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Technical Strategy With AI in Mind

5 min read
Tech LeadCto

Tech Lead

Your strategy doc should answer: Where does AI help? Where does it not?

Cto

Board wants an AI strategy. Give them one that's grounded in reality.

Technical Strategy With AI in Mind

TL;DR

  • AI changes implementation speed, not strategic direction. Your job is still: what we build, why, and in what order.
  • Include AI explicitly: approved tools, integration points, and where you're not using it.
  • Avoid two extremes: AI-washing (everything is AI now) and AI-avoidance (pretend it doesn't exist).

Technical strategy used to mean: architecture, tech stack, hiring, and roadmap. It still does. Now add: how AI fits into all of it. Not as a buzzword. As a real force that changes how fast you ship, what you can attempt, and what risks you take.

What Stays the Same

  • Customer problems don't change. You're still solving for users, performance, reliability.
  • Trade-offs don't change. Build vs. buy, speed vs. quality, flexibility vs. simplicity. AI might shift the curve; the dimensions stay.
  • Ownership and accountability don't change. Someone still owns each system. AI doesn't run your infra.

What Changes

  • Throughput assumptions. If implementation is 1.5–2x faster, your roadmap can be more ambitious — or you can do the same with fewer people. Make the choice explicit.
  • Skill mix. You need fewer people who "write code from scratch" and more who "orchestrate AI and verify output." That affects hiring and leveling.
  • Vendor and tool landscape. Cursor, Copilot, Claude Code, Gemini — each has trade-offs. Your strategy should name what you standardize on and why.
  • Risk surface. AI can introduce bugs, security issues, and IP leaks. Your strategy should address governance.

Writing an AI-Aware Technical Strategy

1. Tools and standards

  • What AI coding tools are approved? What's forbidden?
  • Do you have internal prompt libraries or patterns? Who maintains them?

2. Integration points

  • Where does AI touch your stack? Code gen, docs, tests, infra-as-code?
  • What's automated vs. human-in-the-loop?

3. Governance

  • What code requires human review before ship?
  • How do you handle proprietary data and AI? (Hint: don't put it in public models.)

4. Honest boundaries

  • Where are you not using AI? Legacy systems, safety-critical paths, regulatory contexts. Be explicit.

Avoiding Strategy Theater

  • Don't AI-wash. "We're an AI-first company" means nothing if everyone's still manually debugging.
  • Don't over-promise. "AI will 3x our velocity" — maybe. Pilot first. Commit to numbers when you have data.
  • Do have a point of view. "We're standardizing on X for code gen because Y." That's strategy. "We're exploring AI" is not.

AI Disruption Risk for Technical Leaders

Mostly Safe

SafeCritical

AI automates routine work. Strategy, judgment, and human touch remain essential. Mostly safe for those who own the outcomes.

Manual process. Repetitive tasks. Limited scale.

Click "With AI" to see the difference →

Quick Check

What remains human when AI automates more of this role?

Do This Next

  1. Update your tech strategy doc — Add a section: "AI in our stack." One page. Tools, governance, boundaries.
  2. Identify one pilot — A team or project where you'll measure AI impact. Define success. Run for one quarter.
  3. Communicate it — Share the strategy with your org. Make it discussable. Revise based on feedback.