A Team Lunch-and-Learn on AI That Doesn't Add to the Anxiety
From bite-sized insights to strategic takeaways, Alex makes learning over lunch engaging
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Half the team is scrolling AI headlines on their phones before the sandwiches even arrive, and none of those headlines were written with their specific job in mind. A team lunch-and-learn either meets that anxiety directly or lets it keep circulating unanswered. That headline never mentions the specific software this specific team uses every day, which is exactly the gap this session has to close.
Why team lunch-and-learns are different
Unlike an executive session, a team lunch-and-learn happens at the level where AI questions are least abstract. People aren't asking about strategy; they're asking whether the tool their company just licensed will quietly take over parts of their actual job description. Generic AI content skates past that question because it's uncomfortable, which is exactly why skipping it fails.
Teams also compare notes constantly with each other, on breaks, in group chats. A lunch-and-learn that oversells AI enthusiasm gets fact-checked by the team's own experience with the tools they're already using, often within the same week, and any dishonesty in the room shows up fast.
The team setting also means the audience already trusts each other more than it trusts corporate messaging. That trust can work in favor of the session, if the content respects what the team already knows, or against it, if the content sounds like it was written for a press release rather than for them.
General AI coverage is written for a broad audience and inevitably misses the texture of any one team's actual job. A support team, a design team, and a logistics team are all affected by AI differently, and a session that treats them identically ends up accurate for none of them.
Filling that gap takes specific knowledge of what this team actually does day to day, not just familiarity with AI in general. The value of the session lives entirely in that specificity, or it doesn't land at all.
What this keynote delivers
- A direct, non-evasive answer to what agentic AI actually changes about day-to-day team workflows, without vague reassurance
- An honest breakdown of which parts of a job are realistically automatable soon versus overstated in coverage employees have already seen
- Practical ways the team can start using AI tools well this month, not hypothetically someday
- A model for raising AI concerns to management that doesn't get waved off as resistance to change
- Real time for the team's own questions, including the ones people don't ask in front of a manager
Why Alex for a team lunch-and-learn
Alex has delivered 310+ keynotes and engagements across 6 continents and 14 countries, most of them to working teams rather than curated executive rooms, and 98% of those audiences say they'd recommend the session to a colleague. He is also a practitioner, not a futurist, having run a $1.1B innovation portfolio that generated $400M+ in revenue at Cisco before ever stepping onto a stage professionally.
Frequently Asked Questions
Will this session candidly address AI's effect on our jobs?
Yes — it addresses the job-security question directly, without invented promises in either direction.
Does the team need to prepare anything beforehand?
No formal preparation is needed; a short list of real concerns the team has already raised is the only useful input.
Is this suitable for a mixed team of different roles and tenures?
Yes, the content is built to hold a mixed-experience room without losing either newer or longer-tenured staff.
How long does a team lunch-and-learn run?
Most run 45–60 minutes, with time held for the team's own questions. If the team is distributed, the same format adapts well to a virtual session without losing the informal tone.
Work with Alex
To give your team real answers instead of more headlines, reach out 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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