Not every client:
But every client in the legal industry has legal bills.
And I don’t know about you, but I opened some of our legal bills and been taken back by the charges. In many cases I’ve had to go back and negotiate what seemed more reasonable and fair for my clients.
Every year in the United States, lawyers record their work activities in one-tenth of an hour increments.
Law firms biggest challenge is pricing uncertainty, waste and inefficiency. Every year, over $60 billion is “up for grabs” between clients and their law firms.
For an industry that uses a
Time x Hourly Rate = Price
equation as its core pricing model, it is no wonder that $60 billion of pricing uncertainty is the industry’s primary economic challenge.
External forces have irreversibly impacted the time it takes a lawyer to do his or her job and the hourly rate which that lawyer can charge.
Without data analytics tools, law firms and clients are relegated to a circular, unscientific game where one side is forced to increase rates to sustain profitability levels and the other side is forced to ask for larger discounts of ever-increasing rates to stay within budget.
Leveraging AI and ML, Legal Decoder’s software analyzes complex legal spend data for law firms and their clients. Its software transforms raw data into strategic insights to help law firms and their clients price matters with greater accuracy and measure outside counsel’s efficiency levels.
Legal Decoder’s software analyzes “who” (legal professional credentials) did “what” (work elements identified in narrative) and “how long” it took on a line item-by-line item basis. This was until recently all being done manually.
Then the software starts categorizing the data and showing industry-specific pricing trends. At the same time, Legal Decoder’s software measures:
1) staffing efficiency ensuring the right level of legal professional handles a task appropriate for his or her skill level.
2) workflow efficiency to surface waste and redundancy in workflow processes
3) billing hygiene to ensure legal bills clearly reflect accurately recorded time.
The algorithms in Legal Decoder’s software “learn” by leveraging trained ML models from hundreds of thousands of legal bills. Every time a new bill flows through their software it allows the AI models to get smarter and more accurate. This eclipses any human capability.
In the United States, the judicial system is an open system with much of the information about the case, the litigants and the judge available in the public domain. The amount of litigation data available is head-spinning.
Historically, it has been impossible to cull through all the data to see any patterns, trends or insights from it.
Lex Machina developed AI and ML technology that transforms much of this public information into strategic insights. Its technology crawls millions of pages of messy, unstructured, litigation dockets and documents every day.
The system uses Natural Language Processing and ML to clean, tag, and structure the data.
Now the litigation data can be mined, revealing insights never before available about judges, lawyers, parties, and the subjects of the cases.
On the basis of previous case win/loss history, previous case law and a judge’s history, lawyers can use data points which can be utilized for patterns and trends.
From a practical perspective, law firms can use this data to pitch and land new clients and win lawsuits. Clients can use the data to select and manage outside counsel and set litigation strategy.
When reviewing contracts, lawyers identify risks and issues in contracts that could have negative implications. Many provisions in contracts are considered “boilerplate” in that they are less important than the core terms of a contract and usually not heavily negotiated terms.
AI tools can identify problematic contractual terms or missing terms and alleviate much of the laborious and time intensive nature of analyzing contracts.
Similarly, legal professionals conduct due diligence to uncover background information that could be relevant to a transaction or matter. Due diligence involves document analysis, confirming facts and figures and thoroughly evaluating other pertinent materials to gauge potential risks.
AI tools are automating a significant portion of due diligence to allow legal support professionals to conduct due diligence evaluations more efficiently and with more accuracy.
As applications of AI and ML tools proliferate, and the benefits of increased accuracy and efficiency become more tangible, clients will expect AI and ML to apply to even more legal services and functions.
Even though the slow adopting legal industry has made great strides with the implementation and integration of AI and ML, the greatest potential has yet to come.