Desiree Sainthrope stands at the intersection of traditional legal expertise and the cutting edge of digital transformation. As a recognized authority in global compliance with a deep interest in the evolving implications of generative AI, she has spent years helping organizations navigate complex regulatory frameworks while integrating new technologies. During our discussion, she delves into the findings from Legalweek 2026, exploring how the legal sector is moving beyond simple cost-benefit analyses to a more nuanced understanding of AI’s value. Our conversation covers the strategic frameworks for identifying ROI, the tension between measurable economic gains and qualitative improvements, and the challenges of scaling tools that promise to do what was once deemed impossible.
When tracking the economic impact of AI, how do you balance measurable time savings and hourly rate shifts against the more subjective improvements in work quality? What specific internal benchmarks or client feedback loops do you implement to ensure these quality gains are actually being realized?
We look at this through a framework of three distinct factors: quality, quantity, and quantum. Quantity is perhaps the easiest to track because you can physically measure the time saved in producing work both for the client and for the firm’s internal operations. By analyzing rates per hour and other economic markers, we can see exactly where the efficiency gains are hitting the bottom line. However, the quality aspect is equally vital, requiring us to identify if the work product has visibly improved through the data we already possess. We ensure these gains are real by assessing whether we are producing higher-quality information internally and externally than we were before the integration of these generative tools.
Many organizations struggle with “quantum” ROI because it involves capabilities that previously seemed impossible. How do you pitch the value of untapped potential to stakeholders, and what practical steps can a firm take to test a tool’s capacity for scale before officially bringing it in-house?
“Quantum” ROI is inherently difficult for law firms to wrap their heads around because it is based entirely on potential rather than historical data. It represents the ability to do things we could never dream of doing today, providing a level of capacity and scale that was previously out of reach for even the largest departments. To pitch this to stakeholders, you have to emphasize that a wait-and-see approach often prevents a firm from understanding what a tool can truly do in a live environment. The most practical step is acknowledging that it’s nearly impossible to judge these capabilities without actually bringing a tool in-house for rigorous testing. By seeing how others in the industry are starting to adopt these tools, firms can find the inspiration to explore how that same technology might unlock their own unique, untapped capabilities.
Beyond basic usage, how do you calculate the ratio of augmented processes against the total scope of potential AI applications? In what ways does collaborating with a dedicated analytics team help refine these adoption metrics, and how do you determine if you are moving at the right speed?
Calculating the true impact requires us to measure adoption and integration far beyond simple login numbers or basic usage statistics. We focus on how many specific processes we have successfully augmented compared to the total scope of what we could theoretically achieve with the technology at our disposal. Working closely with a dedicated analytics team is essential here, as they help us develop sophisticated metrics that show exactly how fast we are moving across different departments. This collaboration allows us to move beyond the traditional balance sheet and understand the depth of integration within our legal project management operations. Ultimately, knowing the ratio of transformed tasks helps us determine if our pace of adoption is keeping up with the rapid evolution of the market.
Since observing peer adoption can often trigger a push for new technology, how do you distinguish between following a trend and identifying a genuine use case? What criteria do you use to evaluate if a specific generative AI tool fits your organization’s unique operational architecture?
It is very easy to be swayed by what other firms are doing at events like Legalweek, but a genuine use case must be rooted in your organization’s specific data and operational needs. We evaluate a tool based on whether it can produce high-quality information from the specific datasets we handle daily, rather than just performing generic tasks. The distinction between a trend and a use case often comes down to whether the tool provides a “quantity” benefit, like saving hours on a task, or a “quality” benefit that fundamentally improves the final work product. If a tool doesn’t align with our strategy for collecting ROI metrics or doesn’t offer a path toward that “quantum” scale we desire, it’s likely just a trend. We rely on internal testing to confirm that the technology isn’t just a shiny new object but a fundamental upgrade to our existing architecture.
What is your forecast for the future of AI ROI in the legal industry?
I anticipate that the legal industry will move away from viewing AI ROI as a simple math problem of hours saved and toward a more holistic integration model. We will likely see firms becoming much more comfortable with the “quantum” aspect of ROI, as they realize that the true value lies in expanding their capacity to handle complex work that was previously impossible. Analytics teams will become permanent fixtures in law firms to track these three-factor metrics—quality, quantity, and quantum—on a continuous basis. As firms stop searching for a single magic number and start measuring the breadth of their augmented processes, the success of AI will be judged by how deeply it is woven into the firm’s very DNA. Strategies will become more sophisticated, moving from simple adoption to full-scale operational transformation.
