Imagine being able to know how much time and money it will take to resolve a matter, before even beginning work. Or, moving forward with confidence, knowing that the firm you choose will offer the best possible outcomes and representation for your particular needs.
Complete certainty isn’t possible in the absence of a crystal ball, of course, but predictive analytics can offer the next best thing. Predictive analytics, fueled by AI technologies such as machine learning and pattern recognition, can help corporate legal departments (CLDs) and claims departments transform historical data into powerful predictions. With this information in hand, organizations can more accurately predict costs and litigation times, and even choose the best possible firms to use for different matters––all based on past performance and outcomes.
The latter is an exceptionally important tool to have in one’s arsenal. Typically, CLDs and claims departments choose law firm partners based on a number of factors, such as the work they may have done on a recent matter, or perhaps just on “gut instinct.” None of this is backed by data, however, and the firms that are chosen may not be the best firms for the job.
While AI and predictive analytics can help organizations choose the right legal partners, it’s important to understand what’s going on under the hood. The reality is that AI is only as powerful as the data that’s put in—and more data is always better, especially when it comes to choosing the best law firms.
Expanding data collection
CLDs and claims departments are increasingly seeing the importance of metrics and consistent reporting. They’ve begun systematically collecting a wide range of data points, from average case cost to settlement amounts and times and matter types. And that’s great. The more historical data you feed to your AI, including information about firms’ past performances, the more accurate your prediction engine will be.
Still, these metrics don’t tell the full story.
If one extreme is the complete absence of data—simply choosing a go-to firm because the relationship is long-standing without even glancing at cost or KPIs—the other extreme is becoming so robotically data-driven that you ignore the actual experience of working with particular outside counsel. These data points—legal spend, matter type, etc.—are forms of objective data. They are factual stats captured by something like a claims management system. But if you’re just using analytics to identify and choose the cheapest option, for instance, your decision-making is far too myopic.
By including subjective data in your AI inputs, though, you can find the ideal middle ground. Subjective data captures the day-to-day experience of working with outside counsel. For example, subjective data could be the impressions that a fellow attorney has about a particular firm. Or it could be how easy it is to ask the firm questions and get a timely response. A recent survey, for instance, found that hotlines and access to experts for quick questions were the most preferred value-adds from outside counsel.
The easiest way to collect subjective data is to ask involved parties to rate outside counsel as part of matter closing. A simple exit survey can boost the recommendations of your AI tremendously over time. Some questions to include are:
- How effectively did the firm communicate during the course of the matter?
- How timely was their work?
- How would you rate their level of expertise?
- How would you rate their overall performance?
Once you’re consistently collecting subjective data, it’s time to combine it with the objective data mentioned earlier. The goal is to parse large amounts of past performance data to make smarter decisions more quickly without losing sight of the experience behind a particular bill or outcome.
Combining subjective and objective data gives a complete picture of the efficacy of your firms. By inputting large amounts of subjective and objective historical data into your AI, you’ll be rewarded with actionable recommendations, like which firm to use for an upcoming matter.
Implementing something like the aforementioned survey sounds like a simple task, but it’s important to realize that you may be met with resistance. The same can be said for shifting from gut or consensus decisions to evidence-based ones. A recent survey showed that less than 15% of respondents thought their legal department was effectively using big data to deliver services. But understanding this shortcoming and wanting to help change it are not necessarily the same thing. Many people are resistant to change even when it’s in their best interest. This is especially true when people feel like their own expertise and experience is being undermined by a growing emphasis on analytics.
By getting people to buy into a big-picture shift toward data-driven decision-making, there’s a far greater chance they’ll engage in exit surveys and other data collection efforts. Establishing a strategic change management process can help produce such buy-in. Don’t underestimate how challenging this might be. But also, don’t be afraid. Change happens all of the time and generally breeds better outcomes.
Chances are, once your employees see how broader data collection can fuel smarter decision-making, the impact will snowball quickly. The right mix of subjective and objective data is tremendously powerful, especially when fed into a predictive analytics tool. It can shine a light on hidden patterns, remove bias, and make very reliable predictions—two outcomes that can take a CLD or claims department to the next level. When employees experience the benefits firsthand, resistance tends to evaporate.
The bottom line
Predictive analytics can be a game changer for CLDs and claims departments. For many important decisions, it’s the closest thing there is to a crystal ball. Its benefits are tangible and wide-ranging, expanding far beyond the ones outlined here.
To dive deeper into predictive analytics, download our complimentary whitepaper, Using Predictive Analytics to Choose the Best Law Firm.