P2P

54 P E E R T O P E E R : I L T A ' S Q U A R T E R L Y M A G A Z I N E | S P R I N G 2 0 2 1 As businesses look ahead in increasingly competitive marketplaces, they need to have an edge. They need to streamline their processes and find new and innovative ways to unlock and activate the knowledge across their teams. In other words, they need to make knowledge work. Law firms and legal departments are exploring AI as one way to make this happen. How organizations, law firms, and legal departments approach this challenge and make their investments will set the stage for competitive advantage, creating winners and losers in the decade ahead. Think "Verb" Not "Noun" to Successfully Modernize with AI Over the past several years, as practical AI has been maturing in the market and we've looked closely at lawyering challenges that can be addressed with AI, we have grown to better understand the makings of a successful AI-based (or data driven) modernization project. What we have found is that successful projects make knowledge work by turning knowledge from a noun to a verb. This means that knowledge – a thing, an output, a noun – is not the final end. A business outcome – doing something with that knowledge, taking action, impacting something and creating a result, turning knowledge into a verb – is the ultimate goal and the way to define success. Making Knowledge Work: A Practical Guide Projects that are successfully making knowledge work use all tools available to optimize the outcome. Often, knowledge workers will use a combination of techniques: automation, classification, entity/data extraction, analytics, and more in modernizing a given process. These tools – tied to other knowledge assets like templates and best practices – lead to integrated solutions that address more of the ultimate problem. If modernization is framed only as an AI problem, firms may find they are not solving the whole problem and that the impact is diminished. Experience shows that successful AI modernization projects share some common characteristics. A successful approach commonly includes the following best practices: Identify the ultimate impact not in terms of technology but business metrics. Think here of real estate lease work. Manual review of standard lease documents for renewal or renegotiation can be rethought through processes that fit with AI capabilities marrying data to insight and automation. The result? Shorter length of delivery, quick risk reporting to the business, quicker decisions on approach, and more targeted AI-enhanced due diligence review through extraction. Additionally, data can feed automation that lowers the bill for the client, and data (not "landlocked" PDFs) can be a value-add service provided to feed into client business systems, saving time and costly keying. Firms can expect savings of 40-60% or more in human labor required to complete a review – leading to more competitive, predictable pricing for the client and higher business value of the end product, making firms more sticky to the client. Producing industry insights for an existing or prospective client provides another example of identifying impact in terms of business metrics. As part of their role as a trusted advisor to clients, a firm might produce an industry overview of evolving contractual terms that could impact their business strategy. This used to take 100 hours of associate time, emailing partners and lawyers for deals worked on. With a combination of AI search, deal locator, F E A T U R E S

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