Assessing investment readiness
Gives the investment committee an evidence-based view of likely twelve-month delivery, distinguishing credible commitments from claims that exceed demonstrated capacity.
— Product & Tech
Product roadmap realism check
Tests product roadmap commitments against engineering capacity, delivery history and dependencies to give investment committees a defensible view of what is likely to ship within twelve months.
- Product & Tech
- Deterministic scoring
- Traceable reasoning
- Runs anywhere
2–3 weeks → ~25 minutes
For a typical multi-document review workflow
No coding required
- Input
- Product roadmap and committed delivery dates
- Output
- Evidence-based roadmap realism assessment
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~2–3 weeks per run
— USE CASES
What people use Product roadmap realism check for
Testing an ambitious roadmap
Assesses whether available engineering capacity supports the promised scope and timing. Highlights where delivery expectations rely on assumptions that need stronger evidence.
Exposing delivery accountability gaps
Identifies planned work without clear ownership or adequate dependency coverage, showing where apparently firm commitments carry unresolved delivery risk.
Challenging pitch deck claims
Compares stated product commitments with backlog evidence and shipped-on-time history, giving advisers a defensible basis for challenging delivery claims.
What it works from
- Product roadmap and committed delivery dates
- Product backlog and scope estimates
- Engineering capacity and allocation data
- Historical delivery and shipped-on-time records construction
What you get back
- Evidence-based roadmap realism assessment
- Engineering capacity versus committed scope analysis
- Ownership and dependency coverage gaps
- Defensible twelve-month delivery outlook for the investment committee
— HOW IT BEHAVES
How Product roadmap realism check produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Scored against your criteria
Each process step is assessed against criteria you control and weight, so the same standard applies to every process step in the end-to-end process.
Calculated per process step, not estimated
The figures behind each process step are computed by formula during the run, so the arithmetic is identical every time and can be checked line by line.
Numbers lifted out of process documentation and run data
Figures buried in narrative process documentation and run data are extracted as data you can compute and compare with.
Process documentation and run data pulled into one schema
Every process step is captured in the same field structure, so records drawn from different documents and sources stay comparable.
Whole sets of process documentation and run data at once
All of your process documentation and run data is processed as one set, so patterns across documents surface instead of being read one file at a time.
Live research on the end-to-end process
Current external sources on the end-to-end process are researched during the run rather than recalled from training data, and every source travels with the output.
Why this is expensive by hand
Product roadmaps often present ambition as commitment, leaving investment teams to judge delivery prospects without a clear link between promised scope and engineering capacity. In practice it shows up as assessing investment readiness: gives the investment committee an evidence-based view of likely twelve-month delivery, distinguishing credible commitments from claims that exceed demonstrated capacity. It is the kind of work that decides whether a recommendation survives scrutiny — and the kind that quietly eats 2–3 weeks of senior time whenever it comes round.
How this Skill produces it
As a Skill, the work is already sequenced. You bring the evidence, and the run produces evidence-based roadmap realism assessment plus engineering capacity versus committed scope analysis. What sits between input and output is the codified method: thresholds, sequencing and the points where a human confirms a call — all of it visible and editable in the Skill. In effect, 2–3 weeks of senior time compresses into ~25 minutes — and the output is comparable across clients, quarters and colleagues instead of shaped by whoever ran it.
Who it's for
- Independent consultants codifying their own methodology
- Strategy and transformation teams standardising delivery
- Internal advisory functions under pressure to produce faster
- Operators who need defensible output, not a one-off chat answer
Run it in Skillsize — or export it anywhere
Product roadmap realism check exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
ChatGPTClaudeCopilotAI Products (MCP)
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