Build vs. Buy: Generative AI Adoption in Legal Practice
Where I explore law firms' strategic dilemma in AI adoption, weighing the advantages of proprietary development against the convenience of commercial solutions.
Welcome back, my astute legal innovators! As AI capabilities expand at a breakneck pace, a new challenge emerges: should firms build their own AI solutions or partner with specialized vendors? 🏢💻
For those just joining the conversation, buckle up – we're about to embark on a thrilling journey. From proprietary platforms like Wilson Sonsini's contracting solution to commercial partnerships like Allen & Overy's adoption of Harvey, we'll examine how leading firms are navigating this critical decision. 🤖⚖️
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This article marks my third contribution to the National Law Review's Artificial Intelligence newsletter, building upon the themes we explored in the first piece and second piece.
As generative AI becomes increasingly central to legal practice, firms face a critical strategic decision: how should they implement this transformative technology? From Wilson Sonsini's proprietary contracting platform to Allen & Overy's partnership with Harvey, the approaches taken by leading firms highlight the complexity of the build-versus-buy decision. We'll examine the advantages of in-house development, the appeal of commercial solutions, and the various factors that influence this choice - from data security and ethical compliance to resource allocation and long-term competitive positioning. I'll also propose a framework for evaluating which path best suits different types of legal practices.
If this sounds interesting to you, please read on...
Build Proprietary AI Or Buy Commercial Solutions?
Generative artificial intelligence (AI) is reshaping the practice of law, influencing how firms research cases, draft documents, and interact with clients. As client expectations rise and competitive pressures intensify, firms must consider whether building a bespoke system—tightly aligned with their own data, workflows, and strategic priorities—is worth the effort it requires, or whether a vendor-developed tool, with its quicker deployment and ongoing external innovation, is a more practical choice. Both avenues carry implications for data stewardship, regulatory compliance, financial investment, and long-term positioning. In the face of these competing values, one fundamental inquiry guides the decision:
Should law firms build their own proprietary AI models in-house or buy commercial solutions from specialized legal AI vendors?
1. The Case for Building Proprietary Solutions
Firms that build their own AI solution can tailor it to their unique legal culture and specialties. Instead of relying on a generic product, they develop a system that reflects their institutional know-how, internal precedent, and well-honed practice areas. Such a customized model can generate contract language, research outputs, and insights that mirror the firm’s distinctive style and strategic objectives.
Control over data remains a powerful incentive. A proprietary approach allows a firm to keep confidential client information securely within its own environment, mitigating the risk of commingling sensitive documents with a vendor’s broader database. This is particularly appealing in a profession bound by rigorous ethical rules, where the slightest misstep can erode client trust.
Examples of successful in-house builds underscore the potential rewards. In May 2024, Wilson Sonsini Goodrich & Rosati introduced an AI-enabled commercial contracting solution for cloud services companies. Integrated into Neuron, the firm’s proprietary software platform for startups, this AI agent supports commercial contracts attorneys and achieves an accuracy rate of 92% in contract review. Similarly, in August 2023, Dentons launched “fleetAI,” a proprietary version of ChatGPT based on OpenAI’s GPT-4. Developed in collaboration with Microsoft, fleetAI assists lawyers in conducting legal research, generating legal content, and analyzing documents while ensuring that uploaded data is neither used to train the model nor accessible outside the firm’s secure environment.
Still, building an AI solution demands substantial resources. The initial investment in data scientists, legal technologists, and machine learning engineers is significant. The firm must also prepare, clean, and structure extensive training data, a process that can be time-consuming and complex. Technology evolves quickly, forcing continuous refinement and adaptation. For some organizations, this ongoing commitment to improvement may exceed their capacity or appetite, particularly as new AI techniques emerge and threaten to make recently developed models obsolete.
2. The Attraction of Buying Commercial Solutions
The most straightforward route is purchasing a commercial system designed by specialists who have already invested heavily in research and development. Off-the-shelf solutions arrive pre-trained, tested, and often backed by robust security frameworks. This path relieves the firm of building an internal AI team or piecing together data pipelines, allowing them to integrate advanced capabilities into existing workflows more rapidly. Time-to-market advantages can be crucial in a competitive legal environment.
High-profile collaborations illustrate the potential of the buy strategy. In February 2023, Allen & Overy began utilizing “Harvey,” a generative AI platform developed by an external provider, to streamline tasks such as contract analysis, due diligence, and regulatory compliance. Meanwhile, in October 2024, U.S. firm Fennemore Craig merged with Lucent Law and announced a collaboration with OpenAI to incorporate advanced AI technology into their operations. This partnership enhances the firm’s drafting capabilities and pricing decisions, allowing them to offer clients more efficient service models.
Buying is not a panacea. Firms may face integration hurdles, as proprietary workflows must bend to fit a vendor’s platform, potentially sacrificing customization. Moreover, entrusting data to a third party can raise concerns about confidentiality and privilege, despite a vendor’s best efforts to ensure data security. There is also the risk of vendor lock-in, where future price hikes, product overhauls, or shifts in service quality could leave the firm constrained and forced into costly renegotiations or system migrations.
3. Ethical, Compliance, and Governance Challenges
Whether building or buying, a firm must consider an evolving landscape of ethical obligations and professional rules. Maintaining client confidentiality, preventing conflicts of interest, and respecting privilege boundaries are paramount. A proprietary model allows a firm to encode these standards directly into the system. Parameters can be set to filter out confidential information, enforce anonymization, and comply with the procedural and substantive regulations of multiple jurisdictions. Oversight resides in-house, and modifications can be implemented swiftly as legal norms change.
Commercial vendors also prioritize compliance and offer their own safeguards—encryption protocols, access controls, and monitoring features to ensure the responsible use of data. Still, when a firm turns to an external provider, the complexity of vetting these safeguards intensifies. Firms must conduct rigorous due diligence, evaluating how the vendor handles data breaches, adapts to regulatory shifts, and upholds confidentiality. This scrutiny may determine whether the firm is comfortable delegating its ethical responsibilities or prefers the direct accountability of an internally managed system.
4. Talent, Data and Financial Considerations
Cost is a central factor. Building a proprietary LLM entails heavy initial spending on talent, infrastructure, and data curation. Firms building proprietary solutions must ensure that their internal documents are well-structured, current, and comprehensive. Proprietary solutions demand recruiting professionals skilled in both technology and law, a combination that is neither abundant nor inexpensive. The upside is control: the firm owns the intellectual property and can fine-tune the solution indefinitely. Over time, a well-implemented custom model may streamline labor-intensive tasks, improve accuracy, and enhance client satisfaction, justifying the initial outlay.
By contrast, buying a vendor solution often involves a subscription model or licensing fee, reducing upfront costs and providing predictable expenses. Firms gain immediate access to cutting-edge technology without having to develop expertise in-house. By choosing a vendor solution, a firm can delegate most technical responsibilities, needing only enough expertise in-house to evaluate outputs, configure settings, and ensure that the AI aligns with the firm’s objectives. However, over the longer term, reliance on external providers may limit the firm’s ability to influence product direction, lock in favorable pricing, or maintain competitive differentiation. Market trends, rather than the firm’s unique needs, may shape future updates and improvements.
Closing Thoughts
The decision to build or buy generative AI capabilities goes to the heart of a firm’s strategic vision in an age of rapid technological change. Proprietary solutions, as shown by Wilson Sonsini’s AI-enhanced contracting platform and Dentons’ fleetAI, illustrate the potential rewards of differentiation and direct control. Yet these benefits come with high resource demands and ongoing complexity.
At the same time, the buy approach, exemplified by Allen & Overy’s adoption of Harvey and Fennemore Craig’s collaboration with OpenAI, highlights how quickly firms can modernize through external partnerships. The cost is a certain dependence on outside providers and less customization, raising questions of long-term influence and operational freedom.
Ultimately, neither path is universally superior. A global powerhouse with the resources to innovate from within may favor building a proprietary model to create a long-term competitive moat. A mid-sized firm seeking immediate improvements may find a vendor product more pragmatic. In all scenarios, careful assessment of data assets, ethical responsibilities, cost structures, client expectations, and strategic priorities is critical. The right decision ensures that generative AI evolves from a buzzword into a trusted ally, empowering firms to deliver sophisticated, efficient, and forward-thinking legal services.
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