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Understanding Business Context

5 min read

Tech Lead

AI doesn't know that the VP of Product hates microservices. You do. That's why your design doc gets approved and the generic one doesn't.

Solutions Eng

AI can draft a proposal. It doesn't know this prospect is in a 6-month evaluation and needs a very specific POC. You do.

Eng Manager

AI can't navigate 'Engineering wants X, Product wants Y, and we have 3 months.' You're the translator.

Understanding Business Context

TL;DR

  • AI has no access to your org chart, your politics, or your company's history. It designs in a vacuum.
  • "Why did we build it that way?" and "Who owns this?" — AI can't answer. You can.
  • Your value: you know the constraints that actually matter. Use them.

AI is trained on public data. Your company's internal context — who reports to whom, which projects are sacred, what failed last year — is invisible to it. That's a moat.

What AI Doesn't Know

Org Structure and Ownership

  • "Who decides this?" — Product? Engineering? A steering committee? AI doesn't know. It'll give you a generic "typically Product owns..." You know who actually owns it at your company.
  • "Who do I need to align?" — Stakeholders, dependencies, approval chains. Human map.
  • "Why did we choose vendor X?" — Politics. Past failures. Budget. AI wasn't in the room.

History and Precedent

  • "We tried that in 2022. It failed." — AI has no memory of your experiments. It might suggest the same thing again. You're the institutional memory.
  • "That team owns that system. Don't touch it." — Boundaries. AI doesn't know your org's boundaries. You do.
  • "The board cares about X this quarter." — Priorities shift. AI gives generic priorities. Yours are time-bound and political.

Politics and Relationships

  • "Don't propose that — it'll piss off the CFO." — Relationship dynamics. AI can't navigate them.
  • "Engineering and Product are in a cold war over this." — You know how to broker. AI suggests a "rational" solution that ignores the human layer.
  • "We're merging with Company B. Everything is in flux." — Turbulence. AI assumes stability. You live in the chaos.

Why This Matters

Every decision AI "helps" with is made in a vacuum. The best technical answer might be wrong for your context because:

  • The team doesn't have the skills.
  • The timeline is driven by an external event (earnings, a customer, a merger).
  • Someone powerful will veto it.
  • You're deliberately not changing something because of downstream dependencies.

You're not just choosing the right answer. You're choosing the right answer that can actually happen.

How to Use This as a Moat

  1. Annotate AI output with context. AI suggests a design. You add: "We're doing this because X constraint, and we're avoiding Y because of Z history." The annotation is the valuable part.
  2. Own the stakeholder map. Document who cares about what. AI can't. You can. That doc is worth more than any AI-generated spec.
  3. Be the "why" layer. When AI gives you options, you add "given our context, we choose A because B." That's judgment. That's irreplaceable.

Quick Check

AI suggests a microservices architecture. It's technically sound. Why might it still be wrong for your company?

Quick Check

'We tried that in 2022. It failed.' AI suggests the same approach again. What's going on?

Do This Next

  1. Take one AI-generated recommendation (from any lesson). Add three "our company" constraints that would change it. That's your context layer.
  2. Write down one piece of institutional knowledge that would break an AI suggestion. "We can't do X because..." — that's your moat. Share it with your team.