A Leadership Lunch-and-Learn That Gets the Story Straight on AI
From quick insights to strategic clarity, Alex makes every lunch and learn impactful
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Within a week of a leadership team's AI lunch-and-learn, three different versions of what was said will circulate through the company — one optimistic, one alarmed, one confused. That drift usually isn't the fault of the people repeating it. It's a sign the leadership team never actually agreed on one story in the first place. Nobody decided to tell five different stories; it just happens when nobody agrees on one first.
Why a leadership lunch-and-learn is different
A leadership team isn't one audience; it's a small collection of people whose functions AI is affecting unevenly, and who each carry their own read on it back to their own reports. The lunch-and-learn is often the only shared moment where the whole leadership group hears the same framing at the same time, which makes it disproportionately important relative to its length.
The failure mode here is subtle: the session feels successful in the room — heads nodding, good questions — but two weeks later, five different leaders are telling five different stories to their own organizations about what the company's AI direction actually is. Nothing in the session forced convergence, only exposure.
Getting this right means designing explicitly for what leaders will repeat later, not just what they'll understand in the room. That's a different bar, and most one-off AI talks aren't built to clear it.
Leadership teams tend to assume alignment exists because everyone nodded in the same meeting. But nodding isn't agreement on wording, and the gap between the two only becomes visible once different leaders are already repeating different versions to their own organizations.
The fix isn't more meetings about strategy. It's a short, deliberately literal set of phrases the whole leadership group commits to using the same way, so the story that reaches employees doesn't depend on which leader happens to be talking that day.
What this keynote delivers
- One consistent, repeatable framing of agentic AI and the future of work the whole leadership team can carry downward
- A shared vocabulary that reduces the drift between what different leaders tell their own organizations
- An honest accounting of where the leadership team's current AI messaging is inconsistent or contradictory
- A model for talking about AI governance that doesn't require legal or compliance in the room to stay accurate
- A short set of talking points leaders can use verbatim when their own teams ask what leadership actually said
Why Alex for a leadership lunch-and-learn
Alex is a WSJ-bestselling author of Fearless Innovation and a LinkedIn Top Voice precisely because he builds frameworks designed to be repeated accurately, not just understood in the moment — which is the specific problem a leadership team's messaging drift creates. He has also advised the California State University system's AI Working Group, where one leadership team's consistent framing has to hold across dozens of campuses at once.
Frequently Asked Questions
How do you keep the leadership team's message consistent afterward?
The keynote ends with a short, shared set of talking points the whole leadership group agrees to use, reducing drift when they return to their own teams.
Can this be recorded for leaders who miss the session?
That depends on the venue and can be arranged in advance; ask during scheduling.
What size leadership group does this work best for?
It scales from a dozen leaders to several dozen without losing the shared-framing effect.
What's the typical format and length?
A 45–60 minute keynote fits most lunch-and-learn blocks, with optional time for leadership Q&A after. A short written recap of the shared talking points is also typically provided afterward for reference.
Work with Alex
To get your leadership team telling one AI story instead of five, start 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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