AI is no longer a chatbot. It is an agent that can move data and make decisions.

In this in-studio conversation, Leo Arzumanyan, Matthew Luciani, and Jeremy Stock cut through the hype and get practical about using AI in payments. We start where risk lives: privacy, closed versus open loops, and how to keep sensitive underwriting logic and merchant data inside your walls. Then we map the real use cases operators are deploying now: CRM ingestion, sales intelligence, document checks, and dispute workflows that turn noisy inputs into usable signals.

You will hear a clear-eyed view of model choice and control. Free models are fine for quick searches. Paid models and tuned agents belong in underwriting, portfolio analytics, and customer operations. The team explains how to set boundaries, why hallucinations happen, and how to keep an agent from freelancing outside your rules. We also tackle the organizational impact: which entry-level tasks will change, why experts must stay in the loop, and how to write ethical and operational guidelines that keep you compliant while you scale.

What you will take back to your team
•A simple governance plan: closed data loop, role-based access, red-team tests, and an incident path when an agent is wrong
•A deployment map: CRM ingestion, underwriting triage, post-payment risk checks, and dispute assembly with human review
•A safety checklist: consent and privacy prompts, model provenance, logging and evidence retention for audits and insurers
•A portfolio lens: use AI to raise approval rates, shorten dispute cycles, and find at-risk MIDs before attrition hits
Bottom line: adopt with intent. Train models on your domain, keep experts in the loop, and instrument every step so AI reduces risk instead of adding it.

Wondering where AI truly helps—and where it quietly raises the stakes? We dig into the real-world shift from chatbots to agentic AI and map the line between useful automation and unacceptable risk across payments, legal, and healthcare. From CRM workflows and underwriting logic to privileged communications and HIPAA concerns, we share practical guardrails to protect client data, trade secrets, and your competitive edge without slowing down innovation.

We compare leading models—GPT, Gemini, Claude, and Grok—through the lens of enterprise needs: reasoning quality, context windows, customization, and the difference between free tiers and paid, closed-loop deployments. We unpack why “paid is safer” isn’t just about accuracy; it’s about governance, logging, and the ability to constrain learning on sensitive inputs. You’ll hear concrete examples of how poorly scoped prompts and thin domain knowledge can produce confident, wrong outputs, including a contract that looked fine until expert review revealed major gaps.

The conversation also tackles a hard question: who should set the limits? We weigh user-driven controls against platform-imposed restrictions on legal and medical advice, arguing for transparent refusal reasons and identity-based access where appropriate. Ethics are lagging the tech, so we outline a practical playbook: define your AI usage policy, set role-based permissions, preflight prompts with boundaries, label unverified outputs, and route high-impact decisions to human experts. The near-term future of work will favor professionals who pair deep subject knowledge with strong model orchestration skills.


**Matters discussed are all opinions and do not constitute legal advice. All events or likeness to real people and events is a coincidence.**

PEP Links:
https://www.globallegallawfirm.com/podcasts/
https://www.buzzsprout.com/2176695

A payments podcast of Global Legal Law Firm