Context Engineering for Leaders

You've probably heard someone in your company brag about a perfect prompt. The right words, in the right order, and the AI gave them exactly what they wanted.

That works for small things. It does not work for the way your business needs to use AI.

The shift that matters is from prompt engineering — chasing the right phrasing — to context engineering — preparing the information the AI has to work with. The companies winning with AI right now have figured out that the phrasing is the easy part.

The contractor analogy

Imagine you hire a contractor to renovate part of your office. You can give them a beautifully worded instruction:

"Build out a modern, professional workspace for our marketing team."

That sentence is fine. It's also useless on its own.

A real renovation requires:

• The blueprint of the existing space — the data the AI is working from
• The brand standards and budget — the constraints
• The history of how the space has been used — the legacy state
• The schedule and approvals — the process around the work

Prompt engineering is the sentence. Context engineering is the briefing pack.

The question I keep getting from executives

The most common thing executives ask me isn't "What model should we use?" or "How does this work under the hood?"

It's: "Where do I actually begin?"

That question is a symptom. You're not stuck because you don't have the right tool. You're stuck because the landscape is moving so fast that any answer feels like it might be wrong by next quarter — so you do nothing.

The answer isn't a grand strategy. It's a small, specific move.

A small, specific move: the Two-Mind Workflow

Don't make a real business decision with one AI tool. Use two, and let them check each other.

Mind 1 — the grounded one

Pick a tool that only answers from your own documents (an example is Google's NotebookLM). Upload your strategy docs, your customer data summaries, your historical reports. Ask questions. It only answers from what you fed it, with citations. No invention.

Mind 2 — the broad one

Pick a general AI assistant (an example is Claude or Gemini). Use it to think more widely — bring in industry context, alternative perspectives, "what would competitors do" angles.

Then read both answers side by side. Where do they agree? Where do they diverge? That comparison is the work.

The mess you'll find: the brownfield reality

If your business is more than three years old, your data is messy.

You have customer records with three different spellings of the same company. You have spreadsheets from 2019 that someone keeps editing. You have policy documents that contradict each other.

When you point an AI at that, you don't get a smart system. You get a confidently wrong system. The industry calls this context rot — the AI is reading garbage, so it produces garbage.

Two things to do about it:

1. Clean your data first. Before you spend on AI, spend on data hygiene. Pick the five or ten datasets the AI will actually use, and get them right.
2. Teach the AI your language. Every organization has its own acronyms, product names, and internal vocabulary. The AI doesn't know any of it until you teach it. Treat this as onboarding, not configuration.

Redefining ROI: stop counting saved minutes

The most common reason executives give up on AI is: "It takes me longer to ask the AI than to just do it myself."

That comparison is wrong. You're comparing the wrong things.

Old way — serial work

You spend two hours pulling information together. While you do that, nothing else gets done.

New way — parallel work

You hand the information-gathering to an AI. While it works, you draft the actual decision, talk to a stakeholder, or move the next item forward.

You didn't save two hours. You doubled your week's capacity by running two work streams at once.

The metric isn't time-per-task. It's how much work happens in parallel.

The human is still the conductor

Nothing in this article suggests removing your people from the work. The opposite.

Once you have a clear question (the prompt) and a clean, scoped knowledge base (the context), a trained human becomes the conductor — the one who reviews the AI's draft, catches the mistakes, refines the input, and runs the next iteration.

This is what the industry calls Human-in-the-Loop, and it's how production AI actually works inside serious organizations.

The AI does the grunt work. The human owns the judgment.

The companies that move from "playing with AI" to "AI as a production capability" aren't the ones with the best prompts. They're the ones who took the context seriously.