Strong Tower Consulting · October 22, 2026
A promising demonstration creates interest. A dependable way of working takes a different set of decisions. Before expanding an enterprise AI pilot, leaders need a clear view of the workflow, the people responsible for it, and the evidence that will justify the next investment.
At Strong Tower, we recommend organizing that discussion around five decisions. Each should produce something the delivery team can use: a defined outcome, an accountable owner, practical controls, an adoption plan, and a small scorecard.
1. Choose a workflow and define the outcome.
Start with a specific task and the people who perform it. Identify where the work begins, what information it uses, who depends on the output, and what a good result looks like. A broad ambition to improve productivity becomes useful when it is connected to a repeatable activity.
For example, a team might explore AI assistance for preparing a weekly program status report. The outcome is a report that gives leaders reliable information in time to make decisions. Document the current preparation time, recurring errors, review effort, and missed information before changing the process.
Decision to leave with: one defined workflow, its users, a baseline, and the business outcome the pilot will test.
2. Name the owner of the complete process.
Technology ownership and business accountability need to connect. The person responsible for a platform may not own the quality of the report, the service delivered to a customer, or the decision made from an AI-generated output.
Identify who can approve the workflow, who reviews outputs, who resolves exceptions, and who can pause its use. Give that owner a practical route to technology, security, privacy, and business specialists when a decision crosses functional boundaries. Agree who will support the workflow after the project team moves on.
Decision to leave with: a named business owner and clear responsibilities for review, escalation, support, and expansion.
3. Set controls that fit the work.
Define which information may enter the system, who may access it, what outputs require human review, and which actions the system may take. Consider the consequences of an inaccurate answer or an inappropriate action in the specific workflow.
For the status-report example, reviewers might require a source for every material claim, check changes to dates and costs, and approve distribution. An assistant that can update systems or contact others needs additional limits on those actions and a clear way to stop or reverse them.
NIST describes its AI Risk Management Framework as a voluntary resource for incorporating trustworthiness into the design, development, use, and evaluation of AI systems. Use that kind of guidance to inform decisions that fit your organization and the work being performed.
Decision to leave with: permitted data, access rules, review requirements, action limits, and an escalation path.
4. Build adoption into the delivery plan.
Involve the people doing the work early enough to change the design. Ask what slows them down, which exceptions occur, and what would make the proposed workflow difficult to use. Test with realistic tasks and provide an accessible way to report problems.
Training should prepare people for the decisions they will make. They need to know when to use the tool, how to check its work, when to use another process, and where to ask for help. Managers need the same clarity so expectations remain consistent after launch.
Plan support beyond the initial demonstration. Review early questions, revise instructions, and remove unnecessary steps as the team learns.
Decision to leave with: a user testing plan, role-specific learning, named support, and a feedback process.
5. Agree how evidence will determine the next step.
Set the review date and success criteria before the pilot begins. A useful scorecard balances time, quality, adoption, risk, and operating cost. Account for the work needed to check and correct outputs; time saved generating a draft is only one part of the full process.
Compare results with the baseline and examine the exceptions. A tool may help one task or group while adding effort elsewhere. Ask users what changed, inspect a meaningful sample of outputs, and assess whether the benefits can be sustained with the available support.
Make the next decision explicit: expand, revise, continue testing, or stop. Each is a valid outcome when it follows the evidence.
Decision to leave with: a small scorecard, a review date, and agreed criteria for the next investment.
Make the next leadership discussion concrete.
Bring one workflow, its current baseline, and a proposed owner to the table. Work through the controls, people, and measures together. That gives the team a clear delivery brief and gives leaders a practical way to judge progress.
Strong Tower connects AI enablement, program leadership, and organizational change and adoption to help clients plan and deliver that work.
Further reading: NIST AI Risk Management Framework. The workflow example above is illustrative.