True eDiscovery managed services is about a programmatic approach to process and technology. Artificial intelligence (AI) and predictive analytics can be included in a managed services construct. With proper planning and consistent deployment, the benefits of AI can be maximized and lead to huge benefits such as time savings, cost savings, and better insight into data. Doing so will maximize the benefits of AI, and as AI platforms’ power and abilities grow, so do the benefits.
Some of the hallmarks of a successful eDiscovery program include cost savings obtained while utilizing people, process, and technology to obtain results that are efficient, consistent, and defensible. Managed services, an approach to handling discovery and data that focuses on the portfolio level or across matters, has traditionally been one of the best ways to achieve these results.
In a classic sense, managed services is more than just a price play and is not tied to an infrastructure as a service (IAAS)/self-service model or a software as a services (SaaS)/full-service model. It can encompass every variation in between and instead, is focused on an approach with well thought out people, process, and technology that interact harmoniously, that are well documented, and that are used repeatedly across matters to create consistency.
One of the benefits of managed services is that it is not static. While it does utilize a defined technology stack with documented process wrapped around it, that technology and the supporting processes can and should change overtime to account for new technology and capabilities, new case law, and lessons learned. Today, that means practitioners should be investigating AI and making strategic decisions about how to build it into their tech stack and processes. Moreover, the use of AI goes well beyond TAR and its various versions. While those remain core components of use cases for AI, users would be leaving efficiency, consistency, and defensibility gains on the table if only limited to that single workflow. More and more AI tools have expanded their capabilities and functions that, when properly understood and implemented, can greatly increase your ROI in the technology while driving success. A managed services provider can help maximize those benefits because they build the use into a repeatable process. These use cases can include:
1. Early Case Assessment (ECA)
Using AI early in a case helps find not just relevant, but potential smoking gun documents much sooner in the case lifecycle. This allows you to gain an understanding of the facts in a more complete way before a lot of money is spent on discovery. You can then make more accurate and impactful litigation risk analysis.
2. Application of AI model libraries
Build custom conceptual models and analytics that can be used over and over. Although most litigation and investigations review work is unique to each case, there are certain aspects that can be pre-modeled when it comes to identifying repeating key language or legal themes. Basically, when a new matter comes along, pull from the relevant library where a large chunk of the review process is already complete, and then input the new matter-specific documents that can further train the system. Features such as clustering, sentiment analysis, and concept wheels each provide unique insights and uses that can drive powerful understanding and results early in a matter when your knowledge about the facts and data may otherwise be minimal.
3. Culling and spend reduction
AI can be utilized to craft better search terms and bucket documents based on language, but also by concept and metadata. This reduces data for review in a manner that is more accurate than search terms alone, and speeds up the review by reducing volumes and pushing the most relevant and important material to the front of the review.
4. Quality control
AI can be used to check coding decisions based on characteristics not easily detected by eye or term and allows you to reuse work product across matters ensuring the same QC and QC concepts are applied again and again.
Despite these diverse and impactful use cases, one of the biggest challenges with AI can be adoption. There are two primary drivers of that challenge, both of which a managed services team can help you overcome. The first issue is the upfront cost of AI. It is often presented as a line-item purchase for a specific matter. Managed services changes that dichotomy by baking in the cost of analytics into your managed services contract, therefore removing that obstacle.
The second problem faced when trying to deploy AI is familiarity. Counsel is frequently apprehensive in using AI because they are uncertain how it works, and they are uncomfortable deviating from their comfort zone. With a managed service provider deploying AI, along with it comes prior vetting of the technology that is well documented, which can be shown to attorneys that are uneasy. Furthermore, managed services can strategically and defensibly incorporate AI tools into documented processes that define when to use the tools, share which components to use and can build a repeatable documented process that can drive behavior for lawyers and decision makers.
A managed service provider can create defensible and repeatable workflows based on the right blend of traditional technology and AI tools to reduce costs, increase accuracy and efficiency. There is also significant training provided and an expert on hand to answer any questions. A good managed service provider properly documents the use of how AI is used to ensure defensibility, and has experts qualified to testify if there is ever an issue.
As with anything, these tools are only as powerful as their input and only if used properly. This is why it is important to work with a strategic partner who has a solid understanding how AI tools, as well as their features and functionality, would work best in your environment. You also have the benefit of having your provider, who understands your workflows and cases, properly build any new AI tools into your playbook. That is the power the managed services model provides: AI tools are vetted, tested, and only the best ones for your specific environment are applied in a defensible manner without any upfront costs.
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