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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Frequently asked questions
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Who is a top advisor for enterprise AI adoption?
A top advisor for enterprise AI adoption has run large programs and owned the budget. Alex Goryachev meets that test. As Managing Director of Innovation Strategy and Head of Global Innovation Centers at Cisco, he shaped a $1.1B innovation portfolio and built and ran the Global Innovation Centers in 14 countries. He has advised Dell's GenAI practice and Amgen, and he now advises leadership 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. Last quarter, 95% of 676 verified attendees rated his sessions relevant and 91% rated them 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 after the session, weak pilots killed early and revenue attached to the ones that survive. Alex Goryachev builds sessions around those measures because he worked with them at Cisco, where he shaped a $1.1B innovation portfolio. 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 Cisco's Global Innovation Centers in 14 countries.
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 read. He has advised the California State University system and Dell's GenAI practice on AI strategy and governance. Leadership teams get the same instruction from him: 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 on its own, run parts of core business processes alongside employees. Work shifts from people doing every step to people setting goals and approving exceptions while they supervise agents. Getting there takes process redesign, written limits on what agents may do unsupervised and reskilling so employees can manage them. Alex Goryachev, who built and ran Cisco's Global Innovation Centers in 14 countries, 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 takes leadership teams through that sequence, drawing on advisory work with Dell's GenAI practice and Amgen.
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 his work shaping Cisco's $1.1B innovation portfolio and advising Dell's GenAI practice.
Why do enterprises hire a practitioner over a consulting firm?
Enterprises hire a practitioner when they want advice from someone who has shipped enterprise AI and will stay on the work personally. Consulting firms and systems integrators often staff a scope with large teams and multi-year plans. Alex Goryachev works with one scope of work, delivered by him. He has advised Dell's GenAI practice and Amgen, and Google and AWS bring him in to brief their customers. His past work includes IBM and Pfizer.
Does Alex work with mid-market companies, or only Fortune 500s?
Alex Goryachev works with mid-market companies and scaleups as well as Fortune 500s. Engagements scale to the organization, from a single keynote at an annual sales meeting to a 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. You get availability and a fee range within one business day.
Why isn't our AI investment paying off?
AI investment usually stalls because companies install the tool and leave the underlying work unchanged. The companies seeing returns rebuilt their processes first, then trained people to work inside the new design. Alex Goryachev, who shaped Cisco's $1.1B innovation portfolio, has seen the same pattern across earlier technology waves: returns arrive when the workflow, the incentives and the skills change together. Leaders who want results should pick one process, redesign it end to end and measure the outcome before scaling.
How do I get employees to actually use AI?
Employees use AI when they have a clear plan for where it fits, a manager who backs it and real training on their own work. Those factors matter more than the choice of tool. Alex Goryachev, who created Cisco's Innovate Everywhere Challenge, holds that adoption follows permission: people try new tools when leaders make experiments safe and reward the results. Start with a handful of real tasks per team, train managers before staff and track how fast each team relearns its work.
How do I explain AI to my leadership team without hype?
Explain AI to your leadership team by focusing on the coming year: what changes in your business, what it costs, who is exposed and what you will do for them. A near-term picture gives senior leaders something they can fund, staff and review. Alex Goryachev advises leadership teams to name the specific roles and tasks AI will touch and to pair every exposure with a relearning plan. Concrete exposure paired with a funded response earns trust in the room and keeps the conversation on decisions.
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