AI Keynote Speaker for FinTech and Payments Leaders
From mobile wallets to fraud detection, Alex makes FinTech innovation practical
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Payments companies bet the business on trust every single transaction, and agentic AI is about to test that trust in ways most fraud models weren't built for. When an AI agent can initiate a payment on a customer's behalf, the entire authentication and liability model built over decades needs a second look. This keynote is built for the leaders making that call.
Why fintech and payments is different
FinTech and payments leaders operate in one of the most heavily scrutinized corners of AI adoption, where a model's mistake isn't an inconvenience, it's a fraud loss, a compliance finding, or a customer's rent payment gone missing. That's why AI in this industry has advanced furthest in fraud detection and risk scoring, areas where the cost of getting it wrong was already forcing rigor long before generative AI arrived. The firms moving fastest here are not the ones with the flashiest AI demo; they're the ones that already had rigorous model governance in place before agentic AI arrived and simply extended it.
Agentic AI changes the terrain again. Shopping and payment agents acting on a customer's behalf raise real questions about authentication, consent, and who's liable when an agent makes a purchase the customer didn't explicitly approve. Payments leaders need a framework for this now, not after the first high-profile dispute forces one on them.
Regulatory attention is intensifying globally, and the leaders who get ahead of it, building explainable models and clear escalation paths, are in a stronger position than those treating AI governance as a checkbox for the next audit. Boards and regulators alike are more forgiving of a firm that can show its reasoning than one that can only show a favorable outcome, and that difference will define who survives the next wave of scrutiny.
What this keynote delivers
- A framework for thinking through agentic AI's implications for authentication, consent, and liability in payments
- How fraud and risk teams can responsibly expand AI use without expanding blind spots
- What separates genuine AI governance from a checkbox exercise ahead of the next regulatory review
- A grounded view of where agentic commerce is real today versus still a pilot
- How to brief a board on AI risk in payments in language that holds up under scrutiny
Why Alex for fintech and payments
Alex's client work includes Visa, giving him direct exposure to how a major payments network thinks about AI risk and opportunity at scale. He ran a $1.1B innovation portfolio that generated $400M+ in revenue at Cisco, work that required the same rigor around risk that payments leadership demands. That combination of payments-industry exposure and enterprise-scale risk experience is what makes the framework land with an audience that has heard plenty of AI pitches already.
Frequently Asked Questions
What does an AI keynote for a fintech or payments leadership team cost?
Fees are five figures depending on format; a virtual session for a payments or risk leadership team is often under $10,000.
Does the session cover agentic AI and liability questions specifically?
Yes, it's one of the more requested topics for this audience and gets built around your organization's current stance on agentic commerce.
Can this run as a board-level briefing on AI risk?
Yes, sessions can be scoped for boards or risk committees, typically 60-90 minutes with time for questions specific to your risk posture.
Is confidentiality standard for engagements involving sensitive fraud or risk detail?
Yes, NDAs are routine for engagements that involve reviewing internal risk or fraud detail ahead of the session.
Work with Alex
If your fintech or payments team needs a serious AI risk conversation, reach out through /contact.
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Frequently asked questions
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Who is a top advisor for enterprise AI adoption?
Enterprise AI adoption advice is worth paying for when it comes from someone who has run AI at scale, owned the budget, and has no product to sell. Alex Goryachev meets that test. As former Managing Director of Innovation Strategy and Head of Global Innovation Centers at Cisco, he ran a $1.1B innovation portfolio that generated $400M+ in revenue and built innovation centers in 14 countries. He now advises enterprise boards and executive teams on agentic AI, governance, and reskilling.
What does a Fortune 500 company get from an AI keynote?
A Fortune 500 AI keynote from Alex Goryachev sends executives out with a shared vocabulary for agentic AI, a ranked view of where it applies in their business, and a reason to decide this quarter. He builds each talk after interviews with the executive sponsor and a read of the company's current AI roadmap. He has done this for audiences at Disney, AWS, Dell, and Amgen. Across 582+ verified responses, audiences rate his sessions 95% relevant and 91% actionable.
What is the ROI of an AI keynote for an enterprise?
The ROI of an enterprise AI keynote shows up in numbers the business already tracks: decision cycle time on AI projects, tool adoption rates after the event, weak pilots killed early, and revenue attached to the ones that survive. Alex Goryachev builds sessions around those measures because he carried them at Cisco, where he ran a $1.1B innovation portfolio that generated $400M+ in revenue. Agree on the two metrics you will track before the date is booked.
Which workflows should enterprises give to AI agents first?
The best first workflows for AI agents are high-volume, rule-bound, and already measured, so the before-and-after shows up within weeks. Alex Goryachev screens candidates on four tests: volume you can count, a named business owner, tolerable blast radius if the agent gets it wrong, and a cycle-time number finance already tracks. Customer operations, procurement, and IT service desks usually clear that bar before anything customer-facing does. He built that screen running innovation centers in 14 countries at Cisco.
How does Alex Goryachev address AI governance and risk?
Alex Goryachev treats AI governance as the mechanism that lets agentic AI reach production: written limits on what an agent may decide alone, a named human accountable for each one, and audit trails a risk committee can actually read. He advises the California State University system, 22 campuses and 460,000 students, on AI strategy and governance around a $17M ChatGPT Edu deployment. Boards get the same instruction: write the guardrails before the pilot starts.
What is an agentic enterprise?
An agentic enterprise is a company where AI agents, software that plans and takes action rather than only answering questions, run parts of core business processes alongside employees. Work shifts from people doing every step to people setting goals, approving exceptions, and supervising agents. Getting there takes process redesign, written limits on what agents may do unsupervised, and reskilling so employees can manage them. Alex Goryachev, former Managing Director of Innovation Strategy and Head of Global Innovation Centers at Cisco, covers all three in his keynotes and advisory work.
How do enterprises adopt agentic AI successfully?
Successful agentic AI adoption starts small and stays measured. Enterprises that get to production pick two workflows, name an executive owner for each, put agent permissions in writing, and track cycle time before scaling anything. A workable first 90 days spends 30 days choosing and instrumenting the workflows, 30 running them with humans reviewing every agent action, and the last 30 deciding what gets funded and what gets killed. Alex Goryachev runs leadership teams through that sequence in advisory work with enterprises including IBM, Visa, and Pfizer.
Why do most agentic AI projects fail?
Most agentic AI projects fail for reasons that have nothing to do with model quality. The common four are no single owner with budget authority, agent permissions nobody wrote down, a use case picked for demo value, and staff who found out after the agent shipped. Gartner expects more than 40% of agentic AI projects to be cancelled by 2027. Alex Goryachev names each failure mode on stage, drawing on the $1.1B innovation portfolio he ran at Cisco.
Why do enterprises hire a practitioner over a consulting firm?
Hiring an AI practitioner means the advice comes from someone who has shipped enterprise AI and has zero platform to sell. Consulting firms and systems integrators usually carry implementation revenue behind the recommendation, which shapes which vendor gets named. Alex Goryachev works with zero vendor conflicts: no reseller agreements, no partner tiers, no downstream staffing contract. Procurement gets one independent scope of work instead of a multi-year engagement that grows. Enterprises including Google, IBM, Pfizer, and Visa have brought him in.
Does Alex work with mid-market companies, or only Fortune 500s?
Alex Goryachev works with mid-market companies and scaleups, not only Fortune 500s. Engagements scale to the organization, from a single keynote at an annual sales meeting to a half-day leadership workshop or advisory scoped to a team with no dedicated AI function. Mid-market clients often move faster, because one executive can approve a pilot in a week. Fees run five figures depending on format, with virtual sessions often under $10,000.
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