AI isn’t coming to the legal industry. It’s already here.
Law firms across the country are experimenting with tools that promise faster research, more accurate drafting, stronger client service, and dramatically lower costs.
And the shift isn’t theoretical anymore. A recent industry survey showed that more than half of law firms are already using or piloting AI tools across their practice groups, whether for litigation, corporate work, eDiscovery, or knowledge management.
But while experimentation is widespread, strategic adoption is not. Many firms are testing discrete AI tools in pockets of the practice, yet few have a cohesive, long-term roadmap.
Whether you’re a Managing Partner looking to modernize your firm’s approach or a Practice Group Leader trying to bring order to ad-hoc experimentation, you need a structured path for AI adoption.
Step 1: Appoint a Project Lead and Core Implementation Team
AI adoption cannot be a side project. Someone must own it.
If your firm has a knowledge management, legal operations, or innovation team, they should take the lead. If not, identify a tech-savvy attorney or administrator with the bandwidth to manage cross-departmental work. This person becomes the AI Project Lead, and their existing responsibilities may need to be rebalanced to ensure they can focus on implementation.
From there, assemble a cross-functional team. Include partners from key practice areas, associates who understand pain points firsthand, IT or knowledge-management leads, and administrative champions. This team will evaluate needs, represent stakeholders, and create buy-in across the firm.
Step 2: Pinpoint the Areas With the Greatest Need
AI doesn’t start with technology. It starts with identifying inefficiencies.
For law firms, common candidates include research bottlenecks, slow drafting cycles, review-heavy matters, inconsistent knowledge management systems, and time-consuming due diligence or discovery tasks. Compliance-driven practices may also benefit from automated monitoring and contract scanning tools.
Rather than sifting through an overwhelming landscape of AI capabilities, begin by identifying specific areas in your firm that consistently drain time, budget, or client satisfaction. Interview partners and associates. Analyze timekeeping trends. Look for patterns in missed deadlines, capacity issues, or repeat staffing challenges.
Most firms will identify two or three core opportunities. Prioritize them, align on what “success” looks like, and define efficiency or quality thresholds that would represent meaningful improvement.
Step 3: Research Tools and Build a Business Case
Once your priorities are clear, begin researching AI solutions that directly address them. Use AI itself to support this research; tools like ChatGPT, Gemini, and Claude can quickly summarize categories such as “AI for litigation drafting” or “AI for M&A due diligence.”
Look for tools that align with your firm’s size, practice mix, budget, and appetite for change. Consult peer firms to learn what’s working for them. In some cases, the best solution won’t be AI at all; many workflow problems can be fixed with better process, templates, or simple automation.
As you explore options, draft an initial business case. Document the pain point, the proposed AI solution, estimated costs, likely efficiency gains, and the time and personnel required for adoption. Cost-benefit analysis should extend beyond subscription fees to include implementation labor and expected productivity gains.
Step 4: Select a Vendor with the Right Support Structure
Vendor selection means choosing the right partner.
Ask peer firms about their experiences. Evaluate the vendor’s training and onboarding programs, support availability, and willingness to assist with implementation. Firms without dedicated innovation staff should favor vendors who provide hands-on guidance, structured deployment plans, and clear communication.
Your assessment should also address key risks: regulatory compliance, data security, confidentiality, data quality, and user training. AI systems must meet professional obligations around client data, privilege, and cybersecurity. If your firm’s data is disorganized, outdated, or siloed, AI performance will suffer; cleanup may need to be part of the rollout.
Once you have a finalist, define the solution’s capabilities, implementation timeline, performance metrics, and detailed ROI estimates.
Step 5: Create a Phased Implementation Plan and Execute
With vendor input, build a phased rollout plan. Each phase should outline the team’s responsibilities, timelines, success metrics, and testing checkpoints. Many firms start with a pilot program—often within a single practice group—then expand once efficacy is proven.
Training is critical. Lawyers are notoriously cautious adopters of new technology. Break training into manageable steps, reinforce with practical examples, and assign internal “champions” who can support their peers. Continually measure outcomes against your business case and adjust as needed.
The goal is ensuring attorneys embrace AI and use it effectively.
Key Takeaways
Successful AI adoption inside a law firm requires structure, leadership, and clarity of purpose. Identify where the firm needs the most help, appoint a strong project lead, evaluate tools through a practical lens, and implement in phases with thoughtful training.
The question for law firms today is not if they will adopt AI, but how quickly and strategically they can use it to improve client service, enhance efficiency, and maintain competitive advantage.
We are a boutique search firm providing exceptional executive and professional talent to our clients and helping talented experts achieve their career goals.
Our goal is to match human capital needs with highly qualified individuals as quickly and efficiently as possible, with the end goal of enhancing the client’s organizational capability.