Baker McKenzie Outlines Multivendor Generative AI Strategy

In a Talking Tech podcast published by Legal IT Insider on June 23, 2026, Baker McKenzie chief innovation officer Ben Allgrove set out the firm's generative AI approach, describing it as "multi-model and multi-vendor" and arguing against long-term vendor lock-in. Allgrove said, "The pace of change... is so fast that tying yourself to a single vendor or a single model at this stage... is not the right strategy," and added, "You're pretty brave if you think you can predict more than six months out from a strategic perspective" (Legal IT Insider).

Per the podcast, Baker McKenzie is standardising tools within practice areas or use cases rather than allowing unrestricted choice, with the aim of reducing lawyer overwhelm and supporting adoption (Legal IT Insider). Allgrove described training priorities as education in the foundations of the technology and how it fits into a lawyer's workflow, rather than focusing primarily on individual tools (Legal IT Insider). He also said the firm seeks to retain control of its orchestration layer while enabling different technologies to plug into that environment, citing reasons including cost management, intellectual property, client relationships and scalability (Legal IT Insider).

Editorial analysis: Companies adopting multi-vendor, multi-model approaches commonly aim to reduce vendor lock-in and preserve flexibility as models and commercial offerings evolve. Editorial analysis: Firms that standardise toolsets by domain or use case typically see higher practitioner adoption because constrained choice lowers cognitive overhead and simplifies governance. Editorial analysis: Emphasising conceptual training rather than tool-specific instruction is an emerging best practice for professional services organisations integrating generative AI, since tool churn increases the value of transferable knowledge.

Indicators observers can follow include whether competing law firms publish similar multi-vendor stances, the emergence of orchestration or integration platforms tailored to legal workflows, vendor responses from legal-specialist AI providers versus general-model providers such as Anthropic, and whether firms move from pilot projects to practice-area-wide rollouts with defined training curricula.