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Artificial Intelligence (AI) is not a magic wand. But it is a pretty awesome set of tools.

As a leader in AI and document and knowledge management, iManage has worked with thousands of professionals across law firms, corporate legal departments, and professional services organizations. Invariably, these organizations know that their document stores are filled with rich, high-value content and know-how, but they generally don’t understand how to apply AI for real business transformation. That’s where iManage comes in – helping customers put AI to work with confidence to drive meaningful business outcomes.

How do we do that? Simply put, iManage RAVN is the most adaptable and trainable AI engine available today. But we don’t just rely on our powerful machine learning algorithms.  Because machine learning by itself isn’t always sufficient, the iManage solution also incorporates deep capabilities in rules-based extraction techniques.  The ability to fine-tune what the engine learns, gives users great power and flexibility in getting to the desired information quickly and efficiently. We’re also committed to making our customers successful with AI by giving them a path to learn how to apply the technology with their own data, directly from our experts, in a multi-day workshop known as AI University (AIU).

What We’ve Observed

Many firms get their feet wet using AI with familiar and straightforward knowledge management projects – like due diligence reviews and extracting provisions from leases, for example. These “push-button” projects require very little effort with little to no training needed.

However, we increasingly find that customers want to utilize this emerging technology in a transformational way – beyond a single project or use case. They want to unlock knowledge and insights from their legal documents and contracts at an enterprise level, in a repeatable way, and across many projects and teams.

While many customers love the convenience, simplicity and outcomes of using the large number of out-of-the-box extraction models that iManage provides, there’s often a light-bulb moment when a firm realizes that taking the time to develop their own AI extraction models and rules could be more effective in providing a competitive advantage in serving their clients.

Personalized AI Learning

With that in mind, iManage expanded upon its basic AI training to develop AIU. The curriculum features in-depth workshops to educate customers on which tools to use for specific use cases and also teaches customers how to excel in creating more effective rules-based models. iManage offers the training in two formats – a newly introduced live, virtual model delivered on-line during 3 half-day sessions, and an in-person model delivered at the customer site over 2 full days.

The whole inspiration behind AIU is to empower our customers to use their own expertise and documents to move forward with a real AI project in a significant way that solves a firm’s known business problem.

Preparation Ensures Success

Advance preparation by both the iManage and customer team a few weeks prior to conducting an AIU ensures the customer is ready to engage in the project. Customers identify and upload the documents they want to work with—as well as the datapoints they want to extract from those documents. If needed, instructors can also provide input on which documents customers may want to use for specific use cases. Once determined, iManage instructors study the customer use case to suggest the most optimal extraction techniques for the project.

While this preparation takes some time, it gives customers a better understanding of what’s involved in facilitating machine learning and how it must be managed. We’ve had prospective customers ask us to run AIU sessions for them just so they can better understand how the process works and how it could be administered within their organizations.

Key stakeholders such as subject matter experts, trained legal staff, and data knowledge managers are designated by the customer to participate. With these stakeholders present during an AIU, we can validate their use case and confirm they are going to have the time and resources to work on it properly.

Delving into Real-World Projects

During the AIU, we provide a suite of AI-powered tools and train customers on the best fit for each one. Customers discover all sorts of things in their documents, such as patterns, similarities, and complexities that machine learning can pick up. We challenge them to think about which tool is best suited to achieve their goals, and we are continually impressed at the innovative applications and results that customers discover for themselves after this first project.

Our goal is to work with the customer to help the technology learn what we want it to do. Sometimes that leads to the realization within the organization that RAVN needs to be applied to review other groups of documents or other types of parameters in order to return the results they desire.

If priorities change and they want to work on a new project, they can then save the data and insights they found into the solution’s clause bank so they can resurface them for other work or projects. Typically, the common use cases we see are for contract intelligence, such as a customer trying to turn unstructured or semi-structured information into data points. They also often use RAVN for due diligence purposes, such as to learn what contracts need to be renegotiated or repapered.

We also reserve time during the AIU for user feedback about what might be needed in another module or use case. That allows us to incorporate customer insights into future product enhancements, so we can continue to help teach RAVN how to help our customers.

Time and again, we’ve seen that when firms really roll up their sleeves and give us a few days for concentrated training and exercises that apply to real situations, we can show them how RAVN can deliver improved efficiency, meaningful results, and competitive advantage.

More math than magic, AI has the power to radically transform business operations and outcomes. But it’s nice to know that iManage AIU may give customers a trick or two up their sleeves to get started.

If you’re interested in an AIU, or learning more about how RAVN can work in your organization, check out more about it here.

About the author

Bryan Bach

Bryan helps firms unlock knowledge and insights in their legal documents by leading workshops to help them apply AI to their own data. Prior to iManage, Bryan was a research attorney and practice area consultant for LexisNexis. He received his JD from American University.