Tom Gricks, Managing Director of eDiscovery at OpenText, joins the podcast to discuss the evolution of Technology-Assisted Review (TAR), the legal industry's resistance to AI-driven workflows, and how Generative AI is reshaping document review. With a unique background in chemical engineering and law, Tom shares insights into the early battles over predictive coding, the transition from TAR 1.0 to TAR 2.0, and the future of AI-powered legal technology.

Key Takeaways

1. The Intersection of Engineering & Law – Tom Gricks shared his unconventional journey from chemical engineering to law, highlighting how his analytical mindset shaped his approach to eDiscovery. He explained how engineers and lawyers think differently but ultimately solve complex problems using structured methodologies.
2. The Evolution of Technology-Assisted Review (TAR) – Tom argued the first case in the U.S. advocating for TAR, overcoming resistance to predictive coding in litigation. He outlined how TAR evolved from 1.0 (seed set training) to 2.0 (continuous active learning - CAL), reducing document review costs and improving efficiency.
3. Generative AI’s Role in eDiscovery – Tom provided an insightful comparison between TAR and Generative AI, warning against overhyping AI’s potential while acknowledging its growing role in automating document review. He stressed the importance of validation, transparency in prompts, and integrating AI into existing workflows rather than replacing traditional review methods entirely.

Action Items

✅ Understand Validation – If you’re using TAR or AI in eDiscovery, ensure robust validation measures to confirm accuracy and defensibility.
✅ Bridge the Knowledge Gap – Legal teams should collaborate with data scientists to understand machine learning models and optimize AI-assisted review.
✅ Prepare for the Future – AI-powered tools will continue to evolve, so firms must stay agile, incorporating AI alongside traditional methodologies rather than prematurely discarding proven tools.

Chapters with Timecodes

⏱ 00:02 – Introduction & Guest Welcome (Tom Gricks)
⏱ 00:06 – Tom’s Engineering-to-Law Journey: A Non-Traditional Path
⏱ 00:14 – The Early Days of eDiscovery & Predictive Coding Resistance
⏱ 00:24 – The Landmark Case That Introduced TAR to Courts
⏱ 00:35 – TAR 1.0 vs. TAR 2.0 – The Shift to Continuous Active Learning
⏱ 00:45 – Why Some Firms Still Resist TAR and What Needs to Change
⏱ 00:54 – Generative AI in eDiscovery: Is It the Next Big Thing or Overhyped?
⏱ 01:05 – The Future: AI + TAR for Maximum Efficiency

Most Compelling Quote

"Lawyers tend to be risk-averse, but what they don’t realize is that they got worse results before technology than they do with it. The challenge is not whether AI works—it’s whether we trust what we don’t understand." – Tom Gricks

Resources:

• Visit the NeLI's website at: www.ediscoveryleadership.org
• Visit UMKC School of Law: law.umkc.edu
• Tom Gricks: https://www.linkedin.com/in/tom-gricks-5259a71a 
• Daniel Gold: https://www.linkedin.com/in/danielegold 
• Brandon Mack: https://www.linkedin.com/in/brandonmack 

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