The Five Step Framework: What Legal Departments Should Think About as They Develop Their AI Strategy

AI here, AI there, AI everywhere. It's clear that AI adoption in corporate legal departments has reached a tipping point: A recent survey conducted by our firm, Major, Lindsey & Africa, revealed that 55% of in-house legal departments are either currently using or implementing specific AI tools in their legal departments to reduce costs, boost efficiencies, and drive business value.

While many legal departments have started testing AI applications, many still lack a cohesive approach to implementation. But whether you're a General Counsel looking to enhance existing AI capabilities, or you're leading a legal department with no formal AI initiative, you need an AI adoption roadmap.

Following a step-by-step process will help you implement AI strategically and maximize its impact on your goals. Legal leaders should start with these five essential steps as they develop their departments' AI strategy:

Step 1: Select a project leader and project team

If you are fortunate enough to have a legal operations department or person, they should take the lead on this project. If you are not so fortunate, as part of the first step, you need to identify a tech-savvy team member to become your Project Leader. (General Counsel will not have the time to take the lead). And once you select your Project Leader, you need to adjust their responsibilities to ensure they have significant time to handle their responsibilities.

Since successful implementation of an AI solution will require support from several stakeholders, the Project Leader should create a small cross-functional Project Team. Certainly, members of a stakeholder that will be part of the adoption of an AI solution should be part of the team. The Project Team should also identify managers in strategic areas who will be supporters of the new technology.

Step 2: Identify areas needing improvement

There is a plethora of AI solutions to improve the efficiency and performance of legal departments. For instance, risk assessment (especially for legal departments within financial institutions) could be one area where AI could significantly save time and resources.

By automating the scanning of internal documents and vendor contracts, AI can flag risks and unusual terms earlier, preventing potential violations and costly fines while supporting risk management goals. Similarly, other tools automate checks against regulations such as GDPR and CCPA, identifying non-compliant clauses in contracts.

Evaluating outside counsel's use of AI is also another promising area for AI applications. As the cost pressures facing in-house departments only mount, many GCs are already pushing their law firms to use AI to deliver better solutions faster. Indeed, Mark Smolik, chief legal officer at DHL Supply Chain Americas, was quoted in Bloomberg Law earlier this month saying, "the dynamics of the traditional buy-sell relationship in the legal industry are shifting." Clients Push Big Law Firms to Use Generative AI for Cost Savings," Bloomberg Law, Sept. 11, 2025.

These are just a few of the potential use cases for AI. Looking for AI applications that might work in your department can be confusing and overwhelming. Rather than reviewing the ever-expanding AI capabilities to try to determine what might be useful for your legal department, the first task of the Project Leader and Team should be to identify specific areas in your legal department that need improved efficiency, and then determine if there are AI solutions. This process should involve discussions with key stakeholders as well as the legal team.

Most likely, the Project Team will identify two or three areas that need improvement. From there, they should prioritize where to focus, and define success. At this stage, specific performance levels may not be known, but there should be a consensus on when the problem is solved, or the necessary efficiency is achieved.

Step 3: Research solutions

The Project Leader and Team now need to research potential AI solutions to achieve the identified objective. They should use AI tools to research available options. For example, you can query Google Gemini about "AI for SEC filings" to get a summary of how legal departments are using AI to draft corporate governance filings or to understand current SEC applications and vendor options.

Focus on solutions that align with your specific objectives and department size. Reach out to colleagues to see if they have adopted an AI solution to the identified issue or similar one. Perhaps there is a simpler solution that does not require AI. Hit the low-hanging fruit first. Even when selecting a problem that requires an AI solution, start with the one that appears to be the simplest or easiest to adopt.

In addition, the Project Team should draft an initial business case that identifies the problem and planned solution. The business case should identify estimated costs and savings. Be sure to consider the time and effort of all involved in adopting a new solution in your cost-benefit analysis.

Step 4: Vendor selection

The next step is to conduct AI research to provide a list of potential vendors. Reach out to colleagues to see if any have used a vendor and their satisfaction level.

Most importantly, the vendor must fit your needs. For example, if you do not have a legal operations group, you will want to select a vendor that is willing to work closely with you on developing a business case and implementation plans. Will you be assigned a project leader from the vendor who can guide you through the next steps in the process? Having this support is critical even if there is a preliminary cost for a project leader from the vendor to work closely with you to map out your implementation plan.

Working with the selected vendor, you should have a clear understanding of what can be achieved by the proposed AI solution and its specific performance criteria, implementation timelines, and comprehensive cost analysis, including long-term ROI projections.

This assessment should also include an analysis of some of the risks of AI implementation, including:

• Regulatory Compliance While the U.S. lacks comprehensive AI legislation, the EU has implemented extensive AI requirements. Monitor emerging state-level legislation. Ensure compliance with applicable regulations, corporate guidelines, and contractual obligations.
• Confidentiality and Data Security: Any AI system must maintain required confidentiality standards and robust security measures. Ensure proper authorization for new information usage and data handling protocols.
• Data Quality: Is your data accurate and organized? The rule of garbage-in-garbage-out still applies even for AI.
• Training and Education: Insufficient training poses significant risks. Both legal teams and internal stakeholders must understand and embrace AI system usage. For example, AI-powered compliance monitoring requires active participation from compliance departments and relevant business functions.

Step 5: Draft implementation plan and execute

With the input of the hired vendor, the Project Leader and Team must develop a step-by-step implementation plan. Ideally, the plan will be divided into phases. Each phase should identify the roles of those needed and a specific timeline for performance. If possible, identify key success factors and test criteria for the successful completion of each stage.

Perhaps you can start with a pilot program, measure its success, and then move forward. Continue to monitor the performance of each phase and make adjustment where necessary. Monitor performance as compared with the objectives in the business plan.

The importance of training both users and stakeholders on adoption of each phase of the implementation is critical. Lawyers are typically not early adopters of technology. Break up training and adoption into bite-size pieces to the extent possible.

Key takeaways

Success requires a structured approach: identifying priority areas for AI implementation, assigning trusted project leadership, and systematically evaluating AI solutions (and pitfalls) that address specific departmental needs.

The question is not whether to adopt AI in legal departments, but how quickly and effectively you can implement solutions that drive measurable value for your department and organization. To maximize the chances of success, it is essential to adopt a procedural, results-driven approach.