Artificial Intelligence — through applications powered by natural language processing — is rapidly transforming the legal services industry. The trick to getting the most out of these market solutions is knowing how to sift through all the noise.
A McKinsey & Co. report on “The Age of Analytics” hits on the value of AI and deep learning to innovate industries on a global scale. Looking specifically at lawyers, the report cites the value deep learning and natural language processing can bring by automating processes to provide enhanced consultation. The report estimates that 31% of activities could be automated with machine learning. This efficiency boost is directly correlated with a firm’s ability to generate more revenue.
Since AI technology allows for a better review of documents and relevant data, this enhances how quickly firms can analyze materials to discover both patterns and inconsistent language. The ability to augment this review process is where true savings can be realized — both from a monetary and time-saving perspective.
Leveraging the power of pattern recognition, emotional intelligence, behavioral analysis and natural language processing, AI predictive analytics can extract data that leads to better decision making. AI technology decreases the chances for errors, and equips firms with the right — and better — information to strengthen cases through more compelling evidence, all in a shorter period of time.
The Evolution of the Predictive Analytics Market
The predictive analytics software market is propelling the efforts of enterprises and law firms thanks to the anticipated increased customer demand and productivity gains from automating processes. The market is projected to reach $6.5 billion in 2019 — up $4.5B from 2012. Artificial Intelligence, in general, is projected to be a $15.7 trillion market by 2030, according to PwC data — with the U.S. accounting for $3.7 trillion of that growth.
What’s driving that growth is businesses ability to recognize the potential of AI to overhaul their data review processes.
Even with the knowledge of how litigation analytics tools can lead to better outcomes in court, firms’ management must have a clear vision as to how the application of AI tools can streamline their efforts and deliver results for their bottom line. While some firms struggle with adding another line item to the technology budget, it’s important to realize the long-term value that can be generated through investments in AI tools that can overhaul manual data review practices that are time-intensive and ineffective.
Equally important, the application of AI technology in a firm’s eDiscovery practices allows for its lawyers to focus on more high-level projects — while leaving computers to focus on the time-consuming tasks of document review and data analysis.
According to PwC’s report: “The first step in creating value from data and analytics is accessing all the information that is relevant to a given problem. This may involve generating the data, accessing it from new sources, breaking silos within an organization to link existing data, or all of the above…Combining and integrating large stores of data from all of these varied sources has incredible potential to yield insights, but many organizations have struggled with creating the right structure for that synthesis to take place.”
That’s where predictive analytics is changing how data classification is thought of and conducted by legal teams.
How Predictive Analytics Enhances Data Review
Instead of lawyers and investigators finding and labeling documents through manual methods, AI technology has taken the legwork out of the equation. Instead, firms are able to leverage AI tech to holistically analyze data with contextual review at scale with better speed and accuracy. The ability to detect patterns, flag risk and discover information relevant to a specific case in a matter of minutes — as opposed to weeks or months — is where the real value of AI and predictive analytics can be realized.
Client demands today have sparked the need for more efficient, cost-effective services. AI that leverages predictive analytics technology addresses these gaps by enhancing how raw, unstructured data is reviewed and structured so that legal teams can use that information to make stronger cases quicker and without inefficient manual review processes.
Through the implementation of enhanced data analytics, legal teams are equipped to mine massive data sets in little time to bring about better evidence analysis. They are also prepared to eliminate costly and ineffective manual review processes.
The Future of the Predictive Analytics Market
Industry data suggests firms are already taking advantage of technology-assisted tools that leverage data analysis, machine learning, data visualization and artificial intelligence. But just how much is that market evolving?
Global revenues from AI for enterprise applications is projected to grow from $1.62B in 2018 to $31.2B in 2025. That’s why 84% of enterprises believe investing in AI Will lead to greater competitive advantages.
What firms must tackle in order to achieve these operational efficiencies, is finding easier methods for mining unstructured data — with natural language processing being at the top of that list for text indexing and classification. Through the use of enterprise application software technology that relies on automated machine learning and data management, firms can quickly realize the long-term benefit of investing in better data analysis tools.
Guided by the potential to support legal decisions and enhanced litigation strategies, predictive analytics tools are going to continue playing an important part in the courtroom — and all the lead up to the courtroom. As firms use AI technology to craft better trial strategies and develop more accurate timelines, resources and budgets for cases, the true value of AI and predictive analytics will continue to be realized across the legal services industry.
In 2019, firms should no longer determine if they should invest in automated technology to streamline these business practices — but instead should be determining how their teams can better use this technology to take the guesswork out of data analysis in order to effectively compete and differentiate themselves in the market.