AI Strategy is Bigger than the AI Tech
When people talk about AI strategy, they often start with the technology. Which model should we use? Should we code it in-house or buy a solution?
But here’s the reality: if your strategy starts at the tech and ends at deployment, it’s already too small.
Why AI Strategy Needs to Start Earlier
AI strategy doesn’t begin with coding. It begins much earlier.
- What business problem are we solving?
- How will we measure ROI?
- What does success look like?
- And other questions…
Without these foundations, teams risk building something that works in a demo but doesn’t deliver business value.
Why AI Strategy Needs to Continue Beyond Deployment
AI strategy doesn’t end at deployment, either.
Unlike a one-time software project, AI is a living system. It requires:
- Monitoring for performance and drift
- Governance for compliance and ethical use
- Retraining to keep models relevant
- User adoption strategies to ensure people actually use it
If you stop thinking strategically at deployment, the system will break down over time, and your return will diminish (or even sink below 0).
Why AI Strategy Must Be Wider Than the App
Finally, AI doesn’t run in isolation. It runs inside your larger technical ecosystem.
That means your AI strategy has to cover:
- Data pipelines (quality, reliability, compliance)
- Integrations (APIs, CRMs, ERPs, ticketing systems)
- Security and privacy (identity, access, policies)
- User workflows (so people adopt it instead of working around it)
- Every system, process, workflow, department, etc. that’s touched by the AI app
Treating AI like a siloed app is a recipe for disappointment and failure.
Bottom Line
AI is powerful — but only if the strategy is bigger than the app.
That means:
- Start early (before coding or design)
- Think wide (cover the ecosystem)
- Plan long-term (lifecycle and operations)
Because in the end, AI strategy isn’t just about AI. It’s about the business and technical ecosystem it lives in.
