criteria, and comparing results with observed data can yield more useful gains than adopting tools solely based on the hype of their announcements. It also allows for discussions on confidentiality, credit, and responsibility before conflicts arise.
The potential of technology is significant, and harnessing it requires changes in work organization. Professionals capable of formulating good questions, recognizing limitations, and explaining decisions will play a central role in this process. Their contribution needs to be valued even when it manifests as a less optimistic conclusion, a deadline revision, or the decision to perform another test.
The Navier-Stokes case helps put that choice in perspective. The ability to produce results is advancing, and institutions need to develop ways to incorporate them with confidence. For a manager, a question should accompany every promise of acceleration: who is able to explain and assume the decision that will come next? The answer indicates how much of the contracted technology is being transformed into the organization’s capability.
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Who wrote this column
Maurício Acconcia Dias
