P2P

60 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 | W I N T E R 2 0 2 1 F E A T U R E S AI reads specific questions and can select appropriate answers based on what has worked in the past. Each answer is scored based on how appropriate the content is when mapped to the questions. The AI system will learn and improve over time, even when a manual answer or addition is more appropriate. This alleviates running painstaking content searches or scouring individual databases to assemble responses. Trusting AI. The most innovative law firms have already started using AI to parse content for RFPs, avoiding cumbersome and often time- consuming searches. Instead, the system recommends, for example, five buyers and six experience records as part of the new business pitch based on their relevance and past pitch performance. The data points are there, all of which can be used to build a strong recommendations engine. Although enterprise search is still the norm when it comes to finding content, with these intelligent systems, the AI will automatically recommend the track to pursue thereby eliminating the need to start with a broad search and filtering through to the precise information required. For example, an AI- powered recommendation may say "we think you should use these biographies", which can then be followed by supplementary search, if required, saving valuable time. Predictive Pitching The other area that data science and AI are focused on is the predictability of outcomes. This is being seen in litigation matters, for example, to quickly surface which lawyers have winning case records. And now, as business development professionals are facing an influx of RFP requests, predicting the likelihood of winning new business has become vital––and thanks to AI and ML, is finally a reality. Law firms are using predictive analytics to determine, for example, what business they should and shouldn't pitch for, and who has the best track record based on proposal requirements to work on a given matter. AI recommends the right content at the right point in the sales cycle and personalizes the content accordingly. These inputs and "learnings" are driving machine learning to recommend more accurate content, and over time, winning content. Most of this discussion has focused on specific AI use cases for law firm proposal management. However, there are far broader implications for the legal industry. The idea here being to further help demystify and 'unarm' applications of AI, whether they are used in law firms or across other industries. AI can help create and enhance the personalized client experience. This is true with smart proposals that can anticipate and understand what the recipient requires. Beyond personalization, data analytics and the structuring of data based on client preferences and choices, is not only here to stay but the present and future in marketing and business development. In our proposal world, data can help us determine how well received our content is and help predict future activity and outcomes. "AI recommends the right content at the right point in the sales cycle and personalizes the content accordingly."

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