Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I started by managing large-scale data platforms for consumer banking, which set the base for building a cloud-native AI/ML environment. That platform now supports a wide range of internal models across risk, lending, and customer engagement.

Q2. What, in your view, truly makes an enterprise “AI-ready” today—maturity in data infrastructure, a solid cloud strategy, the right org design, or something else?

Being “AI-ready” means three things working together:

• Treating data as products with quality/lineage/SLA,
• Having a flexible cloud/hybrid setup
• Having controls and compliance built into the workflow.
  Because of that, we can turn an idea into a governed service very quickly.

Q3. How do you see leading banks navigating the shift toward in-house GenAI platforms or fine-tuned LLMs?

Leading banks are taking an “adopt then tailor” approach to GenAI: start from a strong foundation model, add retrieval over internal knowledge, fine-tune on enterprise-safe data, and run it inside the bank’s own secure environment. That way, you get speed while still keeping data, guardrails, and audit in your own perimeter.

Q4. What do you think are the biggest lessons from scaling GenAI in highly regulated banking environments?

In regulated environments, three lessons keep coming up:

• Regulators want evidence, so logging and traceability are non-negotiable
• GenAI outputs must be explainable and tied back to sources, and
• You need people who understand both banking/regulatory language and AI/ML so they can bridge model owners, risk, and audit

Q5. In your opinion, which categories of AI startups or tech vendors are gaining traction within banks?

Banks are most interested in solutions that:

• Make AI governance and policy automation easier
• Integrate well with existing data/feature platforms, and
• Protect models from misuse or low-quality prompts before they hit production

Q6. Which enterprise GenAI startups do you think are becoming the go-to options for specific banking needs?

For common use cases (service, operations, developer productivity), banks prefer tools that are easy to integrate with existing systems, support enterprise controls, and can be deployed in a private/banked environment rather than “one-size-fits-all” public tools.

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

I would ask: “Show me your architecture and operating model for a single-tenant, governed deployment, including performance when all guardrails are on.” If a vendor can’t show that clearly and simply, they’re not ready for a bank.