Human-in-the-Loop Isn’t Optional: The Real Future of AI in Law with Sam Dixon

What happens when a traditional profession meets generative AI, and now agentic AI? In this episode, I’m joined by Sam Dixon (Chief Innovation Officer) to unpack how law firms can adopt GenAI responsibly, train teams to critically evaluate outputs, and use AI to deliver more value, without burning out in the process.

We cover what GenAI is (and isn’t), why humans must stay in the loop, how to overcome resistance to change, the reality of “hallucinations,” and what the next phase, agentic AI, could mean for legal research, negotiation simulation, and early-talent development.

Timestamps

0:00 – Intro

1:39 – Sam’s role as Chief Innovation Officer & how he defines innovation

5:29 – Getting partners to embrace change (and what actually works)

8:39 – Why GenAI adoption is different from past legal tech waves

10:26 – Explaining AI to senior leaders (without the jargon)

14:49 – “Human in the loop” + the risks of over-trusting AI

19:26 – GenAI vs Agentic AI: what changes, and why it matters

22:46 – New lawyers, verification skills, and “AI makes people lazy?”

33:11 – Simulation: negotiation practice, interviews & training with GenAI

47:02 – AI & well-being: tool or threat?

What we cover

• The real job of innovation inside a law firm

• How to get buy-in from busy, skeptical stakeholders

• The difference between AI, ML, GenAI, and Agentic AI

• Why hallucinations are misunderstood, and why that matters

• Guardrails: verification, accountability, and client expectations

• The future of training: simulations for negotiation, interviews, and client conversations

• AI and burnout: productivity vs cognitive load and recovery

• Innovation isn’t just tech, sometimes it’s removing a step entirely.

• GenAI adoption is faster because users can get value immediately, unlike tools that require heavy setup.

• Agentic AI adds value through multi-step planning, not just answering prompts.

• “Hallucinations” aren’t a rare bug, GenAI always generates probabilistically; the key is how you manage risk.

• The winning model is human + AI, where each catches different kinds of errors.

• As outputs become “more accurate,” verification discipline becomes even more critical, not less.

• AI may improve efficiency, but well-being depends on how work is redesigned, not just sped up.

Guest Resources

Sam Dixon’s Legal Firm: https://www.womblebonddickinson.com/uk/people/sam-dixon

Sam Dixon’s LinkedIn: https://uk.linkedin.com/in/innovationinlaw

Connect with Me

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Timestamps What we coverKey takeawaysGuest Resources