Joey Organisciak, CEO, Case Compass
Jun 1, 2025
(Part 3 of the series “Cutting Through AI Hype in Legal Tech”)
Why You Need a Scorecard… Now!
Adoption is accelerating: only 14 % of in-house lawyers now say they never use GenAI, down from 45 % last year. (Artificial Lawyer) Yet the risk of wasted effort is growing just as fast: Gartner warns that 30 % of GenAI projects will be abandoned after proof-of-concept by 2025. (Gartner)
Add rising regulatory scrutiny—e.g., the FTC’s $193 k fine and permanent marketing restrictions on DoNotPay’s “robot-lawyer” claims (Federal Trade Commission) and the stakes for choosing the right AI partner have never been higher. A lightweight, repeatable evaluation rubric is no longer a nice-to-have; it’s an operational necessity.
The Six-Step Vendor Evaluation Framework
Step | Core Question | Quick Pass Indicator | Red-Flag Indicator |
|---|---|---|---|
1. Purpose & Provenance | What problem does the AI solve, and what data trained it? | Vendor ties AI to a measurable KPI (e.g., ↓ cycle time) and names its training sources. | Buzzwords only; vague “disrupt everything” pitch. |
2. Privacy & Security | Where does client data flow—and who can see it? | SOC 2 / ISO 27001 in place; no cross-client model training. | Shared public LLM with no tenant isolation. |
3. Explainability | Can my team trace every output back to sources? | Inline citations, confidence scores, audit log. | “Proprietary secret sauce, trust us.” |
4. Human-in-the-Loop | How easy is expert review and override? | One-click edit/approve workflow; feedback improves model. | Output is final by default; edits break automation. |
5. Workflow Integration | Does it live where lawyers already work? | Native plug-ins or open APIs for DMS, email, CMS. | Requires new standalone portal for every task. |
6. Limits & Maintenance | What breaks, how often, and who fixes it? | Clear error metrics; self-service retraining or zero-shot adaptability. | Every tweak triggers a paid SOW or weeks-long retrain. |
The 100-Point Scorecard (DIY Template)
Assign 0 – 5 points for each criterion below (max = 100). Anything under 70 warrants caution.
Purpose Fit (×2)
Data Security & Compliance (×2)
Explainability Depth (×2)
User-Control & Oversight (×1)
Workflow Fit (×1)
Scalability & Maintenance (×2)
(Tip: put this grid in a shared spreadsheet, score vendors live during demos, and keep the sheet for audit trails.)
How Case Compass Measures Up
We built Case Compass to clear this bar by design:
Self-Managed Automation – Intake rules and data extractions are configured by your ops staff—no perpetual pro-services.
Transparency – Every classification shows its source text and weighting.
Secure by Default – Models run in a dedicated tenancy; no client matter data ever trains a shared model.
Rapid Extensibility – New practice areas spin up via drag-and-drop forms; models adapt zero-shot to fresh questionnaires.
Result: clients routinely score Case Compass 90+ on the framework and see double-digit reductions in qualification time within the first month.
Putting the Framework to Work
Run it on live data. Give vendors a sanitized sample matter and score the outputs.
Weight what matters. If confidentiality is paramount, double its point value.
Track before/after KPIs. Cycle-time ↓, accuracy ↑, hours saved, make ROI explicit.
Re-score annually. AI evolves; so should your vendor’s grade.
In Part 4 (final) we’ll cover rollout strategy pilots, change-management, and governance to turn a high score into sustained performance.