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What should “control” mean in an AI workflow?
Control is a set of operational safeguards around a model, not a claim about the model’s character. For every deployed agent, operators should be able to answer three questions: What did it do? What information and rules shaped its decision? What can a person do if the action is wrong or out of bounds?
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- Visibility: Show the agent’s actual actions and the data it used. Its own summary is not a substitute for an inspectable record.
- Predictability: Document the business rules, exceptions, and judgment the workflow expects. A model cannot reliably follow norms that exist only in employees’ heads.
- Control: Set boundaries on permitted actions, define when human review is required, and make stopping or redirecting the workflow practical.
Chris Willis makes this case in a Built In opinion article published September 14, 2026, reviewed by Seth Wilson. Willis writes, “The useful question is whether you’ve built the system around the model so that trust is beside the point.” This is an operational argument, not evidence that any particular model or workflow is safe by default.
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People reviewing an AI recommendation need enough context to verify it without reconstructing the entire workflow. For example, a refund-denial recommendation should show the policy it applied and the records it read. The interface should distinguish governed information and rules from generated text or an inference the agent has improvised.
- Display the source records and relevant policy at the point of review.
- Show the agent’s actions, not only its final answer.
- Indicate whether the case falls within the workflow’s defined competence or requires judgment beyond it.
- Provide a clear intervention route: approve, correct, redirect, or stop, as appropriate to the system.
Visibility is useful only when it supports action. A log that no operator can inspect in time, or an override button that does not halt the next step, offers little practical control. Willis summarizes the interface point this way: “Transparency, accountability and oversight live in the interface or they don’t live at all.”
Make business judgment explicit before deployment
Many workflow failures arise not from a missing written rule but from an unwritten one. Employees may know when a discount is exceptional, when an angry customer needs a careful response, or when a policy should be escalated. An agent cannot consistently apply that tacit knowledge unless the organization turns it into accessible rules, examples, exception paths, or review requirements.
Rank #2
Before automating a decision, identify the standard case, the exceptions, and the person or team responsible for resolving ambiguity. Define what information the agent may rely on and what it must do when the available evidence conflicts or does not fit. If a workflow has no agreed answer for an exception, route it to a person rather than asking the model to invent a business norm.
Decide which tasks need human review
Human oversight should follow the task’s risk, not a blanket rule that every output gets the same review. Willis proposes three useful axes: the cost of an error, whether the judgment is explicit or tacit, and whether an action can be reversed. These are decision prompts, not a validated scoring rubric.
Rank #3
| Question | Less review may be appropriate when… | More review is appropriate when… |
|---|---|---|
| What is the cost of an error? | The consequence is limited. | The consequence is serious, including regulatory concern. |
| What kind of judgment is required? | The task follows explicit data or codified rules. | The case depends on ambiguous, tacit, or undocumented judgment. |
| Can the action be reversed? | It is easy to undo. | It is difficult or impossible to undo. |
Expense categorization, support-ticket routing, and invoice-to-purchase-order matching are examples of comparatively explicit work that may suit lighter review when errors are low-cost and correctable. Non-standard discounts, responses to angry customers, and matters a regulator might question are examples where human handling is more appropriate. The surrounding organization still has to assess its own consequences and rules; these examples do not establish that a task is safe to automate in every company.
Use a measured approach to scaling AI
Governance and workflow design require effort beyond choosing an algorithm. Boston Consulting Group’s January 15, 2025 article, “From Potential to Profit: Closing the AI Impact Gap”, describes a 10-20-70 principle followed by top-performing organizations: 10% of effort on algorithms, 20% on data and technology, and 70% on people, processes, and cultural transformation. BCG says the principle reflects its 2025 survey, in which more than 1,800 executives participated. It is BCG’s effort-allocation principle, not a universal measured split of AI value or a guarantee of results for every organization.
The practical implication is to treat operating rules, accountability, and usable oversight as part of implementation—not as polish to add after an agent is already making decisions.
Turn oversight into an operating procedure
- Define the task boundary. Write down what the agent may do, what information it may use, and which cases must be escalated.
- Expose the basis for decisions. Put relevant records, policies, and action history where an operator can inspect them alongside the recommendation.
- Test exception handling. Check how the workflow behaves when data is missing, rules conflict, or a case requires undocumented judgment. Specify whether it pauses, asks for clarification, or routes to a person.
- Make intervention effective. Ensure operators can correct, redirect, or stop actions before consequential steps proceed, and make responsibility for handling escalations clear.
- Revisit the boundary when the workflow changes. New policies, data sources, or action permissions can change the risk; update the rules and review path accordingly.
The central question is not whether a person can nominally remain “in the loop.” It is whether that person has the evidence, authority, and time to intervene when the system departs from the agreed workflow.
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