Is the 50 hours figure exact to the minute?

No — it is an honest estimate across memo cycles, scenario comparisons, and external conversation prep; the order of magnitude helped us prioritise process.

Can AI replace customer interviews?

No. It can speed drafting and structured contradiction; ground truth (interviews, usage, commercial signal) remains essential.

What is the first trap to avoid with LLMs?

False consensus: smooth prose that hides unresolved conflicts — force objections first, trade-offs in the same paragraph, and a human editor.

How can I apply this tomorrow without a proprietary tool?

Write a one-page bet, run a role-based debate, extract three kill criteria, schedule one interview on the riskiest assumption, archive the memo.

How we saved 50 hours of strategy work with AI (field notes)

What large language models actually replaced in our workflow — and what still requires human judgment, ownership, and ground truth.

The problem: iterating on strategy without enough structured friction

We were iterating quickly on positioning, narrative, and decision memos for an early product. The bottleneck was not typing speed — it was structured disagreement and compression: turning messy debates into crisp trade-offs without losing nuance.

We wanted a repeatable way to simulate challengers without booking five calendars every time we changed a paragraph.

Why it fails: “strategy work” without a frame, and LLM failure modes

Hours disappeared into workshops that circled the same questions, documents that mixed vision and assumptions, and rewrites for different audiences that drifted in meaning.

Where AI failed without a frame: false consensus (prose hiding conflict), generic advice, overconfidence if sources are not pinned. The fix is process: separate facts, assumptions, and decisions — and keep a human owner per block.

A concrete method: where AI helps, workflow, rules, metrics

Acceleration when constraints are clear: scenario trees, role-based critique (legal, tech, distribution — verify afterwards), compressing notes into a one-page memo, language variants for the same decision.

Workflow: Inputs (problem, ICP, evidence, non-negotiables) → Simulation (multi-angle challenge, not one chat turn) → Decision (owner + kill criterion) → Ground truth (interviews, data, commercial signal). If step 4 disappears, you built a story — not a company.

Rules: objections first, trade-offs in the same paragraph, human editor deleting 30–40% of the output.

Metrics (loose): cycle time, structured iteration count, surprise rate. Time saved is real; quality still correlates with how hard you argue with the draft.

Example: privacy, compliance, and ethics

Do not paste confidential third-party data you are not allowed to process. Treat the model as a brainstorming partner, not a vault. For regulated domains, run human review — always.

What to do now (five steps)

Write a one-page bet (see our stress-test guide).
Run a structured debate with explicit roles — not a single prompt.
Extract three kill criteria you would accept as real.
Schedule one customer conversation that targets the riskiest assumption.
Archive the memo so the next pivot does not restart from zero.