AI Success Depends on Behaviors, Not Just Systems
60% of AI projects fail due to poor data foundations, but also due to behaviors: whether the org can make data-driven decisions when they're inconvenient, whether data governance has teeth under pressure, whether "data quality" is real or performative.
We measure 113 signals across data infrastructure (systems), governance processes (how data should flow), and decision-making behaviors (whether data actually drives decisions). Pre-LOI ready for PE firms. <2 weeks. $12K-$15K (Rapid Scan) or $45K-$65K (Full TRI).
Why AI Projects Fail and Why It's Often Human, Not Technical
You approved an AI initiative 12 months ago. The team is working hard. Demos look promising. Then reality hits: data exists but teams don't trust it, governance exists but gets bypassed under pressure, "data-driven decisions" happen when convenient but not when they conflict with exec intuition. The technical plan was sound. The behavioral reality killed it.
What We Measure and Why Behaviors Matter
We collect evidence on three layers, because AI outcomes are rarely "just technical."
Systems (What Exists)
Data quality, lineage, infrastructure, ML platform readiness, pipelines, feature stores, model registries, monitoring tools, integration architecture.
Processes (How Work Should Run)
Data governance frameworks, model validation procedures, deployment pipelines, monitoring protocols, privacy controls, compliance reviews, ROI measurement.
Behaviors (How Work Actually Runs)
Are data-driven decisions made when inconvenient? Does governance have teeth when it conflicts with shipping? Is data quality real or performative? Can teams say "this AI use case isn't ready" safely?
Why Behavioral Evidence Matters for AI
A target with strong data infrastructure but weak decision-making culture will fail at AI because models get deployed without validation, governance gets bypassed under pressure, and data quality becomes a checkbox exercise. Traditional firms note these risks; we score them as measurable signals inside PE due diligence.
Three-Layer AI & Data Readiness Assessment
You cannot fix what you cannot measure. We deliver quantified readiness scores across 113 signals, 16 dimensions, built on the same Signalomix TRI platform co-developed with AWS AI specialists. Evidence-based scoring benchmarked against industry cohorts. Portfolio-grade format comparable across deals.
Three Decisions This Assessment Enables for PE Firms
- Pre-LOI screening: Can the target's AI ambition become a competitive moat, or will it burn cash with zero ROI? Predict AI success before you fund it.
- Fix-first prioritization: Ranked list of capability gaps (data quality, governance, infrastructure, culture) with mitigation cost and timeline, so you know what to fix in the first 30/60/90 days.
- IC scorecard: Risk index, top drivers, deal impact, not an 80-page narrative. Portfolio-grade format you can compare across deals.
Framework validated by industry-leading AI experts and battle-tested across 50+ enterprise assessments
16 Dimensions Across Data, Platform, Governance, and Organization
We score your AI readiness across four areas that make or break AI programs: data foundations, platform & engineering, governance & risk, and behavioral culture, broken into 16 detailed dimensions.
Each dimension produces an evidence-based score benchmarked against industry cohorts. We don't simulate scenarios, we measure actual capability across systems, processes, and behaviors to predict AI readiness in PE diligence context.
Pre-LOI AI Risk Assessment: Can the Target Predict AI Success?
For PE firms, the question isn't just "does AI exist?", it's "will AI initiatives deliver value post-close, or burn $2M-$8M with zero ROI?"
What We Identify Pre-Close
- High AI ambition + low data quality = $300K-$1M failed POCs
- Poor data governance → regulatory blockers (GDPR, CCPA)
- Fragmented data architecture → integration cost 2-3x estimates
- Unvalidated use cases → AI spend with zero ROI
- Weak ML infrastructure → models never reach production
- Data culture risk: "data-driven" when convenient, not when hard
What You Get for IC
- AI readiness risk index (quantified, benchmarked)
- Top 5 risk drivers that change deal model
- AI capability validation: competitive moat vs. marketing narrative
- Fix-first plan: 30/60/90 priorities, mitigation cost, timeline
- Behavioral evidence: can they execute data governance under pressure?
- Portfolio-grade format: comparable across deals, trackable post-close
What You Get: IC Scorecard + Execution Agenda
IC-Ready Risk Scorecard
AI readiness risk index, top 5 drivers, deal impact translation, behavioral evidence, not 80-page narrative. Portfolio-grade format comparable across deals.
Fix-First Plan (30/60/90)
Prioritized remediation roadmap: data quality gaps, governance enforcement, infrastructure build-out, culture fixes, with mitigation cost, timeline, and ownership.
Behavioral Evidence Collection
Decision velocity analysis, governance enforcement patterns, data culture assessment, scored signals, not soft commentary. Can they execute data-driven decisions under pressure?
PE Impact: Prevent $300K-$1M in Failed AI Spend
Signals Measured
Data quality, governance, infrastructure, ML capability, and decision-making behaviors, scored, benchmarked, and rolled up to risk index
IC-Ready Output
Pre-LOI screening or post-LOI deep dive. Risk scorecard + fix-first plan + 30/60/90 operating priorities, diligence that becomes execution agenda
Human Risk Quantified
Can they make data-driven decisions under pressure? Does governance have teeth? Is data quality real or performative? Scored, not noted.