Team Lunch-Learn: Separating Real AI Change From the Blame It's Taking
From future trends to practical takeaways, Alex equips teams to grow in every session
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Some of what's frustrating a team right now is truly caused by new AI tools. A lot more of it is old frustration — thin staffing, unclear priorities, tool fatigue — that's easier to blame on AI than on the decisions that actually caused it. Once a tool gets blamed for something it didn't cause, that blame is hard to walk back later, even after the real cause is identified.
Why a team lunch-learn is different
AI has become a convenient scapegoat inside teams. When a process breaks, when workload spikes, when a reorg happens near the same time as a new AI tool, the tool gets blamed regardless of whether it's actually responsible. That confusion is bad for morale and bad for adoption, because a team convinced AI caused problems it didn't cause will resist tools that might truly help.
Sorting out what's real from what's misattributed takes more than a reassurance memo from leadership; it takes someone with no stake in the outcome walking through, specifically, what AI tools do and don't explain about a team's current frustrations.
Getting this right protects morale two ways: it stops AI from absorbing blame for problems that need a different fix, and it keeps the team from dismissing a tool that's actually useful just because it arrived at a bad moment.
Teams under pressure look for an explanation, and a newly deployed AI tool is often the most visible, recent change available to point to, whether or not it's actually connected to the problem at hand.
Untangling that misattribution matters because it protects two things at once: the team's willingness to keep using a tool that might actually help, and management's ability to actually see and fix the real underlying issue instead of quietly rolling back a good tool.
What this keynote delivers
- A clear-eyed way to tell which team frustrations are actually AI-driven and which are being misattributed to it
- Straight talk about where agentic AI is and isn't changing real workloads right now
- A model for raising the real underlying issues, like staffing and priorities, instead of letting AI absorb the blame
- Concrete, usable guidance on getting more value from the AI tools the team already has access to
- Space for the team to name frustrations candidly without it turning into a complaint session
Why Alex for a team lunch-learn
Alex is a practitioner, not a futurist, whose core themes include innovation culture and the future of work — the exact seam where team morale and AI adoption problems usually collide. He has delivered more than 310 keynotes and engagements across 6 continents and 14 countries, and 98% of those audiences say they'd recommend the session.
Frequently Asked Questions
What does a team lunch-learn typically cost?
Fees are five figures depending on format; virtual sessions for a single team are often under $10,000. That distinction is drawn using the team's own recent examples rather than generic categories that don't map to their actual work.
Is this delivered as a keynote, a workshop, or something in between?
Most team sessions run as a straightforward keynote with open discussion; a workshop format is available if your team needs a working session instead. A short written summary of what was decided can also be shared with the team's manager afterward.
Do teams get anything to keep after the session?
A short set of takeaways can be shared afterward to reinforce what the team discussed.
Can this pair with a broader team offsite or planning day?
Yes, it works well as one segment within a longer team day. A workshop version can also run as a half-day session if your team wants deeper facilitated problem-solving.
Work with Alex
If your team's frustration is getting misfiled under AI, talk to Alex's team 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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