Bmc AI Assisted Draft Starting

BMC AI-Assisted Draft Starting

Purpose

A blank canvas is slow to fill and tends to produce vague first drafts, because a team spends its first real energy just generating plausible- sounding content rather than debating it. This skill inverts the order: feed the target company's own public material to an AI model before the session, get a fast first-draft canvas and brand read, and have the team's actual work be editing that draft — accepting, rejecting, correcting — rather than generating one from scratch. Use this as a pre-session preparation step, immediately before bmc-session-facilitation-design.

Anchored in research

The source practitioner, identified independently: an executive advisor on strategy, innovation, and communication, a certified Business Model Generation (BMG) facilitator/coach, a contributor to Value Proposition Design (the well-known Osterwalder/Strategyzer book), and a workshop leader for Strategyzer's own "Accelerating Innovation with AI" program — i.e. a real, Strategyzer-affiliated practitioner, not an unverifiable name. The specific "start from edit" phrase and exact workflow described here could not be independently located in public material beyond the owner's own account — if the owner attended or watched this specific workshop personally, that's a reasonable first-hand source; if secondhand, describe it more cautiously as a technique demonstrated in a Strategyzer AI workshop rather than a verified direct quote. Either way, the underlying mechanism (AI-generated first draft, human edits rather than generates) is sound practice independent of the specific attribution.

Method

1. Gather public material on the target company before the session — its website, any public product pages, visible pricing, marketing copy, and (if available) recent public statements or press.
2. Generate a fast first-draft brand audit and BMC sketch from this material. Ask explicitly for: a short brand-positioning read (what the company seems to claim about itself, in its own words vs. what actually seems differentiated), and a draft canvas across all nine blocks, each entry marked with a rough confidence level (clearly stated in the public material vs. inferred vs. genuinely uncertain).
3. Bring the draft to the session as a starting point, not a deliverable. State to the team explicitly, before anything else: this is a fast, AI-generated first pass built only from public material — it will contain errors, blind spots, and things the team knows are wrong. Its only job is to save the team from a blank page.
4. Run the session as an edit pass, not a generation pass. For each block, the team's first move is to accept, reject, or correct what's already there — not to start fresh. This surfaces disagreement faster than blank-page brainstorming does, because reacting to a concrete (even wrong) claim is easier than generating one from nothing, and a wrong AI guess often provokes a sharper, faster correction than an open question would.
5. Pay closest attention to confident-sounding AI content that's actually wrong — the biggest risk of this method isn't obviously uncertain content (the team will naturally scrutinize that), it's plausible-sounding, confidently-stated content that's subtly incorrect and gets accepted without real scrutiny because it reads well. Explicitly ask the team, block by block: "if this were actually wrong, would we have caught it?"
6. Treat low-confidence-marked entries as session priorities, not afterthoughts — these are exactly the blocks where the team's real, non-public knowledge adds the most value over what AI could infer from public material alone.
7. Don't skip bmc-canvas-diagnostic-reading at the end just because the canvas started from a draft — an AI-originated starting point doesn't reduce the need for the pack's normal quality checks; if anything, the DR-05 "defensive canvas" signal (a team accepting everything too readily) deserves extra attention here, since editing an existing draft can create a false sense that the content has already been vetted.

What this skill does NOT do

• Doesn't replace bmc-session-facilitation-design's session structure — it's a preparation step that changes what the team starts editing, not a replacement for how the session itself is run.
• Doesn't work well without genuine public material to draw from — a company with almost no public footprint (early-stage, pre-launch, or deliberately private) won't produce a useful draft this way; use the normal blank-canvas approach instead.
• Doesn't remove the need for real customer/market evidence — an AI-generated draft is built from public material only, which is a different and much weaker evidence source than the validated, evidence-graded canvas bmc-canvas-diagnostic-reading expects by the end of the process.

Refinement notes

• How much does this actually speed up a real session, in your experience — is it the first 20 minutes, or does it change the whole session's pace?
• What's the clearest example of a confidently-wrong AI draft entry that a team almost accepted without scrutiny?
• Does this work as well for a company you (the facilitator) already know well, or is its real value specifically for an unfamiliar target?

Continue from here

• Use immediately before: bmc-session-facilitation-design/SKILL.md.
• Still required afterward: bmc-canvas-diagnostic-reading/SKILL.md — an AI-originated starting draft doesn't reduce the need for the pack's normal quality checks.
• Related in another pack: ../../../../ai-strategy-and-governance/skills/workshop-to-agent-productization/SKILL.md — a different application of AI-assisted content reuse, for productizing existing material rather than drafting new analysis.
• This pack's shared guardrails: ../../CLAUDE.md

References

• ../../references/bmc-source-material-notes.md — source material background
• ../../../../ai-strategy-and-governance/references/ai-native-reshuffle-heuristics-research.md — selection and grounding notes for this skill and its siblings
• ../../CLAUDE.md — this pack's shared guardrails