The adoption of advanced technology in legal managed services has been evolving for over a decade, with early AI machine learning steadily gaining traction. However, in the past 18 months, AI—particularly Generative AI—has accelerated this transformation, promising to revolutionize the industry at an unprecedented pace by automating routine legal tasks and optimizing operations more efficiently than ever before.
What’s truly groundbreaking is how accessible and seamlessly integrated AI has become in recent years. The introduction of ChatGPT marked a turning point for the industry, making advanced technology intuitive and usable even for those without a technical background. Unsurprisingly, platforms like ChatGPT, Microsoft Copilot, and other user-friendly AI solutions have demystified the technology, allowing even the most skeptical legal professionals to explore its potential with greater confidence.
Blickstein Group’s 17th Annual Law Department Operations Survey Report found that 89% of survey respondents are in some stage of developing a technology strategy and roadmap for their organization. For clients and organizations, the question is no longer “Should we use AI?” It’s “Where do we begin?”
4 Key Considerations to Maximize the Value of Generative AI
1. Gain clarity within your own organization first.
Before starting a discovery phase, it’s important to first know exactly what you’re looking to accomplish. This starts with conducting a full assessment of where your organization currently stands and asking some foundational questions to establish purpose and readiness for AI adoption.
- What problem are you trying to solve?
- What process inefficiencies exist that AI can improve?
- Do you have the necessary resources, such as a budget, structured processes, clear documentation, and access to data?
AI thrives on data. Without robust, organized, and comprehensive data inputs, you’ll be limited in what you can achieve. For the vast majority of organizations, data is often duplicated and incomplete, and it lacks meaningful reference tags or parent-child relationships to support the search. Having AI-ready data is the only way to drive real ROI from any tool or process you implement.
2. Define specific use cases.
While AI can help with almost anything, not all its uses are right for you. Instead of getting distracted by shiny tools or market trends, focus on identifying specific use cases grounded in your organization and department’s needs.
Whether you’re looking to streamline contract review or automate aspects of data management, every use case should come from understanding your defined business objectives. AI adoption should flow from your specific needs out, not the other way around.
When it comes to use cases, simple is better. Focus on narrow objectives and define clear goals. Examples of specific use cases include:
- Contract creation: Speed first draft creation with AI-suggested content based on a library of relevant topics and clauses
- Redline review and contract clause analysis: Based on playbook options and preferred or historical positions
- Playbook creation: Create preferred position guidance based on existing standard templates
- Bulk extraction: identify key content for repository search tagging, risk analysis, or compliance actions
- Intake reviews: To feed automated triage and workload assignment
- User helpdesk/FAQ interface: Update and maintain policies/knowledge base for business self-service reference
- Legal research: Identify like cases quickly
Taking this approach allows you to prioritize your use of AI based on your specific pain points, identify specific goals and measure outcomes, and gain support for further adoption with real metrics proving value.
3. Take a deliberate, thoughtful approach to vendor research.
The legal AI landscape is overwhelming, and it can be difficult to cut through the noise. Having a framework from which to evaluate vendors is essential to determining which platforms are an actual fit for your specific needs. There are a few priorities to keep in mind:
- Ensure the tool can solve the specific problem you’ve identified
- Request case studies, specific use case demos, and clear documentation to validate the vendor’s AI capabilities as well as identify their limitations
- Determine how well the solution can integrate with your CRM, workflow automation platforms, case management systems, and other existing tools
- Inquire about the product roadmap to ensure the solution can grow with your needs
- Understand how the vendor can support you through implementation and adoption to ensure success
- Require robust security responses for content exchange, model creation and use, and output validation
4. Test with pilots.
Adopting AI is a significant change that impacts both your processes and your people. Like any major business move, thorough testing, impact analysis, and change management are critical.
A smart way to increase your chances of a successful AI implementation is to run a pilot program to evaluate how the tool performs in a real-world environment. Pilots will help to:
- Build stakeholder trust and buy-in
- Proactively identify potential barriers to adoption like training needs or integration challenges
- Get users comfortable with new workflows
Start small by running pilots on one or two use cases before scaling operations and use those successes to demonstrate ROI and build support for more investment.
Exploring Tech-as-a-Service as an Alternative Solution
For organizations hesitant to take on the cost and complexity of implementation, opting for a tech-as-a-service approach is a good alternative. Tech-as-a-service allows businesses to partner with service providers who already have an AI tech stack.
These service providers – like Integreon – act as innovation hubs or service centers where organizations can pilot AI and experiment with minimal upfront risk. Benefits of a tech-as-a-service approach include:
- Lower costs compared to building in-house infrastructure
- Reduced risk through access to proven, pre-existing AI tools
- Access to expert guidance and support for smoother implementation
This model allows organizations to balance innovation and risk while gradually scaling up AI adoption.
Leveraging AI for Success
AI-powered processes are now table stakes in the legal ops world. Organizations who take a standard, structured approach to adoption can ensure the use of the right AI at the right time. Whether implementing AI directly or taking a tech-as-a-service approach, companies must begin moving their AI agenda forward to stay competitive.