As the integration of Artificial Intelligence, Machine Learning and Big Data in the workplace is becoming the new norm, concerns are increasing over how the technology could affect employee performance, job security and the adaptation and reliance of technology in the legal industry.

Raj Goyle, former Kansas State Representative, Harvard Law graduate and Co-Founder/CEO of LawTech platform Bodhala, sat down with Inside Counsel to discuss why AI, Machine Learning and Big Data are not costing jobs when correctly implemented, how technology eliminating inefficiencies within the workplace, and why is legal one of the last spaces to embrace tech integration.

Over the last 20 years, mastering data has enabled new players to leapfrog power players and build dominant positions across all industries. Standing in the path of Moore's Law has proven to be a dangerous place. For example, technology giants like Google, Amazon, and Facebook have flattened scores of companies in verticals like advertising, retail, and media – all who assumed they were in a traditional industry immune to newcomers. And, these same trends are driving the integration of AI, Machine Learning and Big Data in legal. Every day, Goyle hears from GCs and law firm chairs who are scrambling to understand how data can help them in their mission.

“It's hard to avoid the monthly think-piece announcing that 'robots are replacing lawyers,'” he said. “In reality, most of AI in the legal space has been focused on doing first-pass document review or contract management. There will be disruption in that space, although a considerable amount has already been commoditized through offshore or stateside contract labor. The bigger stress is on leaders of legal departments and law firms who will find their positions replaced by competitors who are maximizing data to find and execute on opportunities.”

When used correctly, AI, Machine Learning and Big Data could create more jobs in the law industry, per Goyle. Now, a huge portion of legal is concentrated in a small number of firms because of an over-reliance on outdated proxies. Through the proper collection and analysis of data, companies can make smarter decisions on where to allocate their money, freeing up the redistribution of tens of billions of dollars in spend, which would ultimately increase the number of legal jobs.

“A majority of clients today complain about surprises in bills, while too many talented lawyers simultaneously complaining about being unable to compete for business,” he explained. “Without a data-driven model that can help clients, lawyers, and firms compare matters, practice areas, and providers, the industry has failed in providing transparency and efficiency.”

Data mining tools that companies are constructing enables a more efficient legal market through algorithms that index, connect, and parse datasets, leading to rich insights about lawyers, firms, practice areas and models of capabilities, pricing, and billing patterns. Through this data analysis, clients push work down to less-gold plated firms and boutiques, winning them accolades from management, saving employers money, and winning work for great lawyers that might otherwise not be able to compete against big players.

“Predictive algorithms can help lawyers focus their judgment better. For example, legal departments struggle with predicting which of thousands of active matters are being handled in an inefficient way,” he said. “There are constructed models that examine patterns in billions of dollars of legal spend on staffing, timekeeper billing patterns, partner/associate allocation, etc., which help in-house lawyers focus on what's most important.”

In addition, on the law firm side, current market data can help inform investment and retrenchment decisions in practice areas, offices, and clients that – until now – have been guided by little more than instinct. The top spenders on legal in the world tell us that firms that mine their own data and deliver a data-driven pitch help GCs defend their decisions, consequently help these firms win business.

So, why is legal one of the last spaces to embrace tech?

“Traditionally, the purchase and management of legal services was based mostly on sophisticated opinion,” said Goyle. “The law insulated itself from the normal business questions of the C-suite by arguing that law was too premium, sophisticated and risky to measure with data. This resulted in an opaque market with ever-rising costs that made it hard for up-and-coming lawyers and firms to win work they could successfully do.”

But, the tools available today that can be used to collect data and classify it are powerful given the size of the task. For instance, data tools can drive what billions of people are reading daily on Facebook, or billions of financial transactions an hour. In that context, it is very feasible to draw deep insights on less than one million corporate lawyers and their work with their corporate clients worldwide. Incentives have also changed very quickly. Why? According to Goyle, it's becoming an increasingly risky bet to continue to count on business development for legal services remaining an opaque process.

When it comes to companies using AI, Machine Learning and Big Data effectively according to Goyle, the key is understanding how one goes from data to insight to action. First, data alone does not transform into insight. We often see that companies and law firms that don't realize the depth of insight possible through analysis of their own data. Hal Varian of Google says the company's success is about “recipes, not ingredients.” In other words, while data matters, what is instrumental to their success is the quality of the algorithms to make use of the patterns of the data.

Goyle added, “Lawyers often think if they could get a magical Excel file with everyone's rate card then all will be revealed – when, in actuality, such a file would be largely useless. You must know how to make sense of the data in order to use it efficiently.”