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Legal Project Management: Legal Futures – A 2020 Scenario

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By Rohit Talwar and Alexandra Whittington

Over the last 18 months, we have seen exponential growth in the discussion of and experimentation with the use of artificial intelligence in legal – with an increasing focus on the project management domain. From determining process flows and project plans for conducting a matter and estimating the cost using different billing approaches through to determining optimum resource allocation, managing workflows, maintaining a dynamic document hierarchy, budget monitoring, time tracking, variation analysis, and client reporting – AI has clear applications throughout the legal project management lifecycle. Given that such applications have already been discussed and assessed quite widely elsewhere, we decided to wind the clock forward to explore how the potential applications of AI in legal project management could evolve over the next three to five years. To this end, presented below is a scenario of what might be possible by 2020 in the more advanced adopters of AI in legal.

9:00 am Monday June 1st 2020, Janet reports to work as a legal project manager at the shared office building where her law firm leases co-working space. The start time to her day is the only traditional thing about her job. Janet works for NFW, a top 20 global law firm, which relocated in 2019 from a plush city centre office building to a practical and economical co-working cooperative. The legal team shares conference rooms and some support staff with the other tenants, which include technology startups, graphic designers and consulting firms. The working space is diverse and a bit chaotic, but always occupied.

When not being used as office space, the building is used for pop-up adult education courses, retail shops and civic meetings. This saves NFW money and helps build a presence in the community, giving a local feel to their global firm. NFW’s previous location was a beautiful old building that used only fossil fuel energy, and was poorly retrofitted with the efficiency boosting detectors, beacons and transmitters that today’s smart offices rely on. Plus, it was very expensive real estate. The shared office is cheaper, more ecologically sound and much more high-tech. Furthermore, the reduced overhead allows the firm to spend more on IT and HR, two areas where the legal industry is finally starting to see significant returns on investment.

NFW was one of the first of the global law firms to pioneer a new client service model where an AI-enabled project manager is assigned to orchestrate the conduct of a matter from the outset. With an increasing technology component in many projects and the growing use of non-legal staff including business consultants, researchers and accountants, several law firms decided to put in place legal project managers to ensure the effective integration and co-ordination of all other elements involved to deliver the desired outcomes to the client. While some lawyers objected to what they felt was an undermining of their roles, others appreciated the time it freed up for them to focus on providing sound business-orientated legal advice and keeping their own expertise up to date. A key by-product of this team approach coupled with the use of AI billing tools has been a massive reduction in billing disputes, write-offs and payment delays –issues which many lawyers had traditionally found it uncomfortable raising with clients.

The workday begins with a chat with her personal digital legal assistant, Lawrence, an AI helper who has been with Janet for a year now. Although Janet has a very personal relationship with Lawrence, he is in fact a firm wide legal management environment that effectively supports all aspects of legal project management and provides personal assistant services to professional staff across the firm. Lawrence was programmed to respond to moods and promote the mental and emotional wellbeing of its human companion.

Today, Lawrence notices from Janet’s biometric data feeds that she is tired; she worked all weekend from her home office. Lawrence communicates to the smart coffee machine to make Janet’s cup extra strong so that she can get started on her busy day. He switches on some ambient music which relaxes her. Janet doesn’t even realise that Lawrence has embedded some productivity enhancing tracks alongside the soft music that welcomes her. The sounds were designed just for Janet, based on her biometric feeds. HR ensures that worker productivity at the firm is kept at a maximum by using personalised approaches to stress-reduction and efficiency. A biological and genetic profile of each employee is created for the purpose of customising their environment to enhance each individual worker’s potential. Janet is quickly revitalised and they begin to review the day’s activities.

Lawrence uses hologram displays to show Janet the daily briefing. The first thing on the agenda is to review the dossiers on the clients involved in a potentially big M&A case the firm is bidding on. Full profiles of the companies and the CEOs involved in the merger begin appearing in a 3D video with an amalgamation of data gleaned from the internet and their social media profiles. The presentation gives her a better understanding of the client’s history, motivations, and the context of the job. Above all, it will provide the information necessary for the team to communicate effectively with these clients about the context of the merger and NFWs understanding of the subtleties of the transaction. This also allows Janet and the lawyers she works with to provide a special finesse to the account management, billing and scheduling part of each working relationship.

To create the video, Lawrence trawled the internet, as well as several large databases, to pull in relevant, up to the minute transaction related details for the 10:00 am virtual meeting. The information is presented in a way that Janet is most comfortable with: visual and image-based. Lawrence confirms that the lead lawyer Sarah is happy with the content, and that she is ready to join Janet in this virtual call with the clients to offer the custom detailed legal service package Lawrence has prepared to the exact specifications of the various contributing team members – with oversight from Janet. While it took a while to get the teams working in this highly collaborative AI enabled manner, the model is working well now – with the personal team building, conflict resolution and co-ordination skills of the project manager being seen as the critical differentiator between the best performing projects and the rest.

With what Janet now knows about the client—their likes, dislikes, habits and history as analyzed by her AI assistant—she realizes they are possibly going to ask for alternative service options beyond the bid Lawrence has created. Lawrence can provide a set of delivery options with associated pricing, ranging from highly automated execution through to heavy personal involvement from the lawyers and other key members of the team. The more human involvement, the more expensive the job, but this has become a hallmark of the legal industry and Janet knows that clients appreciate having this range of choices to select from.

Often, particularly with more complex matters and new developments such as the introduction of autonomous vehicles and human enhancement centres, the clients involved are willing to spend more to get the input and oversight of human experts. However, there are an increasing number of situations where clients accept that, for the matter in hand, a ‘robolawyer’ will be just as good—and much more cost-effective—than a person. The firm is not bashful about this reality. Law firm automation is not just happening in the management and accounting functions, it is helping to redefine the entire firm and most other white-collar industries.

Lawrence performs several jobs but his most important contribution is the ability to analyse ever larger data sets to extract usable information for bids and proposals, spot interesting patterns, correlations and anomalies and help apply that knowledge to proposal development, project planning and workflow management. One of the main benefits of Lawrence’s software is the ability to predict case outcomes. Form the early 2010’s onwards, a range of innovative software tools such as Lex Machina started to emerge, predicting the outcome of court cases based on previous results. NFW believes there is still competitive advantage to be gained in this arena and has developed a proprietary algorithm within Lawrence which takes all the factors of a case into account, compares the information to previous cases in public databases, and then determines the probability, costs and likely financial impacts of a range of possible outcomes.

The ability to predict outcomes has technically lost the firm revenue – as clients won’t pursue a case if Lawrence believes the results won’t be in their favour. However, the resulting reduction in legal costs has won the loyalty of many important clients, and has shown the firm to be a source of solid, evidence-based advice. Seamlessly, this has become a new source of revenue for the firm: sales of the predictive outcome service to clients of other firms have more than made up for the loss of revenue. The algorithm can also predict the time and costs involved in each case down to the minute and penny. Hence clients that choose to take a case to court can do so confident in the likely outcome and associated costs. NFWs AI outcome prediction strategy has earned it an excellent reputation for putting client needs first, having never lost a case since its implementation. This implicit guarantee of customer satisfaction has helped drive exponential improvement in the rates of revenue growth, client attrition and new client acquisition.

After lunch Janet checks on the progress of new applicants to work with her in a (human) junior project manager role. The firm uses AI enabled gamification based on role playing real life client scenarios to identify and recruit job candidates, a trend that is really starting to take root in the sector. The idea is to identify those who have the technical knowledge, social skills and emotional intelligence to perform effectively in dynamic multi-disciplinary client facing teams. The firm’s website and social media pages include a link to the game, and high scorers are invited to submit CVs when there are new job openings.

Instead of having a huge file of résumés to read through, Janet checks the scores and team behaviours of the latest batch of applicants. She sees that one candidate, a graduate of an online university, has some very good point scores on the core technical and social interaction requirements, but is also doing well on the strategy and critical thinking criteria, which the gamers probably don’t even realize they are being tested on. Janet remembers when her children’s school had an unexpected closure and she had to bring her 10-year-old son to work with her one day. She let him play the recruitment game all afternoon and he had no idea that it was anything different from the video games he played at home—and Lawrence said his scores suggest he’d make a great lawyer!

Before the day’s end, there would be a big meeting related to the new hire. There would be several departments represented there, including IT, since the candidate needed to have some fairly strong coding experience. The person would be working alongside Janet and Lawrence to train a new piece of AI software, one which, for now, they jokingly call “Big Brother.” Essentially a surveillance technology, the new AI takes the form of a wearable device meant for attorneys. It would be placed on the person’s body, having the appearance of a badge or piece of jewellery, but it would actually use cameras, sensors and recording devices to monitor the entire workday’s activity to simplify the billing process. The device could even pick up on the attorney’s brain patterns to identify when he or she was thinking about a client, to capture all billable moments.

With Big Brother, the firm was hoping to pioneer a to-the-second billing structure, which would allow profits to grow while ensuring the highest possible accuracy in billing. The software also has the ability to recognize the difference between deep thought, reflection and analysis and more routine review and drafting tasks – allowing for truly differential pricing for tasks performed by the same person. This would also allow lawyers to focus more on the core aspects of their job, and made roles like Janet’s much easier. She and her staff (with their AI helpers, of course) should now be able to analyse and monitor the device output in real-time and prepare detailed financial reports at the request of the lawyers, accounting or the client—this is where the coding skills come in.

Another “as-a-service” niche the firm is hoping to fill is that of litigation data analysis, providing clients detailed insights into the time and resources spent on legal services both by their law firms and their own internal legal departments. Hence the new hybrid role would work with IT, HR and legal project management – so there would be a lot of cross-over involved, something Janet was seeing increasingly with the new project team approach. The sensitive nature of the data being handled by Big Brother meant HR needed to be involved to monitor both the human analyst’s adherence to privacy rules, and the evolving behaviour of the system itself, to make sure it didn’t start to cross ethical boundaries as it adapted and evolved.

As Janet was leaving the office she saw that the conference room where they held their meeting today was being set up for a learn-to-code class. She thought of possibly taking the course herself, coding being something she never learned in her youth, when it was really cutting-edge. Maybe she could learn to reprogram Lawrence to order her a lower strength morning coffee and turn down the welcome music.

 

 

 

Image: Aykut Aydogdu

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