Executive summary
Before commissioning AI delivery, leaders must own the business choices behind the mandate. AI and external expertise can strengthen exploration and execution within that direction.
An AI proposal can make business choices before leadership recognizes that a choice has been made. A request to “implement AI in customer service” leaves open what better service means, whose needs take priority and which decisions may be delegated. Those choices can enter through assumptions in the proposed workflow.
When leadership approves the delivery scope without examining those assumptions, a provider’s design can become the enterprise’s direction by default. A technically sound solution may then optimize a measure the business has never properly questioned.
Leadership must explain those choices before a vendor is commissioned or delivery begins.
Don’t outsource your brain; the muscle you can.
The enterprise’s “brain” is its strategic judgment: choosing priorities, deciding acceptable compromises and owning the business consequences. Retaining that judgment requires the internal capability to assess a proposal and explain why its choices are acceptable.
AI and external partners can contribute ideas, specialist knowledge and challenge. Their input may change the vision. Leaders remain responsible for adopting that direction, informed by the people who understand the work and its consequences.
The “muscle” includes exploration, synthesis and execution within agreed authority. It can strengthen thinking as well as delivery. Partners remain responsible for their own work; the enterprise must continue to own the purpose and the choices behind the mandate.
Begin with the customer and business outcome the enterprise wants. Design the Future Operation First explains how to develop that direction and work backward to a feasible intervention.
Use AI directly as cognitive muscle to develop alternatives and challenge assumptions. Use approved tools and feed them appropriate context. Compare models when it adds useful challenge; agreement is not validation. Bring employees, relevant specialists and operating evidence into the assessment. Working with AI When the Problem Is Still Taking Shape develops this exploratory practice.
Before commissioning implementation, record the intended outcome, decisions retained by people, permitted actions and evidence required for acceptance. Name the Outcome owner and identify unresolved choices that need an internal decision. If the direction is still uncertain, commission discovery around explicit questions before committing to a build.
Hypothetical example. A company asks a provider to use AI to draft complaint responses faster. Yet a prompt reply can leave a complaint unresolved when the employee lacks authority to approve a remedy. The business must decide what counts as resolution, who may authorize remedies and which unresolved cases require escalation.
The first scope might allow AI to summarize case information and draft responses, while authorized people approve remedies and case closure. The Outcome owner would set the criteria for a resolved case and a follow-up period for detecting reopened complaints. Test resolution quality, elapsed time and total review effort against comparable work. Faster drafting alone would not establish improved complaint handling.
Treat the mandate as a working hypothesis. Ask the delivery team to surface evidence that challenges it. A change to the customer promise, acceptance criteria or delegated authority should return to the appropriate internal owner before it enters the operation.
Give the Outcome owner the authority and resources needed to achieve the result, while preserving specialist decision rights. The Bounded Autonomy Framework can help translate delegated decisions into enforceable limits and a workable human response.
NIST’s voluntary AI Risk Management Framework 1.0, published in January 2023, assigns executive leadership responsibility for AI risk decisions and calls for explicit responsibilities in human–AI arrangements. [1] These provisions support clear ownership; they do not establish that any particular delivery model will succeed.
Leadership must retain the ability to question a proposal, change the mandate and judge the outcome after delivery begins. An executive signature has substance when the organization understands and can revise the choices it authorizes.
Before approving our next AI engagement, which business choices have we made explicitly—and which are still hidden inside the partner’s proposed solution?
Short, practical perspectives on AI-era operations, governance, and operating-model transformation.
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