Duracell eliminates billable hour trade-offs with AI-augmented contracting and contract insights

For Duracell's in-house legal team, institutional knowledge had no reliable home. Contract data accumulated across PDFs, emails, and SharePoint folders—formats that made retrieval slow and expensive. When attorneys needed to establish the company's precedent position on a standard clause like indemnity, the process required 40 billable hours combing through documents that outside counsel had already reviewed multiple times. The legacy outside-counsel model compounded this: same Word templates, same review process, with costs rising 10% annually and no improvement in delivery speed or comprehensiveness. As Chief Legal Officer Gary Hood put it, the result was always 'a compromise either on time or comprehensiveness.'

Duracell addressed the problem through two simultaneous moves, both powered by large language models and generative AI via vendor Eudia. First, they embedded an AI-augmented attorney—a human secondee supported by Eudia's AI-Augmented Contracting platform—to handle day-to-day redlines, negotiation support, and escalation on a predictable fixed-fee basis, replacing variable hourly billing. Second, they deployed a custom contract insights solution that used LLMs to extract pricing, rebate, and margin-relevant clauses from the full legacy document corpus and linked those outputs to live Sales and Finance data. The program required no new budget: it was funded by reallocating existing outside-counsel spend, which reduced internal friction and accelerated adoption.

The impact was immediate on the metrics that drove the original problem. Precedent research that previously consumed 40 billable hours now returns results in minutes rather than weeks, enabling legal to brief negotiating teams before deals open rather than after they close. Key outcomes include:

• Data retrieval time: reduced from weeks to minutes across the legacy contract portfolio
• Cost structure: shifted from variable hourly spend to predictable fixed fees, with the program self-funding through commercial insights generated
• Scale: model expanding across business units without additional headcount
• Process: every completed agreement feeds playbooks and decision memory, compounding speed gains over time

• Reallocate before you add: funding AI programs through existing outside-counsel budgets removes approval friction and proves ROI against a known baseline.
• Pair delivery with intelligence: combining AI-augmented human contracting with a contract insights layer converts static history into a forward-looking commercial signal—neither alone delivers the same return.
• Fixed-fee models realign incentives: replacing hourly billing with outcome-based fees eliminates the speed-vs.-comprehensiveness trade-off that defines legacy outside-counsel relationships.
• Start narrow, scale systematically: contracts as an entry point created a low-risk on-ramp; SLA/KPI governance kept performance transparent as scope expanded.