Prioritize Ruthlessly

What is prioritization in an AI strategy engagement?

Prioritization is the second step of an AI strategy engagement: it turns a long list of AI ideas into a short, ranked roadmap. In the TurnerNet method, Prioritize Ruthlessly scores every initiative on impact, effort, and ROI using real data. Only initiatives tied to business outcomes earn a place, an owner, and a budget.

Prioritize Ruthlessly builds on the evidence from Step 01, Find the Signal, and both steps typically fit inside a focused strategy engagement of four to eight weeks. Teams that need only one pilot can use the AI Pilot Accelerator, which ranks candidate use cases by feasibility and business impact. The committed roadmap then moves into Step 03, Ship What Works, for delivery.

What Happens in This Phase

• Impact vs. effort scoring across all candidate AI initiatives using real data, not vendor estimates or gut instinct
• Strategic alignment analysis: does each initiative serve the 12-month business vision, or just the technology roadmap?
• ROI estimation with honest assumptions that account for integration costs, data preparation, and organizational change management
• Initiative stack-ranking with clear ownership, milestones, and decision gates at every phase
• Deliberate killing of projects that sound exciting but will not move the needle on business outcomes

How do you score and rank AI initiatives?

Score each AI initiative on four factors: business impact, implementation effort, strategic alignment, and feasibility. Impact and effort scores come from real data on the process, not vendor estimates or gut instinct. Alignment asks whether the initiative serves the 12-month business vision or only the technology roadmap. Feasibility checks technical viability, data readiness, and the organization’s capacity to absorb the change.

ROI estimates must carry the full cost, including integration, data preparation, and change management, because those costs decide whether a pilot survives in production. The initiatives are then stack-ranked, and each one gets an owner, milestones, and a decision gate. The five-question Signal Test on the AI strategy page is a fast first filter before detailed scoring begins.

What happens to the AI projects that do not make the cut?

AI projects that do not make the cut are stopped on purpose, and the reason is written down. Stopping an initiative that sounds exciting but will not move a business outcome frees budget and engineering time for work that will. The written reason keeps the same idea from returning next quarter under a new name.

A stopped project is not always gone for good. Some stall on data readiness or ownership rather than value, and they can return to the backlog once those gaps close. What remains is an opinionated roadmap: a few initiatives with owners, outcome-tied milestones, and timelines, instead of a wish list that spreads the team too thin.