​Build or Buy? The 2027 Legal AI Stack

​How to Evaluate AI Platforms, Custom Solutions, and Technology

​About the Event

​For the past few years, law firms have approached AI adoption by evaluating vendors, purchasing licenses, and experimenting with new tools.

​Meanwhile, legal AI platforms are expanding their capabilities, open-source frameworks offer new options, and firms with internal teams are increasingly able to build around their own knowledge, processes, and requirements.

​This raises an important strategic question for law firm leaders going into 2027:

​Should your firm buy AI capabilities from established vendors, build its own solutions, or combine the two?

​Join Data Strategy Lab for a discussion on the technology and investment decisions shaping the legal AI stack for 2027.

​We'll bring together business strategy, legal governance, and systems engineering perspectives to examine how firms can make informed choices about their next generation of AI infrastructure.

​What We'll Explore

​We'll examine:

• ​Build vs. Buy: When does it make sense to purchase a legal AI platform, extend existing tools, or invest in custom development?

• ​Economics of Ownership: How should firms compare subscription costs, internal engineering investment, implementation effort, maintenance, and long-term total cost of ownership?

• ​Control Over Firm Knowledge and Data: How do different technology choices affect access to internal knowledge, client confidentiality, information governance, and deployment options?

​What You Will Take Away

​By the end of this webinar, you'll have a practical framework for evaluating your firm's 2027 legal AI technology decisions, including how to:

• ​Identify which AI capabilities are best supported by existing platforms and which may justify custom development;

• ​Evaluate the full cost of an AI solution;

• ​Assess the tradeoffs between vendor-managed tools, open-source technology, and internally developed systems;

• ​Understand the organizational capabilities needed to build and maintain legal AI workflows; and

• ​Prioritize technology investments based on workflow value, risk, and long-term flexibility.

​Who Should Attend

​This session is designed for law firm leaders who are already exploring or using Generative AI.

​We welcome participation from:

• ​CIOs, CTOs, and law firm technology leaders;

• ​managing partners and law firm operations executives;

• ​knowledge management and information governance leaders;

• ​legal risk and compliance professionals; and

• ​other AI innovation and legal engineering professionals.

​Speakers

​Andrew Tsintsiruk - Founder & Managing Partner, Data Strategy Lab; Lead Architect, PriviLAB

​His work focuses on connecting models to governed knowledge, evaluating outputs, and building human decision points into workflows.

​Adrian Erlinger - Business AI Strategist, Data Strategy Lab

​He helps organizations turn AI experiments into workflows with owners, review paths, and measures of value.

​Antonia (Toni) Dumas - Responsible AI & Data Governance, Data Strategy Lab; Attorney (privacy, cyber & AI)

​She advises on privacy, security, and trust in digital products and tool adoption.

​About Data Strategy Lab

​Data Strategy Lab helps knowledge-based organizations in the Washington, DC area move from AI experimentation to practical implementation. We combine AI strategy, workflow redesign, systems and data engineering, systems integration, task-specific evaluation, and governance implementation to help organizations build reliable AI capabilities that improve productivity, strengthen knowledge management, and support professional judgment.

​This session is educational. DSL is not a law firm and does not provide legal advice.