The Strategy Gap
Most organizations have an AI strategy. Few have one that works. The typical AI strategy document is long on vision and short on execution specifics. It talks about "leveraging machine learning" and "driving digital transformation" without answering the critical question: What business outcome will improve, by how much, and by when?
An effective AI strategy is a business strategy that happens to use AI as a key enabler. It starts with business problems, not technology capabilities.
The ROI-First Framework
We use a four-step framework that keeps every AI initiative anchored to measurable business value:
1. Problem Identification: Map your highest-cost, highest-friction business processes. Where do your teams spend time on repetitive cognitive tasks? Where do errors have the biggest downstream impact? Where are decisions being made with incomplete information?
2. Impact Estimation: For each candidate process, estimate the potential impact in dollars. Be specific: "Reduce invoice processing time from 15 minutes to 2 minutes across 10,000 monthly invoices" is a strategy input. "Improve operational efficiency" is not.
3. Feasibility Assessment: Not every high-impact problem is AI-solvable today. Assess data availability, technical complexity, integration requirements, and organizational readiness. Plot opportunities on an impact-feasibility matrix.
4. Sequencing: Start with high-impact, high-feasibility opportunities. Early wins build credibility, fund future initiatives, and create organizational momentum.
Common Strategic Mistakes
The Boil-the-Ocean Approach: Trying to transform everything at once. Start with 2-3 focused use cases. Prove value. Expand.
The Technology-First Trap: Buying AI platforms before identifying use cases. The best tool for a problem you don't have is a waste of budget.
The Pilot Purgatory: Running pilots that never graduate to production. Every pilot should have predefined success criteria and a clear path to production deployment.
Ignoring Change Management: Even the best AI system fails if people won't use it. Budget 30% of your AI investment for change management, training, and adoption support.
Measuring What Matters
Define your KPIs before you build. Track leading indicators (adoption rates, data quality improvements) alongside lagging indicators (cost savings, revenue impact). Review monthly. Adjust quarterly.
The companies winning with AI aren't the ones with the most sophisticated models. They're the ones with the clearest link between AI capabilities and business outcomes. Strategy isn't about being smart — it's about being specific.