GenAI in Legal Tech: Field Notes from Legalweek and ABA Techshow

The recent Legalweek and ABA Techshow conferences revealed insights into how Generative AI continues to reshape the legal technology landscape. These back-to-back events—Legalweek drawing larger firms and corporate departments, and the ABA Techshow attracting smaller and mid-size practices—provided an interesting view of current AI adoption trends and challenges. In this post, we provide some of our thoughts as we reflect on our industry discussions at the events.

The eDiscovery-Practice Divide

We saw that a notable structural separation exists between eDiscovery specialists and broader legal practitioners in GenAI implementation.

eDiscovery professionals primarily view GenAI as a tool to improve document review efficiency and streamlining production processes. When questioned about wider applications, they typically defer to "other people in the firm" who handle those areas.

Conversely, attorneys focused on litigation or practice management perceive eDiscovery as a separate domain managed by dedicated teams. This division creates a significant disconnect, as eDiscovery data forms the factual foundation for many downstream legal tasks that could benefit from GenAI technologies.

This gap presents an opportunity for eDiscovery specialists to expand their roles beyond traditional document production and become central figures in broader AI-powered litigation practices.

The vendor landscape largely mirrors this divide, with eDiscovery specialists predominantly showcasing GenAI features aimed at enhancing existing eDiscovery workflows rather than bridging the gap to broader legal practice applications.

In-house counsel appeared less constrained by these divisions during conference discussions. Their focus tends toward strategic applications—how GenAI might alter matter management and relationships with outside counsel, potentially bringing more legal work in-house.

The Point Solution Problem

Both conferences showcased numerous new vendors offering narrowly-focused GenAI tools for specific legal tasks—the quintessential "point solutions."

These targeted applications have immediate appeal—they address clear pain points with straightforward value propositions. The accessibility of powerful APIs has enabled startups to quickly build minimum viable products around a narrow use case, with significant venture capital flowing into some of these specialized tools.

However, adopting multiple point solutions creates substantial challenges for law firms. Each system requires separate vendor management, contract negotiation, training, and user onboarding. Security becomes increasingly complex when sensitive client data flows through numerous distinct vendors, each with unique security protocols. Data silos inevitably arise, hindering information flow between different stages of a matter.

Legal work progresses as a continuum rather than discrete tasks. Document discovery directly informs deposition strategy; legal research connects to factual analysis when drafting briefs. Managing dozens of specialized tools creates friction and inefficiency, impeding the comprehensive insights that GenAI could potentially facilitate.

For vendors, the narrow focus raises questions about long-term viability. Despite the legal market's size, targeting a specific niche significantly limits growth potential. Some larger legal GenAI platforms are already discussing expansion into broader professional services beyond legal, suggesting market size concerns. Firms risk disruption if specialized providers disappear or get acquired after resources have been invested in integration.

Adoption Patterns by Firm Size

Perhaps the most surprising observation was the engagement level of mid-size firms (under 300 lawyers) and boutique firms with GenAI strategy. The ABA Techshow revealed numerous smaller firms forming AI committees and developing implementation strategies—similar to where AmLaw firms were approximately a year ago.

These mid-size and smaller practices may move faster from planning to execution for several reasons. Their decision-making processes tend to be more streamlined, with fewer stakeholders needing to reach consensus. Implementation logistics—purchasing licenses, training staff—are simpler at a smaller scale. Additionally, these firms often have greater flexibility to adjust business models around new efficiencies, perhaps experimenting more readily with alternative fee arrangements.

Meanwhile, larger firms remain committed to GenAI but appear to be in a similar strategic position as last year—still working through firm-wide consensus, running pilots, and navigating complex implementation challenges across diverse practice areas and offices.

If smaller and mid-size firms successfully implement GenAI in ways that measurably improve client value, it could significantly influence adoption patterns across the industry, potentially creating competitive pressure on the slower moving larger organizations stuck in the “paralysis by analysis” reality.

Moving Forward

The legal industry has moved beyond initial excitement about GenAI and now grapples with practical implementation questions. Three key challenges have emerged:

1. The structural divide between eDiscovery and broader practice creates friction that prevents maximizing GenAI's utility across the entire litigation lifecycle.
   
   
2. The proliferation of point solutions raises strategic concerns about sustainability, integration complexity, and long-term vendor viability.
   
   
3. The agility of mid-size firms and smaller firms suggests they may chart a faster course in realizing GenAI's benefits, potentially reshaping competitive dynamics.
   
   

The path ahead requires thoughtful strategy, willingness to question established structures, and clear focus on creating tangible value. As GenAI capabilities continue to evolve rapidly, successful implementation will depend on addressing these foundational challenges rather than simply acquiring the latest technologies.

For forward-thinking legal organizations, these observations suggest several strategic considerations:

• Exploring ways to bridge the eDiscovery-practice divide, perhaps through cross-functional AI initiatives
• Evaluating the long-term sustainability of a point-solution approach versus more integrated platforms
• Considering how firm size and structure might be leveraged as either advantage or challenge in GenAI adoption

How firms navigate these challenges and capitalize on emerging opportunities will shape competitive positioning in the months and years ahead.