AI Keynote Speaker for Standards Bodies and Regulators
From compliance to innovation, Alex equips regulators with practical AI insights
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Standards bodies and regulators exist to write the rules technology has to follow before something goes wrong, not after. AI is testing that model harder than almost anything that's come before it, because the technology is changing faster than any standard-setting process was designed to track.
Why standards and regulator audiences are different
This audience thinks in frameworks, definitions and enforceability, not features. A keynote pitched at general AI enthusiasm misses what this room actually needs: a precise sense of what agentic AI does, where existing standards already apply by analogy, and where genuinely new frameworks are required because nothing on the books anticipated this behavior.
Standards professionals and regulators also carry a specific institutional caution: writing a rule too early can lock in assumptions that age badly as the technology matures, while writing one too late leaves harm unaddressed in the meantime. That timing problem, not a lack of technical understanding, is often the real obstacle in the room, and it deserves to be treated as the serious professional judgment call it is, not dismissed as slowness.
There's also a coordination challenge unique to this world: standards and regulatory bodies operate across jurisdictions and sectors that rarely move in sync, so a session useful to this audience has to acknowledge that no single standard will govern AI globally, and give attendees a way to think about interoperability across frameworks rather than a single unified answer.
A practical addition for this audience is a way to think about pilot rulemaking: sunset clauses, mandatory review periods and staged implementation all give a standards body room to correct course as the technology matures, without either locking in premature assumptions or leaving a gap unaddressed while the perfect rule gets drafted.
Regulatory bodies working on a specific rulemaking often want the talk calibrated to that exact process, and a discovery call ahead of the event lets Alex tailor examples and language to whatever standard your body currently has in draft.
What this keynote delivers
- A precise, non-partisan briefing on what agentic AI does today, framed for people who write enforceable rules
- A way to think through the timing tradeoff between premature and overdue regulation
- A discussion of where existing standards frameworks already apply to AI and where new ones are genuinely needed
- A candid look at cross-jurisdictional coordination challenges in AI standard-setting
- Language this audience can use with industry stakeholders who conflate all regulation with obstruction
Why Alex for standards and regulators
Alex advises the California State University system on AI and AI governance as a member of its AI Working Group, direct experience with institutional AI governance at scale, and he sells nothing from the stage.
Regulatory teams who've used this framework describe the clearest benefit as a shared internal way to debate timing questions without those debates stalling the process entirely.
Frequently Asked Questions
What does an AI keynote for a standards or regulatory body cost?
Fees are five figures depending on format, and virtual sessions are often under $10,000.
Does Alex advocate for specific regulatory outcomes?
No, the session stays independent and focused on what agentic AI does and how institutions think about governing it, not on advocating specific rules.
Can this keynote pair with a working session on a specific standard in development?
Yes, a common format pairs the keynote with 60–90 minutes of facilitated discussion tied to a standard your body is developing.
Is the content adaptable across different regulatory sectors?
Yes, through a discovery call Alex tailors examples to your sector, whether that's financial, safety, data or another regulatory domain.
Work with Alex
To bring this to your next standards or regulatory gathering, connect through /contact.
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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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