AI in production needs an owner, not another pilot

An AI pilot can survive on enthusiasm. A production process cannot.

During a pilot, a project lead gathers data, reminds users to try the tool, explains strange outputs, and calls the vendor when something fails. The extra attention makes the use case look smoother than normal operations will be.

Before moving forward, assign an owner for the business decision the AI affects. That owner does not need to build models or administer the platform. The owner does need authority over the process, consequences, and continued use.

Ownership belongs close to the work

If AI recommends maintenance inspections, the accountable owner is likely in maintenance or operations. If it drafts responses to customer requests, ownership belongs with the customer process. IT, security, legal, and data teams have important responsibilities, but they should not inherit the business result simply because the product contains AI.

Name one accountable owner, then document supporting roles. Who manages access and integration? Who checks data quality? Who reviews privacy, contract, and security terms? Who receives user concerns? Who speaks with the vendor about a model or service change?

The NIST AI Risk Management Framework treats governance as a cross-cutting function and calls for clear roles, responsibilities, and lines of communication across the AI lifecycle. A mid-market company can apply that principle without creating a large committee.

Define the production job

Write a short operating statement: who uses the tool, for which decision, using which data, how often, and under what limits.

Specify what the tool does not do. A summarization assistant may prepare a first review but not approve contract language. A quality model may flag parts for inspection but not change machine settings. Boundaries help users understand where judgment and existing controls remain.

Document the manual path when the tool is unavailable or produces an uncertain result. If no workable fallback exists, availability and recovery requirements need more attention before production.

Measure the decision, not the novelty

Pilot reports often count users, prompts, or model accuracy. Production measures should show whether the operating decision improved and whether the tool creates new burden.

For an exception review, track useful findings, missed cases, unnecessary investigations, and review time. For a drafting tool, examine corrections, unsupported statements, turnaround time, and whether required review actually happens. Measures should reflect the cost of different errors.

Establish a baseline from the current process. Set an initial review date and an acceptable range, not a claim that performance will improve forever. Some uses will justify themselves through time saved. Others reduce exposure or make a scarce expert available to more employees. State the value in terms leadership can inspect.

Build the feedback loop into work

Users need a simple way to flag a wrong, unsafe, or unhelpful output at the moment they encounter it. Decide where those reports go and how quickly someone reviews them.

Capture enough context to learn: the input or source, output, action taken, correction, and result where appropriate. Protect sensitive information in this record. Do not collect every interaction indefinitely simply because storage is available.

The owner should review patterns, not adjudicate every technical detail. Repeated errors may point to bad source data, ambiguous instructions, changed operations, inadequate training, or a product limitation. Each calls for a different response.

Control changes after launch

AI services can change through model updates, vendor releases, new data, revised prompts, or altered integrations. A production owner needs to know which changes require testing.

Keep a basic inventory of the use case, provider, model or service where known, data sources, integrations, approved users, and current instructions. Record material changes. Test important workflows before accepting an update when the service permits it, and review outputs after changes that cannot be staged.

Access should follow the job. Remove users who change roles, and review service accounts and administrative privileges. Revisit vendor data practices when features or contracts change.

This is ordinary operational discipline applied to a system whose behavior may be less predictable than traditional software.

Fund the unglamorous work

Production has costs that pilots hide: integration support, data cleanup, user training, monitoring, vendor management, review time, and incident handling. Include them in the decision.

Assign staff capacity, not only a software budget. A tool that saves employees time but consumes a manager’s week reviewing errors may shift work rather than remove it. A model dependent on one analyst has recreated the same single-person risk the project may have intended to solve.

Decide who supports users and what response they can expect. Frontline employees should not have to locate the original pilot team whenever access fails or an output looks wrong.

Give the owner permission to stop

Every production use needs pause and exit conditions. These may include performance outside an agreed range, loss of a necessary data source, a security or privacy concern, a vendor change, or operating changes that make the original use irrelevant.

Stopping is not necessarily a failed innovation program. It can be good management. Preserve records needed for legal, operational, or learning purposes, revoke access, disconnect integrations, and return to the defined manual path.

Conversely, do not leave a successful use in permanent pilot status to avoid accountability. If employees rely on it, the company is already operating it. Formal ownership catches up with reality.

The production decision

Before launch, the accountable owner should be able to answer: What decision does this improve? How will we know? What can go wrong? Who responds? When will we stop?

If those answers are missing, another pilot will not supply them. A focused operating review might.

EdgePoint can help teams define AI ownership and production controls when a use case crosses several functions. The final accountability should remain inside the company, with the leader responsible for the work the tool is meant to improve.