What is the first step a legal team should take when adopting AI?

Define the specific problem you are trying to solve before selecting any tool. According to Kimberly Harlowe, starting with software features is ineffective; instead, frame every initiative around either saving hard dollars or reclaiming high-value internal capacity.

What is an example of a low-risk AI win for a legal department?

Organizing legacy institutional knowledge using standard enterprise environments like Microsoft SharePoint with automated AI fields. When outside counsel submits a memo, an automated prompt extracts metadata and generates a two-sentence summary, transforming a chaotic repository into a searchable knowledge base without external data security risks.

Where should legal teams focus for significant ROI?

Outside counsel spend, where the majority of legal budgets are spent. High-impact use cases include automated deposition summaries and discovery drafts. Aligning outside counsel around standardized AI tools ensures consistency, improves defensibility, and delivers measurable cost reduction.

Large Language ModelsAI Regulation & PolicyTop Companies' AI MovesTop Companies AIJul 29, 2026Jul 30, 2026, 06:31 JST

Legal teams need problem-first AI, not tech-first tooling

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3 Key Points

What happened
Kimberly Harlowe, Senior Director of Litigation Support and Technology at Altria, outlined a framework for corporate legal departments to adopt AI strategically. The approach prioritizes identifying specific business problems before selecting tools, designing modular infrastructure to swap solutions as AI evolves, and scaling through outside counsel partnerships rather than internal-only automation.
Why it matters
Nearly 87% of General Counsel report active AI use, but barely half have a formal rollout roadmap. A problem-driven strategy—starting with low-risk internal wins like organizing legacy documents, then scaling to outside counsel workflows—helps legal leaders earn executive buy-in by anchoring pitches in cost savings or reclaimed capacity, not software features.
What to watch
The framework emphasizes building a 'plug-and-play architecture' that avoids locking departments into single-vendor ecosystems, since AI solutions shift every six months. High-impact use cases for outside counsel include automated deposition summaries and discovery drafts, with success tied to aligning law firms around standardized tools and maintaining human review at each step.

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Context & Analysis

The article frames a fundamental tension in how legal departments approach AI adoption: technology-first thinking versus problem-first strategy. The data point that 87% of General Counsel use AI but barely half have a formal roadmap reveals a gap between early adoption and strategic governance. Kimberly Harlowe's framework addresses this by reorienting legal leaders away from vendor lock-in and toward agility—specifically, designing modular infrastructure that allows tool substitution as the AI landscape shifts monthly. This distinction matters because rigid, multi-year technology plans become obsolete quickly in a market where solutions evolve constantly.

The roadmap also reflects realistic constraints on legal operations: departments are small relative to their budgets, and executive leadership cares about measurable business outcomes (cost reduction, risk mitigation, capacity reallocation) rather than software capabilities. By starting with internal, low-risk automation—such as document organization—teams build both confidence and a proof-of-concept that justifies larger outside counsel initiatives. The emphasis on outside counsel scaling is strategic: it is where legal budgets concentrate, and where standardized AI tools can deliver the largest ROI. Throughout, the framework maintains a human-in-the-loop expectation, positioning AI as a labor-efficiency tool, not a replacement for legal judgment.

FAQ

What is the first step a legal team should take when adopting AI?: Define the specific problem you are trying to solve before selecting any tool. According to Kimberly Harlowe, starting with software features is ineffective; instead, frame every initiative around either saving hard dollars or reclaiming high-value internal capacity.
What is an example of a low-risk AI win for a legal department?: Organizing legacy institutional knowledge using standard enterprise environments like Microsoft SharePoint with automated AI fields. When outside counsel submits a memo, an automated prompt extracts metadata and generates a two-sentence summary, transforming a chaotic repository into a searchable knowledge base without external data security risks.
Where should legal teams focus for significant ROI?: Outside counsel spend, where the majority of legal budgets are spent. High-impact use cases include automated deposition summaries and discovery drafts. Aligning outside counsel around standardized AI tools ensures consistency, improves defensibility, and delivers measurable cost reduction.

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