Growth, AI, and Data Strategy

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Your team is talking about AI agents. Your board wants an AI strategy. And every vendor in your inbox is promising AI will solve everything.

The pressure to move fast is real. But something we've observed: the teams that start with the flashiest AI use case almost always stall. Those who start with known workflows can build something that provides measurable value. 

The difference comes down to one principle: start where you can measure, scale where you can differentiate.

The Problem With Starting at the Frontier

It's tempting to jump straight to the high-impact vision: AI-powered product features, predictive lead scoring, autonomous agents that replace entire workflows. These are real opportunities, and they will matter. But they share a common trait that makes them terrible starting points.

They have no existing benchmark.

When you launch a predictive churn model on day one, how do you know if it's working? You're comparing AI output against... nothing. There's no human baseline, no established accuracy threshold, no clear way to show the CFO that the investment paid off.

That's how AI initiatives become expensive science projects that lose executive support after one quarter.

Why Internal, Measurable Use Cases Win First

Internal workflows are the opposite. They have known inputs, known outputs, and known costs. Your team already spends a specific number of hours each week generating pipeline reports, building FP&A summaries, or reconciling trial conversion data across systems.

An AI agent that automates a weekly forecast report doesn't need to prove a hypothetical ROI. You can measure it immediately: hours saved, consistency improved, leadership visibility gained. And that measurable win does something more important than saving time. It builds cultural proof that AI actually works at your company.

That cultural proof is what funds everything that comes next.

A Practical Maturity Path: Crawl, Walk, Run

The companies getting real value from AI follow a deliberate progression, not a moonshot.

• Crawl: Target high-visibility workflows where data already exists and benchmarks are clear. Weekly pipeline summaries that currently pass through three layers of rewrites before reaching leadership. FP&A reporting that consumes hours of manual work every cycle. These are agents you can validate against human output from day one, targeting 90%+ efficacy before expanding.
• Walk: Layer on contextual intelligence that compounds on the foundation you've built. Churn signal detection that goes beyond keyword matching to classify risk from transcripts, support tickets, and product usage patterns. Auto-generated battle cards that synthesize competitor intel and customer feedback into actionable sales context. Now your agents aren't just automating tasks. They're surfacing insights humans would miss.
• Run: This is where frontier AI earns its place. Agentic lead scoring that replaces static MQL lookup tables with a self-improving enrichment loop. Product-embedded intelligence that becomes a genuine competitive moat. These capabilities require the data infrastructure, organizational trust, and operational rigor you built in the earlier phases.

The key insight: each phase validates and accelerates the next. Skip ahead, and you're building on sand.

The Real Unlock Isn't Technology

The biggest blocker to AI adoption at most organizations isn't technical capability. It's trust.

Leadership needs to see that AI delivers reliable, auditable output before they'll fund the transformative use cases. Engineering needs to see that agents don't create more maintenance burden than they eliminate. And the broader org needs proof that this isn't another initiative that fizzles after the kickoff deck.

That's why the first agent matters more than any roadmap slide. It sets the tone for everything that follows.

Pick a use case with known data, known benchmarks, and high visibility. Build a constrained agent with guardrails. Validate it against human performance. Then scale to adjacent use cases where the returns compound.

What Comes Next

The organizations that will lead with AI in the next two years aren't the ones with the most ambitious vision. They're the ones who earned the right to scale by proving value one measurable win at a time.

The question isn't whether your company should invest in AI. It's whether you're starting in a place where you can actually prove it works.

Need help deciding where to start? We’d love to talk you through it. 

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