In December 2022 I published a UX Collective article on a systematic way to
develop and socialize product strategy. Six steps. Rumelt’s strategy kernel.
VRIO for pillars. A path from diagnosis to communication.
It got 52 claps. I was happy about that.
Then I went back to work and spent the next three years doing product strategy
exactly the way I always had: a Google Doc, a lot of tabs, and somewhere
between four and eight hours I didn’t really have.
Not because I didn’t know the frameworks. I knew them well enough to write
about them. The problem was never knowledge. It was execution.
Most AI-for-PM tools stop at generating a PRD. They help you write faster, not
think straighter. That sounds helpful until you’re in a leadership review and
someone asks the question that turns a polished doc into wet paper:
“What’s our moat?”
If your answer is an adjective — sticky, delightful, AI-powered — you don’t have a strategy. You have a slide.
The hard part of product strategy isn’t drafting. It’s the work that makes a
draft survive that room:
Done properly, this takes hours. Done quickly, it produces analysis that
wouldn’t survive a single hard question. So most of the time, it doesn’t get
done at all — and the roadmap keeps shipping features that don’t compound into
advantage.
There had to be a better way in 2026.
The 2022 piece —
The systematic approach to developing and socializing product strategy — was a field guide. Useful on paper. Still slow in practice. Knowing Rumelt’s
Why / What / How doesn’t magically create an afternoon where you can run VRIO
without interruptions.
In 2026 I shipped what that article implied: an interactive AI workflow that
runs the frameworks instead of summarizing them in another essay.
Write-up: I wrote about product strategy in 6 steps in 2022. I just built an AI that runs it.
Repo: aniganti/pm-superpowers PM Superpowers is a Claude Code / Cursor plugin: skills, one sub-agent, and
a shared library of named strategy frameworks. It triggers in conversation —
you don’t copy-paste a mega-prompt from a Notion doc you’ll forget to open.
It is not a PRD generator. That distinction matters. Faster docs are easy.
Defensible strategy is scarce.
In practice it feels like a strategy-minded chief of staff who’s read VRIO,
aggregation theory, and every pre-mortem post-mortem — and asks the next hard
question instead of dumping a wall of text. The AI doesn’t freestyle a
plausible-sounding essay; it interviews you along the framework’s actual
dimensions.
Brand hook, said plainly: answer “what’s our moat?” in minutes.
The strategic-moat skill walks you through eight moat types, asks for
evidence (not vibes), rates strength, and surfaces deepening opportunities.
You’re pushed to say what product behavior, data, or user dynamics support
each rating — and where the moat is thin.
You leave with a moat profile you can defend: strongest moats, weakest moats,
deepening moves, and a defensibility verdict grounded in your product, not a
generic essay about “network effects in tech.”
That’s the moat machine. Not a slogan — a session you can run before the next
strategy review.
/strategy — the six-step session
/strategy is the orchestrator from the 2022 framework, now interactive:
Grounded in Rumelt’s Why / What / How. The AI interviews you; you don’t
freestyle into a slide deck and call it strategy. Artifacts land in structured
docs you can revisit — not chat scroll you’ll never find again.
Standalone vrio-analysis applies the decision tree to concrete capabilities:
Most teams skip a gate and still call it a pillar. The skill makes skipping
awkward — which is the point.
Moat analysis without competitive intel is fan fiction.
The competitive-researcher sub-agent gathers competitive intelligence via
web research so competitive-landscape isn’t “please paste everything you
know about competitors.” You review the synthesis; you don’t become a tab
farmer. That landscape then feeds VRIO and moat instead of living in a
separate deck nobody opens.
Recommended full strategy pipeline: