The State Of Automation And AI In The Legal Industry
Marcin Nowakowski's industry evaluation
Compared to other “non-production” industries, the legal operations industry seems to be a latecomer to the world’s chase of RPA (robotic process automation), AI (artificial intelligence), ML (machine learning) and predictive analytics.
The responsibility for the suppositional tardiness (at least partly) is the “silos” mode in which legal operations existed until early 2010. The job of legal advisors was to tell the business what is legal and what’s not. And topics of business partnering, efficiency and automation were nowhere on the radar.
In the second decade of the 21st century, everything has changed. Business operations are on their way to efficiency through automation, natural language processing and data mining – and they expect the same approach to technology usage from their partners, including legal services providers. On the other hand, the supplier market is changing – alternative service providers, outsourcing and agility of B2B operations is heavily influencing legal services sourcing.
Despite being early stage, the initial activities are visible. E-discovery and due diligence efforts are supported heavily by technology. Contract management systems and e-signatures are changing how contracts are prepared, maintained and negotiated. The next step will most probably be more wide range usage of machine learning and predictive analytics.
"Two of the three biggest obstacles in automation and AI adoption in the legal industry are be data quality and process quality."
Also in internal operations of the legal sector (invoice processing, billing, vendor and client management, budgeting and alike) the business case for usage of RPA is clearly visible. Early adopters will most likely be large organizations, which have their operations center, or centers of excellence in their outsourcing centralized network.
Two of the three biggest obstacles in automation and AI adoption in the legal industry are be data quality and process quality. Regarding data quality– while the legal industry operates on large amounts of data, it is not organized in a way that allows quick and easy adoption of technologies (this especially refers to machine learning).
The second topic is process quality. Other industries are on their long (and not easy) journey of process management and quality improvement. Lean, Six Sigma and other tools are widely used “on” processes to get them ready for automation.
The third topic is proper project management that needs to be used to work on the two topics above. LPM (Legal Project Management) is evolving fast and being widely adopted but it still needs enhancement to get to the status of ‘enabler of digitalization.’
Or listen to Marcin's recent AI Network podcast interview here: