NetApp’s Connie Brenton: From Legal Ops to Bot-ticelli

Connie Brenton has been at the forefront of change inside the inhouse legal world. From implementing some of the first eBilling and contract management software solutions in the industry, to leading on the use of legal process outsourcing (LPO) providers, Brenton has always looked for ways to improve legal operations at the corporations that have employed her.

She’s worked for some big-name companies over the last 15 years, such as Sun Microsystems and Oracle, as Deputy General Counsel (GC) and Managing Counsel respectively, where the GCs she supported (Michael Dillon and Dorian Daley) embraced less costly legal services support. 

Brenton also made a mark for herself in founding and then growing the Corporate Legal Operations Consortium (CLOC) from zero to 2000-plus members in 40 countries worldwide, with membership including 40%-plus of the Fortune 500.

Today, Brenton is settled at US-based cloud data services company NetApp, where she works as the Chief of Staff and Senior Director of Legal Operations. Like her prior employers, her current GC, Matt Fawcett, does not believe in the status quo: he’s willing to take risks and try new things to drive significant ROI and create a culture of innovation for the legal department and ultimately, NetApp.

As a Beta tester of new technologies and related services for the legal industry, NetApp is always piloting new technologies, Brenton explains. Right now, NetApp’s legal team is looking at AI, chat bots and robotic process automation (RPA).

The company has already implemented some RPA solutions for automated workflows and it also recently launched a chat bot solution.

‘The chat bot will start the same as every new technology we test out: with a simple use case in a low risk environment. Our first chat bot, called Bot-ticelli, will answer questions such as: ‘Do we have an agreement with this party? When does the agreement expire?’’ she explains.

NetApp is also rolling out a solution that involves very minimal human involvement with the production of documents, such as non-disclosure agreements (NDAs).

‘We are also implementing a solution in partnership with contract specialists LexCheck. The technology takes third-party paper, compares it to the NetApp template, and highlights differences and redlines changes,’ she says, ‘AI functionality allows the technology to get smarter and smarter around identifying issues and become a no-touch solution, over time.’

‘The goal is to end up with a 90% no-touch solution. The remaining 10% that requires human intervention will be outsourced,’ she adds. 

The chat bot will start the same as every new technology we test out: with a simple use case in a low risk environment.

Legal Ops

Mention legal ops to Brenton and she talks passionately about the changes she’s seen that have helped to drive improvements in inhouse legal efficiency. She also notes the opportunities that still lie ahead.

Artificial Lawyer asked her what she thinks about the value of this growing field. 

‘Legal operations is the only role that pays for itself [in an inhouse legal team]… it’s a role that allows your department to run legal like a business. It’s not a novel concept among Fortune 500 Companies, but it’s still a relatively new discipline in the legal space: this concept of running your department like a business exists in all of the other organisations within an enterprise.

‘Inhouse legal has been the last corporate function to bring in a business operations and optimisation approach, partly because of the slow evolution of law firms and partly because of the personality traits of lawyers generally,’ she adds.

The Pros and Cons of Machine Learning

She also has some strong views on other subjects, such as the pros and cons of machine learning software for contract review work, saying there are times when it will be effective and other times it just won’t be.

‘When it’s effective, it’s highly effective. It’s fast and accurate; however, it takes a long time to get AI technology trained. For example, we thought it was going to take 200 documents to train [one piece of software that we used] and it took 2,000 documents to train it’.

She adds: ‘And the trainer needs to be a sophisticated trainer: so you can’t outsource this kind of training activity, because if you train a machine to do something poorly or wrong, all you have is a technology that provides a lousy answer faster.’

Overall then, a practical, yet ambitious approach to changing the inhouse legal function. Artificial Lawyer looks forward to hearing how Bot-ticelli and NetApp’s other projects progress.

Interview by Irene Madongo

Come along to the Legal Innovators California Conference – San Francisco – June 18