Turn AI ambition into a coordinated plan for the business

An AI agenda becomes useful when its business purpose connects to the people, systems, and operating responsibilities needed to carry it out. Our Executive AI Decision Map organizes that discussion around six choices.

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This is KeenSight’s framework for organizing executive discussion. Its value comes from making relationships visible. A platform choice can change operating costs. A workflow opportunity can expose a capability gap. A governance requirement can influence both architecture and delivery scope.

The map also helps distinguish decisions that need leadership alignment from questions a project team can resolve within an agreed direction. That distinction gives executives a practical way to sponsor the work without becoming the approval point for every implementation detail.

The current state often contains useful work worth preserving: a team improving proposals, a manager using AI for analysis, or engineers testing a new development approach. The next question is which practices should remain local and which deserve shared support or investment.

Begin with the business process. Where does information arrive? What decisions follow? Which handoffs consume time? Where does quality depend on an individual’s knowledge? Those details help distinguish a convenient tool from a change that could improve how a function operates.

Then connect the supporting choices. Give the initiative an owner. Establish the information it can use. Define how people check important outputs. Decide what evidence would justify expansion. Where several initiatives share the same need, consider whether a common capability would be more useful than separate solutions.

Progress should be visible in the work itself. Depending on the initiative, that could mean a shorter response cycle, more consistent proposals, better handling of service exceptions, greater production capacity, or stronger customer follow-through. The relevant measure comes from the business objective and an understood baseline.

A useful executive review can therefore ask three connected questions: what changed in the workflow, what changed in the outcome, and what did the organization learn about operating the capability? The answers help determine whether to expand, adapt, or redirect investment.

Start with a conversation about the business situation and the decision you want to improve. We can examine the priorities already established, the initiatives in motion, and the capabilities the organization has available. That context helps determine whether the immediate need is executive alignment, a specialist decision, or a defined piece of delivery work.

The people involved should reflect the question. A strategic investment may need executive and financial perspectives. A workflow discussion benefits from the people who perform and manage the work. A platform decision needs those who understand the existing technology and its operating requirements. Bringing the relevant perspectives together helps surface dependencies early.

Useful inputs might include an existing strategy, a portfolio of ideas, a process description, or lessons from a pilot. They provide a starting point for discussion rather than a required package you must complete before contacting us.

The conversation should lead to an appropriate scope for the next step. That scope can establish the decision to resolve, the information to examine, the people who need to contribute, and the output that would help the organization act. As the work develops, we can connect it to adjacent decisions without losing sight of the original business purpose.

BBVA describes its “The Eight” roadmap as six solution areas supported by two shared foundations: data and AI architecture/capabilities. The example makes the relationship between business priorities and supporting infrastructure tangible. The roadmap includes existing capabilities and future ambitions. BBVA’s strategy description.

McKinsey’s account of Emirates Global Aluminium describes a digital factory delivering use cases alongside a center of excellence, data infrastructure, and employee capability building. It offers an industrial example of implementation and foundational work progressing together. McKinsey was a delivery partner in that program. EGA case study.

These external examples illustrate different ways to connect an AI agenda to operating practice. Your organization’s choices should follow its own priorities, existing capabilities, and constraints.

Different initiatives need different starting points. The delivery path should reflect the work already completed and the question that remains.

AI Workflow Blueprint. Use a blueprint when the workflow, business case, architecture, controls, or implementation scope needs definition. It helps connect the intended outcome to the process and system that would support it.

Production Pilot. A bounded pilot can test important assumptions against representative work. The focus is the decision the test should inform: whether quality is sufficient, how people will use the output, what integration is required, and what must change before expansion.

Custom AI Development. When an initiative has a sufficiently clear scope, development connects the required capabilities into a working application or process. That can include enterprise integrations, domain logic, evaluations, permissions, and the experience people need to use the system.

Managed AI Operations. A running system creates continuing work: performance review, cost management, incident response, changes to models or business requirements, and improvement of the workflow. Operational ownership helps keep those responsibilities visible after launch.

These are possible entry points. An organization with a well-defined production requirement can discuss that requirement directly. An active system can enter an operations conversation without repeating an earlier strategy exercise.