Please enjoy this blog post authored by Alina Antonov, Senior Information Technology Project Manager, Akin Gump Strauss Hauer & Feld LLP.
Introduction
At ILTACON 2024, I spent time walking the exhibit hall and asking colleagues from other firms the same questions:
“What’s your AI implementation status? What’s working well, and what challenges are you seeing with Generative AI?”
Most of these conversations centered around generative AI tools - not proprietary or custom-built solutions. So, while this article focuses mainly on GenAI, many of the points and lessons shared apply just as well to other AI technologies, including those developed in-house or tailored for specific legal workflows.
The answers were honest, varied, and thought-provoking. What stood out most was that the challenges firms face differ a lot based on size. Those hallway conversations, along with follow-up research, led me to write this article.
The legal industry is going through a digital transformation like many other sectors, and artificial intelligence (AI) is playing a central role. From document review to litigation analytics, AI is changing how we work. But implementing it is not simple. It is a multi-step process, and success depends on the size, structure, and readiness of the firm.
This article walks through the core parts of AI implementation and compares what small and large firms experience. While ILTA doesn’t provide a strict definition for firm sizes, we’ll use a working assumption:
• Big Law = 700+ lawyers (based on ILTA community data).
• Small to Midsize Firms = fewer than 100 lawyers (ILTA Small-Mid-Size Firms Community).
What Goes into AI Implementation?
Adopting AI in a law firm isn’t just about buying a tool. It’s a strategic process that touches technology, people, and workflows. Here are the main pieces involved:
Choosing the Right AI
Not every AI tool fits every firm - and that’s okay. Some tools focus on contract review, others on legal research, time tracking, or drafting. The first step is to have a strategic conversation: What specific problem are we trying to solve?
Start by identifying key pain points or goals - like saving time on repetitive tasks or improving accuracy in client work. Then look for tools that match those needs. Evaluate vendors based on how their solution fits your workflows, how easy it is to use, and what risks or changes it may bring.
Choosing AI isn’t just a tech decision - it’s about finding the right solution for the right problem.
Licensing and Budgeting
AI pricing can get complex. Smaller firms may struggle with enterprise pricing models. Larger firms usually negotiate enterprise deals or vendor partnerships. In either case, transparency around total cost of ownership is key.
Integration and Infrastructure
Many tools require integration with the existing document management system (DMS), case management, or time and billing systems. This could mean upgrading the legacy infrastructure and choosing between cloud and on-premises deployment.
Training and Onboarding
Training needs to fit different roles - lawyers, paralegals, staff - and comfort levels with tech. The goal is not just tool knowledge but how to apply it in real-world workflows.
User Adoption and Culture
Change management matters. If people feel AI is replacing them or increasing risk, they may resist. Communication, transparency, and visible leadership support go a long way.
Governance, Risk, and Compliance
AI adoption must align with legal ethics, client confidentiality, and regulations like General Data Protection Regulation (GDPR) or Health Insurance Portability and Accountability Act (HIPAA). Policies, auditability, and governance should be in place from day one.
Cybersecurity is a key part of any AI rollout. These tools often handle sensitive client data and connect with internal systems, which can open the door to new risks. IT and security teams need to work together to put the right protections in place - like access controls, data encryption, vendor checks, and regular audits. It’s not just about using AI - it’s about using it securely.
And remember to check Outside Counsel Guidelines (OCGs). Some clients require formal approval before you use generative AI on their matters. Others may prohibit it altogether - or expect you to use it to improve efficiency. Whatever the case, complying with OCGs isn’t optional - it’s essential.
Ongoing Support and Improvement
AI isn’t a “set it and forget it” solution. These tools need ongoing monitoring, regular updates, and continuous user feedback to stay effective. It’s also important to keep both IT staff and users trained on the latest features and improvements, so the solution keeps delivering value over time.
Small vs. Big Firms: What’s Different?
Although all firms follow similar steps, size changes how each step is managed. Here’s how small and big firms compare:
Budget and Resources
Small Firms: Budgets are tighter, and there may be no dedicated IT or innovation team. Many rely on simple tools or consultants.
Big Firms: More funding allows for enterprise platforms, in-house innovation teams, and custom development.
💡 Interesting note:
One person from a small firm (about 100 employees in total) mentioned that they’re intentionally waiting for Big Law to identify the best practices and successful use cases before fully committing to AI adoption themselves, essentially letting larger firms pave the way before they jump on board.
Decision-Making and Speed
Small Firms: Can act quickly with fewer stakeholders, but lack of bandwidth can delay execution.
Big Firms: Need more time to coordinate across teams and risk committees but can scale initiatives more strategically.
💡 Interesting note:
Big Law often has in-house IT teams or established vendor relationships with deep experience in the legal industry - professionals who understand both the firm's systems and its legal workflows. Small firms, on the other hand, may rely on external consultants who aren't always familiar with legal-specific use cases, which can slow down implementation or lead to misaligned solutions.
Technology Infrastructure
Small Firms: May use simpler cloud tools and lack complex integrations.
Big Firms: Typically have modern but complex systems. Integration is possible - but can be slowed by legacy tools.
Compliance and Risk
Small Firms: Often rely on vendors to help navigate compliance, security, and ethical guidelines.
Big Firms: Usually have in-house risk officers, formal policies, and internal review processes to manage AI-related risks and ensure responsible use.
What is the same?
💡 Interesting note:
Across firm sizes, some things don’t change. Many lawyers - and staff - are naturally cautious about new technology. There’s also a gap in expectations: people are often unsure about what generative AI can realistically do, and where its limitations lie. Regardless of firm size, managing expectations and building trust in the tools is critical for success.
Conclusion
Implementing AI isn’t one-size-fits-all. Small firms need cost-effective, simple tools and external support they can trust. Big firms need governance frameworks, cross-team alignment, and integration strategies. Both groups have opportunities to benefit from AI - but they need different approaches.
Whether your firm is just getting started or already deep in pilot programs, understanding your unique challenges is the key to long - term success. Talk to your peers, ask questions, and share what’s working.
Acknowledgments
Special thanks to my fellow ILTA volunteers, Michael Ertel and Damian Priamurskiy, for their valuable input, thoughtful feedback, and support during the creation and review of this article. Your insights helped shape and strengthen the final piece.
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