Bubble-Proof Your AI Strategy

If the AI hype cycle stopped tomorrow, would your investments still create measurable value, or would they vanish with the buzz?

The moment AI stopped being a headline and became a real design tool, a single question echoed in our strategy reviews: “Where is our AI win?”

That question forced us to rewire our approach. We moved from chasing shiny objects to building a system for durable value. We decided that any AI initiative we launched had to be bubble-proof—designed to deliver results in any market cycle, with or without the hype.

Principle: AI is a Tool, Not a Strategy

Your strategy is the set of critical decisions you must improve, the risks you must reduce, and the value you can prove. AI is simply a powerful tool to execute that strategy. It is not an objective in itself.

The Anti-Hype Filter: Your First Line of Defense

Run every single AI proposal through these three non-negotiable filters. If an idea fails even one, it gets parked until the gap is closed.

1. Impact: Will this materially move revenue, cost, or risk (think >10%) within a single business quarter? Define the KPI and baseline before you start.
2. Data: Do you have clean, consented, and complete data readily available? If not, the project is a data program first, not an AI project.
3. Governance: Are the ethics, compliance, security, and model risk controls defined with a single, accountable owner? If not, the project is not ready for deployment.

An idea that passes all three is a viable investment, not a science fair project.

The AI Investment Scorecard

For projects that pass the filter, use this scorecard to quantify their readiness. Score each prompt on a scale of 0 (gap), 1 (in progress), or 2 (complete). Only fund proposals that score 14 or higher.

• KPI Clarity: One metric, one owner, baseline captured.
• Value Trail: Clear path from the KPI to cash or risk reduction.
• Adoption Path: You know who will use it and how it fits their workflow.
• Data Quality: Coverage, accuracy, and consent are confirmed.
• Model Choice: Simplest model chosen first.
• Controls: Audit logs, bias checks, and a rollback plan are defined.
• Change Management: Training and incentives are planned.
• Security & Privacy: Access is scoped and PII is handled correctly.
• Cost Envelope: Total cost is estimated against expected value.
• Time Box: A 90-day signal is the goal.

The 90-Day Pilot: From Guesswork to Proof

Design every pilot for a clear decision, not for impressive theater.

• Days 1-7: The Setup Name the use case and the specific KPI (e.g., reduce first-response time by 25%). Map all data sources and confirm access. Pick the simplest possible model to start.
• Days 8-45: The Test Build the smallest usable slice of the feature. Train users and embed it directly into their real workflow, not a sandbox. Capture a weekly readout: baseline vs. actual.
• Days 46-90: The Decision Hold a midway “kill or commit” review. If the KPI is moving, scale the pilot and plan the full rollout. If not, document the lessons learned and redeploy the team.

Stop Rule: If the KPI is flat by day 45, you must either stop or fundamentally change the approach.

Your Board-Level Scoreboard

Report on AI with the same discipline as any other capital investment. Track these six numbers.

• KPI Lift: Pilot use case result vs. the baseline.
• Adoption Rate: % of eligible users using the feature weekly.
• Data Quality Score: A single score for coverage, accuracy, and consent.
• Incident Count: Privacy, bias, or model drift issues and their resolution time.
• Cost-to-Value Ratio: Quarterly value created ÷ quarterly AI spend.
• Time to Decision: Days from idea to pilot and from pilot to scale.

Close: Build a Hype-Proof Engine

If your AI investments can pass the three filters, score high on the investment scorecard, and move a real KPI within 90 days, you will create durable value—whether the bubble inflates or bursts. That is a strategy you can defend in any market.

This week’s challenge: Pick one active or proposed AI use case. Run it through the Anti-Hype Filter and the Investment Scorecard. If it doesn’t score at least a 14, your first job isn’t to build the model; it’s to fix the gaps.