Duracell eliminates 40-hour precedent searches and shifts to fixed-fee contracting with Eudia AI

Duracell deployed Large Language Models & Generative AI for Contract Lifecycle Management in Corporate Legal & In-House. As reported by www.eudia.com: Reduced from 40 billable hours to minutes precedent search time.

Maintained by Peter Korpak, Lead EditorHow evidence is checked
Reduced from 40 billable hours to minutesPrecedent Search Time
Shifted from variable hourly to predictable fixed-feeBilling Model
Scaling across business units with zero headcount additionsHeadcount Impact

Source-reported figures — cited source: www.eudia.com

What Duracell was trying to fix

For Duracell's in-house legal team, decades of contract history had accumulated across PDFs, emails, and SharePoint folders — accessible by format, not by meaning. When counsel needed to establish the company's prior position on a clause like indemnity before entering a negotiation, the only path was a manual review of documents already paid to be reviewed multiple times before. That process consumed 40 billable hours per precedent search. The underlying law firm model compounded the problem: same Word templates, same review workflow, just a 10% cost increase annually with no improvement in delivery speed or comprehensiveness. As Chief Legal Officer Gary Hood put it, the result was "slower work product and typically a more expensive one… a compromise either on time or comprehensiveness."

What Duracell deployed

Duracell deployed Eudia's AI-Augmented Contracting platform, combining two capabilities built on large language models and generative AI. The first was an embedded AI-augmented secondee handling day-to-day redlines, negotiation support, and progression to signature on a predictable fixed-fee basis — replacing the variable hourly model with outcome-based delivery. The second was a custom contract insights solution that extracted pricing, rebate, and margin-relevant clauses from the full legacy contract corpus and linked them to Sales and Finance data. Both solutions integrated with Duracell's existing document environment rather than requiring a migration. Critically, the program was funded by reallocating existing outside-counsel spend rather than requesting a new technology budget line — removing the typical procurement barrier to adoption.

Results

The transformation eliminated the 40-hour precedent search entirely: legal can now retrieve clause history and prior positions in minutes rather than weeks. Key outcomes include:

  • Precedent search time: reduced from ~40 billable hours to minutes
  • Billing model: shifted from variable hourly spend to predictable fixed fees
  • Headcount: scaling across business units with zero additions
  • Budget: program self-funded through reallocation of outside-counsel spend

Beyond speed, the contract insights layer now enables pre-negotiation posture analysis — surfacing pricing, rebate, and margin data before discussions begin — giving Sales and Finance decision-ready signals without routing requests through outside counsel.

Key Takeaways

  • Fund AI adoption from existing spend: routing outside-counsel dollars into AI programs avoids new budget approvals and creates immediate ROI accountability.
  • Unlock institutional memory before replacing process: converting legacy contract history into a searchable insights layer is a prerequisite, not an afterthought — it's what makes AI-augmented delivery credible.
  • Fixed-fee structures align incentives: billing by outcome rather than hours ties legal's performance to business speed, not document volume.
  • Embed humans alongside AI for higher-stakes work: the secondee model keeps judgment where it matters while automating repetitive redline and progression tasks.
  • Start narrow, then compound: beginning with contracts created a low-risk entry point; each new agreement now improves playbooks and sharpens future negotiation posture.

Evidence for Duracell's Contract Lifecycle Management deployment

Reported outcome metrics
3 cited below
Cited source
www.eudia.com
Last updated
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