Maturity assessments for in-house teams
See where yourcontractmanagementreally stands.
Four focused assessments, built for in-house legal, finance, and ops leaders who want a defensible read on their data, people, and financial readiness for the contract operating model ahead.
- ~45
- Anchored questions
- 0–4
- Per-question scoring
- Shareable readiness report
The evidence
The number you're shown, and the one you'll live with
Independent research — weighted over vendor claims — on what autonomous AI actually delivers over real business data. The gap between them is set by the data underneath.
91% → 17%1
Text-to-SQL accuracy: same class of models on a clean academic benchmark vs. real enterprise data
~9%2
Of annual revenue lost to poor contract data and management — up to 15% on complex portfolios
17–33%3
Hallucination rate of legal-research AI marketed as “hallucination-free,” in the first independent evaluation
~95%4
Of enterprise generative-AI pilots showed no measurable P&L impact
The maturity climb
From ad hoc to optimized.
Every contract operation sits somewhere on this ladder. Here is what each rung looks like — and the climb the assessments map.
Ad hoc
0+ / 100
Contracts live in inboxes and shared drives. Every renewal is a surprise.
Emerging
25+ / 100
A few templates and a tracker exist — but they lean on the people who made them.
Defined
50+ / 100
Process is documented and followed. Data is structured enough to report on.
Established
75+ / 100
Systems enforce the process. Metrics are trusted; risk is priced, not guessed.
Optimized
90+ / 100
The operating model runs itself and improves — AI and automation compound.
The assessments score where your contract operation stands — and the gap to the next rung. Your report maps how you close it.
See how you climb↓The full product line
See where you stand. Then move.
Three scored assessments, a free starting point, and a bundle for teams ready to tackle all three.
Individual assessments
One scored survey, a full report, and concrete next steps
Bundle
All three assessments together — one price, one synthesis
Every assessment is built on the same foundation — the Bedrock Framework: five pillars of contract operations →
Sources
- 1. In independent testing, the same class of AI models answered 91% of natural-language data questions correctly on a clean academic database — and 17–21% on real enterprise data. Lei et al., Spider 2.0 (ICLR) — The gap is a data-environment problem — schema-linking, dialect confusion, context overflow — not a model defect.
- 2. Poor contract data and management erodes roughly 9% of annual revenue — and up to 15% on large, complex portfolios. World Commerce & Contracting benchmark
- 3. In the first independent, pre-registered evaluation, legal-research AI marketed as “hallucination-free” still hallucinated on roughly one in six to one in three queries. Magesh et al., Stanford RegLab / HAI, Journal of Empirical Legal Studies (2025) — Grounding in a trusted document set reduced hallucinations versus a raw model — it did not eliminate them.
- 4. About 95% of enterprise generative-AI pilots have shown no measurable P&L impact. MIT NANDA, The GenAI Divide: State of AI in Business 2025 — MIT attributes this mostly to an integration and organizational “learning gap,” not raw model quality — this is the value ceiling, distinct from the data-quality accuracy ceiling. It is not evidence that AI does not work.
Independent, peer-reviewed, and pre-registered sources are weighted above vendor-sponsored ones; vendor figures are labeled as claims. Figures current to mid-2026.