Executive Summary

Many organizations lose time and money to preventable project failures because teams fail to surface hidden assumptions and failure modes before execution; this problem hits product, engineering, program management, and cross-functional knowledge-work teams—collectively an addressable market of about 50 million teams and an $18.0B annual spend (roughly $360/year per team) on decision-support and collaboration tooling. High-visibility initiatives, regulated industries, and distributed/hybrid teams are especially exposed because they need auditable decision trails and repeatable risk assessments. Despite existing retrospective tools, there is no broadly adopted way to run systematic, scalable pre-mortems that identify cognitive blindspots and generate concrete mitigation plans before launch. You could build an LLM-enabled "pre-mortem studio" that ingests project artifacts and stakeholder inputs, generates structured failure-mode hypotheses, rates likelihood and impact, proposes mitigation plans with owners and timelines, and exports auditable decision artifacts into existing workflows. This moment is favorable: generative models can now produce structured mitigation plans and detect bias, remote/hybrid work increases demand for written asynchronous decision artifacts, and governance pressures make proactive, documentable risk analysis commercially attractive (market score 90/100, revenue potential 80/100). To stand out, prioritize tight integrations into PM and collaboration tools, explainable outputs with provenance, and validated ROI metrics such as reductions in post-launch incidents or rework costs—competition is medium, so differentiation on privacy, auditability, and measurable impact matters. Be honest about challenges: success requires sufficient project data, careful calibration to avoid noisy false positives, and change management to embed a new pre-mortem discipline, but a focused go-to-market on high-risk programs and regulated customers can produce early, defensible wins.

Market Opportunity

Stop preventable project failures with AI pre-mortems that find blindspots targets a $18.0B = 50M knowledge-work teams x $360/yr average spend on decision-support & collaboration tooling total addressable market with medium saturation and a year-over-year growth rate of 18% (collaboration + decision intelligence convergence).

Key trends driving demand: LLM-enabled automation -- generative models now able to produce structured mitigation plans and detect bias, enabling automated pre-mortems at scale.; Remote/hybrid decision work -- asynchronous teams need written, repeatable decision artifacts and integrated workflows, increasing demand for tooling.; Risk-averse governance -- post-mortem/regulatory scrutiny raises interest in proactive failure-mode analysis and auditable decision records.; Integrations-first adoption -- teams expect tools that plug into Jira/Confluence/Slack, lowering friction for new decision-support products..

Key competitors include Miro, Atlassian (Confluence + Jira), Blameless, Jeli (incident analysis & investigation), Notion / Google Docs (workarounds).

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Stop preventable project failures with AI pre-mortems that find blindspots

Executive Summary

Market Validation

Market Opportunity

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