Gen AI Drives Law Firm Innovation, But Clients Must Come First

While there’s growing consensus and acceptance that generative artificial intelligence will shake up the practice of law, any technology that’s leveraged should meet the needs of clients.

Generative AI will make lawyers more productive, though the effect likely will be uneven—at least in the near term. Most efficiency gains will be in relatively straightforward work—think extraction and summarization.

Substantive analysis in particular matters is possible, but harnessing this capability will take careful collaboration with clients so they’re comfortable with the approach and trust the technology.

Improvements will be incremental rather than disruptive, such as having a faster horse rather than a car. Efficiency will matter only in service of what lawyers ultimately deliver: specialized analytical thinking, experience, and judgment to affect a legal solution to a business problem.

At this stage, the possibilities seem endless. It’s possible to codify legal analysis so law firms can be more efficient. Attorneys may manage risk and resolve disputes in new ways through automation and AI.

However, Silicon Valley is replete with examples of companies that were correct in their long-term vision but never crossed the chasm of mass adoption. To avoid repeating that mistake, start from these principles:

• Success ultimately depends on solving a problem people actually have. This drives product market fit and adoption.
• Usually, the underlying problem is acute and immediate, such that your early adopter is willing to try just about anything.
• Software is never perfectly conceived, implemented, and scaled before a single customer uses it. This is true even for the likes of Google and Facebook. It will be true for law firms too.
• You can get insight into the particular workflow problems of a law firm from inside the law firm.

Law firms will benefit from having a centralized place where all problems flow, such as an innovation team. That team will gain an understanding of the problem flow and form solutions from the available technology. In some cases, the solution can be used again and integrated with product development.

These products will be rough at first. To succeed, firms will need a framework for assessing potential use cases (think product market fit) if they want to implement the product in practice groups that aren’t actively seeking a solution.

They also will need a way to measure return on investment empirically rather than anecdotally—for example, how much time is saved. Finally, they should convince attorneys that it benefits them to think through their workflows. You must know your processes before you choose your tech.

Many of the solutions discussed above will be non-repeatable, but that’s OK. Lawyers want to solve their clients’ particular problems, not create a technology that is generally useful. Issues and facts will depend highly on context.

If writing a little bit of code can have an immediate and significant impact for a client, lawyers will do it. Scaling it across the rest of the practice will be a bonus.

Some of these services will be data-centric, meaning the legal analysis involves data sets of such size and complexity that attorneys reasonably won’t be expected to do it on their own. They will need to work with data scientists to effectuate the analysis. The results may look like magic but are really just a bit of math and code.

This won’t be limited to large sets of Excel files; it may include identifying, extracting, and analyzing text in novel document sets. Think of it as using data science to ascertain the facts to better apply the law. All other commentary is just a variation of that theme.

The key is for law firms to use AI to solve real problems when other tools don’t quite meet their needs. Technology should be useful, not sophisticated. Intelligence can augment decision-making even if it doesn’t mimic human cognition.

Current generative AI tools can reduce overall fees and augment services, but they’ll be even more successful when law firms and clients understand their flows and work with data in new and complementary ways.

Firms that treat both workflows and data as a core competency will have an interesting story to tell. Clients will want to hear it.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law and Bloomberg Tax, or its owners.

Author Information

Matthew Dunne is Katten’s senior innovation and data science manager. He evaluates and implements AI tools into law practice and is a technical adviser to attorneys.

Andrew Sprogis is Katten’s chief innovation officer. He and his team enable the firm’s attorneys, business professionals, and clients to innovate their approach to law and business.

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