As organizations around the world continue to experiment with AI solutions and progress towards implementing AI systems as part of their internal business processes and products, many organizations are confronted with a stark choice: whether to "build" or "buy" an AI solution to meet their needs. 

Unfortunately, there is no easy or "one-size-fits-all" answer to this question. On the one hand, many smaller or less technically sophisticated organizations lack the internal technical capability or resources to build or train their own AI solutions from scratch. Accordingly, these organizations are typically seeking to procure AI solutions from third-party providers. As part of these procurement activities, organizations need to grapple with new twists on a range of typical legal issues – including ownership of AI outputs, re-use of customers' inputs and data as training data for the supplier's other customers, and potential privacy and security concerns where personal information is used as an input – as well as novel legal issues, such as liability for so-called hallucinations in the output of AI models and other potential performance issues. 

On the other hand, larger or technically sophisticated organizations may have the internal capability to build their own tailored AI solutions. In some cases, the "build" option may start with using a publicly available or open-source AI model that the organization deploys, refines and trains itself using its own proprietary datasets. Increasingly, the deployment of certain types of AI solutions can also require the use of specialized computing hardware to achieve the best possible performance. As a result, organizations that are seeking to build and train their own AI solutions are also having to consider whether to purchase and host the necessary computing hardware themselves or whether to obtain access to such hardware via third-party providers (in a manner similar to cloud computing). Consequently, most AI solution "builds" necessarily involve some element of "buy" as well. 

For multinational organizations, the procurement and deployment of AI solutions on a global basis presents additional challenges. The governments of various countries around the world are taking differing approaches to the regulation of AI, ranging from a more prescriptive approach found in the European Union's AI Act to a more targeted or risk-based approach adopted by countries such as Australia. In light of this evolving regulatory landscape around the world, we are yet to see an emerging or settled consensus on what a "market" position is on various contractual terms for the supply of an AI solution. This could be impacting multinationals' "corporate agility" i.e. their ability to quickly adapt and respond to the opportunities offered by AI. Despite the challenges, we are seeing organizations endeavor to address the key contractual risks associated with AI-driven services in many template agreements.

We anticipate that organizations will continue to confront these issues throughout 2025 in this fast-moving space. 

Build vs buy dilemma in the legal industry – key issues to consider 

Author: Břetislav Šimral, Europe Insight & Intelligence Director, Prague

The debate around whether to build or buy generative AI solutions is also a pivotal consideration in the legal industry, where data confidentiality, workflow customization and cost-efficiency are critical. While in-house development offers long-term benefits in scalability, adaptability and control over sensitive processes, this approach is not without challenges, which can incentivize the outsourcing of the desired capabilities.

Size of firm: Smaller firms, for instance, may lack the technical expertise required to design and maintain such systems and the initial investment in infrastructure and talent can be prohibitively high. For these firms, partnering with external vendors to design and build tailored solutions can bridge the gap, allowing them to leverage expert capabilities without the full burden of internal development. Moreover, smaller teams might struggle to allocate resources for training and change management, which are critical for successful adoption throughout the firm.

Cost considerations: While third-party AI solutions provide a rapid entry point and occasionally even better out-of-box integration, they come with significant recurring costs that can easily accumulate into substantial annual operational expenses for large firms. By contrast, in-house development involves reasonable capital investment, and consumption-based operational costs, though these can be scaled predictably. Naturally, smaller firms may struggle with the initial investment and the resources needed to scale adoption effectively.

Data confidentiality: Many third-party tools operate in environments that may not meet the stringent privacy requirements of sensitive legal cases. In-house solutions provide greater control over data processing and ensure compliance with regulatory standards, protecting both client trust and organizational reputation. However, implementing secure, on-premises environments demands robust IT infrastructure, which smaller firms might lack.

Customization: Legal work often demands specific workflows that off-the-shelf tools cannot adequately address without costly modifications. Developing an internal solution allows organizations to tailor workflows and integrate complementary tools, ensuring the system evolves alongside their needs.

Scalability: Without the constraints of per-user licensing models, organizations can expand adoption across their workforce at minimal additional cost. This scalability is vital for embedding AI into daily workflows and maximizing its potential. Still, achieving widespread adoption requires investment in training and change management, which can be resource intensive. The rapid pace of technological advancement further supports the case for building. 

Adaptability: Off-the-shelf solutions often lock firms into specific vendors, limiting flexibility as new capabilities emerge. An internally developed platform can integrate cutting-edge AI models and adapt to evolving needs, ensuring long-term relevance.

Building an in-house AI solution can enhance differentiation and strategic positioning. Proprietary platforms allow firms to stand out as leaders in legal innovation, attract top-tier talent and create new client-facing tools that drive revenue. Smaller firms can mitigate the challenges of building internally by forming partnerships with external vendors or adopting phased approaches that gradually integrate in-house capabilities. This strategy enables access to expertise while distributing the workload and investment over time, making the process more manageable. Nonetheless, smaller firms may find it challenging to capitalize on these opportunities without dedicated resources and clear strategic alignment.

While the benefits of in-house development are significant, this approach is not universally suitable. Smaller legal teams or firms without the necessary technical capabilities may find off-the-shelf solutions more practical in the short term. As tooling becomes more accessible and costs decline, building internally will likely become a viable option for a broader range of organizations. Advancements such as low-code and no-code platforms, pre-trained AI models and modular infrastructure components are increasingly reducing technical barriers, enabling even smaller firms to explore customized solutions with minimal development expertise. For firms with the capacity to invest strategically, prioritizing internal innovation can unlock the full potential of generative AI, delivering transformative value to clients and stakeholders.

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