AI Clarity for the Executive Lunch-Learn Series
From strategy updates to AI trends, Alex makes short sessions impactful
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Ask five executives on the same leadership team what the company's actual position on AI is, and you will often get five different answers — one cautious, one evangelical, one convinced it's overhyped. A lunch-learn slot rarely fixes that; it usually just adds a sixth opinion to the mix. The disagreement rarely gets voiced directly; it shows up instead as five executives each quietly running their own AI initiative.
Why the executive lunch-learn is different
Most AI content aimed at executives is built to inform, not to align. That's the gap this format keeps exposing: individually, executives can be reasonably fluent on AI, but collectively they still walk out of meetings without a shared read on how aggressive, cautious, or sequenced the company should be. A lunch-learn session that only delivers information leaves that gap untouched.
The politics compound the problem. Whoever's function is furthest along with AI tends to dominate the conversation, whoever's function feels most exposed tends to go quiet, and the result is a leadership team that sounds aligned in the room and splinters the moment they're back running their own departments.
Fixing that isn't about adding more AI facts. It's about giving the group a common frame — one set of definitions, one honest picture of where the technology is and isn't ready — that survives past the lunch hour and shows up in how the team actually talks about AI afterward.
Left alone, that quiet divergence compounds. A function that moves fast on AI without a shared frame ends up ahead of budget approval it never actually received, while a cautious function falls further behind without anyone deciding that was the intent. Neither outcome was chosen; both just happened by default.
A lunch-learn built to align rather than inform treats that drift as the actual problem to solve, not a side effect to tolerate. The measure of success isn't whether executives found the hour interesting; it's whether the next planning cycle references one shared position instead of five separate ones.
What this keynote delivers
- One shared, plain-English frame for agentic AI and the future of work that every executive in the room leaves using the same way
- A candid map of where the company's functions are actually positioned relative to each other on AI adoption
- A structure for talking about AI risk and opportunity that doesn't depend on whichever function is loudest that quarter
- Direct, undiplomatic answers to the disagreements this specific leadership team hasn't resolved on its own
- A short set of questions the group can carry into its next planning cycle
Why Alex for the executive lunch-learn
Alex advises the California State University system's AI Working Group on exactly this kind of cross-functional alignment problem, at a scale where dozens of leaders have to land on one workable position. That's a different skill than delivering information, and it's the one this format actually needs. He has also led innovation tracks for three Olympic Games, another setting where dozens of stakeholders had to align fast under a hard deadline.
Frequently Asked Questions
What should our executive team prepare before the session?
A short list of where your leaders currently disagree about AI priorities is more useful preparation than any pre-reading.
Does this pair with a follow-up workshop?
Often, yes. Some teams follow the lunch-learn with 60–90 minutes of facilitated discussion to work through specific decisions.
What does this cost, roughly?
Fees are five figures depending on format; a lunch-learn session delivered virtually is often under $10,000.
Does Alex sell an AI platform or consulting engagement afterward?
No. He sells nothing from the stage and has no vendor relationships to promote. Sessions typically run in-person for a single site or virtually when the leadership team is spread across multiple locations.
Work with Alex
If your leadership team needs one AI position instead of five, start the conversation at /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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