AI for Business Strategy

Every executive I talk to in 2026 has the same question: Where do we actually start?

The answer isn't buying more software. It's getting clear on what you're trying to accomplish and building from there.

The AI Strategy Stack

I've been using a simple three-layer model that works across industries:

Layer 1: Efficiency — Do existing work faster

This is the easiest win. Find workflows where your team spends significant time on repetitive, structured tasks.

Examples:

• Drafting first versions of emails, reports, or proposals
• Summarizing meeting transcripts and documents
• Data extraction from unstructured sources (PDFs, emails, call logs)
• Content localization and translation

KPI: Time saved per task or per week.

Layer 2: Effectiveness — Do work you couldn't do before

This is where AI starts creating value you couldn't get any other way.

Examples:

• Personalizing customer communications at scale
• Real-time sentiment analysis during customer calls
• Predictive lead scoring from multiple data sources
• Automated competitive intelligence gathering

KPI: New revenue, improved conversion, or capability that was previously impossible.

Layer 3: Transformation — Change how your business works

This is the hardest layer but the most rewarding. It requires rethinking processes from the ground up.

Examples:

• A support model where AI handles tier-1 and tier-2, humans handle tier-3
• Marketing operations where AI generates, A/B tests, and optimizes campaigns in a loop
• Product roadmaps informed by AI analysis of user behavior at scale

KPI: Unit economics that are 2-3x better than the old model.

A Quick Diagnostic

If you're unsure where to start, ask three questions:

What takes 2+ hours per week for every person on the team?

- That's your efficiency opportunity.

What decision would you make differently if you had 10x more data?

- That's your effectiveness opportunity.

What would you redesign if you weren't constrained by headcount?

- That's your transformation opportunity.

Measuring Success

The most common mistake I see is measuring AI projects like tech projects (did it deploy?) instead of business projects (did it move a metric?).

For every AI initiative, define:

• The baseline — what does this look like without AI?
• The target — what improvement makes this worth doing?
• The timeframe — when do we check if it's working?

If you can't answer all three, don't start yet.

The Bottom Line

AI strategy isn't about keeping up with the latest model release. It's about having a clear view of where your business needs to improve and fitting AI into that picture — not the other way around.