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Client Expectations
3 minute read | 3 weeks ago

How Clients Think about AI

Photo of Nathaniel Slavin By: Nathaniel Slavin

Over the past six months, the topic of AI has been part of most client feedback interviews. As is expected, the industry is far from reaching anything resembling consensus, even within corporations and legal departments. While much has been reported on the potential of AI, candid comments from those who are tasked with implementing it reflect the wide variety of opinions on its place in the legal landscape. Despite that, the “one size fits one” rule of relationship management is still of utmost importance: meeting clients where they are going and delivering extraordinary value.

Some of the themes uncovered in these conversation include:

Budget for AI: Vendors are already pitching corporate legal departments on tools that can be used for AI, but corporate legal departments are moving very slowly on any capital expenditures. Many will expect law firms to pilot trials at the firm’s expense to hedge against this reality. Expect more cost to the firm up front to try tools that will reduce outside counsel fees.

Law firm efficiency and correlated reduced costs: Every micro generation of lawyers has experienced a changing landscape of research and analysis: From law libraries to DVDs to internet research, the speed at which information can be gathered increases. Clients continue to expect their firms to do routine low-value research and early drafting of arguments faster and faster while retaining the highest level of quality.

There is a broadly consistent perspective that two things will happen in the coming years: 1) Law firms will figure out how to use AI to accomplish the above, and 2) Clients will not have to pay for that work. The impact of this will have far-reaching implications. Companies started refusing to pay for first-year associates not long ago; soon they will not pay for the creation of work product.

Predictive analytics: One company is thinking about how AI will allow them to research motions and briefs in front of individual judges and identify complex language in rulings to determine what arguments will sway which judges.  

Other vendors are pitching on use-cases for AI with a similar hope of reviewing patent portfolios to run competitive comparisons and determine which should be leveraged for enforcement or licensing, for example.

Spend analysis: Law firms submit budgets, but then things change and clients must revise budgets. For highly routinized or high-volume work, AI will make it much easier to predict spend and anticipate the future potential costs of strategic decisions.

Contract analysis: Many companies continue to face the challenge of non-conforming contracts and agreements in high-volume workstreams. AI can already analyze large volumes of information, find non-conforming agreements and fix them.

Compliance: This large category includes everything from monitoring and reporting to risk assessments as well as data privacy compliance for GDPR or CCPA by automating data mapping and doing privacy impact assessments—and doing it very, very fast with updated data sets in real time. AI will also be used for policy enforcement and/or vendor risk assessments as well as for reporting on such things as ESG to meet investor and regulatory expectations.

Massive Tension: Tension between legal departments, within legal departments and with outside counsel will continue along with the very different opinions people hold on the proper use of AI. This will not go away soon, but if the conversations are not already happening between the client and the firm, start those conversations now. Most importantly, find the person in the firm who can speak to this topic. It is highly unlikely that the relationship lawyer will be the right person from the firm to ask about the firm’s use of AI. And once you have that discussion, review the updated outside counsel guidelines and ensure you understand what you can, and cannot, do with AI.

If you aren’t having those conversations, you will be at a competitive disadvantage and one that will force the client to wonder, “Why have they not invested in resources to answer the question of how AI will make our lives easier, reduce costs and give us better outcomes?”