Detect and prevent overconfidence-driven bad decisions with decision-health tooling

Executive Summary

Decision-makers in mid-market and enterprise companies often make overconfident choices without structured evidence, creating fragile bets that risk costly failures; risk, product, and finance teams shoulder the pain but lack tooling to spot and prevent these patterns. Boards and investors are pushing for better decision documentation and post-mortem discipline, yet most organizations still rely on ad-hoc notes and dashboards that don’t flag overconfidence or cross-system fragility. Build a decision-health platform that ingests standardized event schemas and cloud warehouse data to score decisions on confidence vs. evidence, detect fragility across systems, surface risky decisions, and automate remediation workflows into Slack, BI, and ticketing systems. Include decision templates, confidence/evidence dashboards, anomaly detectors, and automated post-mortems so detection converts into repeatable, human-in-the-loop interventions. The addressable market is roughly $12.0B (200,000 mid-market and enterprise companies × $60K ACV) and is attractively timed by three trends: a move from descriptive to prescriptive tooling, schema standardization enabling cross-system diagnostics, and investor/board pressure for documented decision hygiene. With a market score of 90/100 and revenue potential rated 80/100, there’s a sizable opportunity to capture ACV from buyers prioritizing downside protection. You can stand out by focusing on cross-system fragility detection through DWH-native integrations and by selling measurable ROI — for example, a $60K ACV is easily justified if the product prevents a single $1M+ decision failure — rather than another analytics dashboard. Be honest about challenges: integration complexity, proving signal fidelity to skeptical stakeholders, and change management mean early pilots should target compliance-driven teams or functions with clear, high-cost decision failures.