Managing the increasing cost of external counsel with AI

Last year has seen several AM Law 200 firms increase attorney salaries. While others are yet to confirm any increases, the raises are expected to increase pressure on how often in-house engages external counsel. Engagement is expensive and there are usually in-house limits on external spend. As external counsel provide valuable specialist assistance, in-house will still need to engage them but with more focus on achieving best value from money spent. So how can this be achieved where the cost of external counsel is increasing? To create a win-win, in- house should consider adopting AI technologies to offset rises in external counsel spend. By driving efficiencies in other areas, in-house can achieve net-zero where they retain the benefits of engaging external counsel despite any cost increases.

A traditional approach of doing more in-house and engaging external counsel less can be effective in managing costs, but it has significant drawbacks. It can be effective because under time-based billing models the degree of engagement with external counsel has a direct impact on their cost. All else equal, as engagement increases, so does the cost. While effective, there are drawbacks where in-house loses the specialist assistance and capacity that external counsel provides. If, by failing to engage external counsel, in-house cannot provide timely and effective advice on a matter, this can have adverse consequences for the business. Any perceived savings from not engaging external counsel due to cost may be worse than if in-house engaged external counsel at the outset.

This will not be news to those who understand the value of engaging external counsel where appropriate. What may be news, however, is that in-house can use technology that leverages artificial intelligence (AI) to benefit from external counsel while managing their cost. In the same way that electronic means have reduced or even eliminated the cost of more traditional methods, AI can reduce the time and cost involved in areas of in-house work. This can have various benefits, including in-house lawyers spending time in more meaningful and valuable work. For example, an AI tool that can identify an NDA request, review the draft and provide a response without it ever having to be reviewed by the in-house team, saves time and money for the business. Or an extraction tool that can review a large document set and extract key information reduces the time and cost of human review. These kinds of solutions generate time and cost savings that can be used to reduce expenses directly or offset the increasing costs in other areas, such as external counsel. They can also help reduce risk by ensuring that timely and effective advice is provided, whether internal or external.

Choosing an AI technology for a specific need is important. There are many possible solutions, and just adopting anything can be like trying to hit a target in the dark. It’s impossible to know where to aim or if success has been achieved. To ensure that technology best meets the need, in-house should consider the potential ROI that an AI solution is likely to generate before deciding whether to purchase. A reasonable level of confidence is needed that the tool will generate the desired level of savings. If not, it is likely to fail to achieve its goal, and the in-house team is likely to have a negative experience with new technology. This hurts the bottom line and makes future adoptions of technology more difficult. By knowing the ROI, in-house will have a clear target and can determine if it has been achieved.

While knowing about the need for ROI is simple, calculating it can be much more difficult. There is more than one way to calculate ROI and many factors may be relevant depending on in-house needs. They can include factors that are as diverse as the present value of cash, team efficiency metrics, and the reduction of time in client transactions. That there is not just one way to calculate ROI can make the assessment difficult because it can seem like an apples versus oranges comparison. The key here is to know the problem in-house is trying to solve with an AI solution and making an informed prediction as to the likelihood a particular tool will achieve that. There may be a clear winner from a range of available tools, but this is not guaranteed. It’s important to analyse this so it can be communicated to the level where the decision will ultimately rest. Once a decision is made, in-house should be able to measure whether the ROI has been achieved and communicate this back to the C-Suite.  

Instead of trying to engage external counsel less, in-house should consider adopting AI technologies to offset their increasing costs. While this concept is not new, being able to do this through AI is a relatively recent phenomenon. If in-house is considering this approach, they should understand their own needs and how a potential solution will meet them. This will enable in-house to assess the potential ROI of a technology and whether the likely savings will be worth the investment. By reducing costs through AI, in-house can ensure that budgets are available for other areas of their work, such as external counsel. By doing so, in-house can retain their specialist assistance while managing cost, and create a win-win.