A Leaders Lunch-and-Learn on AI That Cross-Functional Managers Can Use
From quick insights to long-term strategy, Alex makes lunch-and-learns meaningful
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Somewhere between the executive team that sets the AI strategy and the employees who have to live with it sit the leaders who have to explain the gap between the two. A leaders lunch-and-learn is where that translation problem either gets solved for an hour, or gets postponed for another quarter. None of them asked to be translators, and most were never actually trained for it.
Why a leaders lunch-and-learn is different
People managers across finance, operations, marketing, and engineering rarely get AI content built for them specifically. They get the executive version, filtered down, or the individual-contributor version, scaled up. Neither fits the actual job of a leader, which is answering their own team's AI questions credibly without being able to say "because leadership decided so."
The cross-functional mix in the room adds a real constraint. A finance leader and an engineering leader are watching AI reshape their functions in completely different ways, on completely different timelines, and a session that speaks to only one of those realities loses half the room in the first ten minutes.
What actually works is treating these leaders as translators, not as an audience to inform. They need language and reasoning they can carry back to their own teams that afternoon, in their own words, not a deck they'll forward and hope lands.
A finance leader translating an AI directive for an analyst team is solving a different problem than an engineering leader translating the same directive for a group of developers, even though both are nominally implementing the same corporate initiative.
Left without shared language, each leader ends up inventing their own version of the translation, which is how a single executive announcement turns into a dozen inconsistent explanations by the time it reaches the people actually doing the work.
What this keynote delivers
- A cross-functional framing of agentic AI that holds up whether a leader runs a finance team or a product team
- Language leaders can reuse immediately when their own teams ask what AI means for their jobs
- A practical way to separate genuine AI-driven change in a function from change being blamed on AI
- Guidance on what leaders should decide themselves versus what needs to go back up to executive sponsors
- An honest look at where innovation culture breaks down between strategy and execution, and how to close that gap
Why Alex for a leaders lunch-and-learn
Alex built and ran innovation programs across a $1.1B portfolio that generated $400M+ in revenue at Cisco, which sat exactly at this translation layer between corporate strategy and functional execution. He's spent more time in that seam than on either side of it, which is what this format actually requires. His core themes, agentic AI, the future of work, and innovation culture, all live at this same execution layer rather than in the strategy deck itself.
Frequently Asked Questions
How is content tailored across such a mixed group of leaders?
The session is framed around AI's effect on decisions and workflows broadly, then anchored with examples pulled from your specific functions during a pre-call.
What should leaders prepare to get the most from this?
A short list of the AI questions their own teams have already been asking them is the most useful input.
Can this format run alongside a broader leadership offsite agenda?
Yes, it pairs well as a focused segment inside a longer leadership day.
What's the typical length for this session?
Most run 45–60 minutes to fit a standard lunch block, with time for questions built in. Where an offsite isn't planned, this format also stands well as its own scheduled session.
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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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