AI Keynote for a Cross-Functional Executives Brown-Bag
From informal conversations to strategic takeaways, Alex makes brown-bags meaningful
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Which department actually has the most credible AI use case in this building, and does anyone outside it know? Executives across departments rarely compare notes informally on AI, they see each other initiatives in polished steering-committee updates, not in the unfiltered version a brown-bag allows. Nobody schedules a meeting to find that overlap; a brown-bag is one of the few settings where it surfaces on its own. Most organizations never build a deliberate setting for that comparison to happen at all. Even a single session can shift how departments talk to each other afterward.
Why executives brown-bags are different
Departmental silos mean AI knowledge gets duplicated or contradicted across the building without anyone noticing. One function may have already solved a problem another is still debating, and a brown-bag is one of the few settings informal enough to surface that overlap without it turning into a turf discussion.
Peer benchmarking works differently in an informal room too, executives are more willing to say plainly what worked and what did not in their own AI initiatives when the setting does not feel like a formal review. That candor is the actual value of this format.
There's a cost to leaving that overlap unaddressed. Duplicated AI spend across departments is invisible in most budget reviews, since each function's request looks reasonable in isolation. It only becomes visible when someone in the room says, out loud, that another team already built something similar.
There's a relationship-building side effect worth noting. Cross-departmental executives who compare notes candidly in this setting tend to collaborate more easily on the next initiative that touches both their functions, since the informal exchange builds a baseline of trust a formal meeting rarely creates.
There's a practical starting point worth suggesting. Organizations new to this format often begin with a single cross-departmental brown-bag before deciding whether to make it recurring, which keeps the initial commitment small while still surfacing whatever overlap exists across functions.
What this keynote delivers
- A shared, informal forum for cross-departmental executives to compare real AI experience, not polished updates
- A neutral, outside framework for evaluating AI initiatives that does not favor any one department's narrative
- Honest surfacing of where departments are duplicating effort or contradicting each other on AI
- A grounded view of agentic AI that gives every function the same starting vocabulary
- Space for informal peer learning that a formal steering committee meeting rarely allows
Why Alex for executives brown-bags
Alex's core themes include innovation culture, built from managing a $1.1B portfolio that generated $400M+ in revenue that spanned multiple business functions at Cisco; he is used to surfacing where departments overlap or duplicate effort on innovation initiatives, AI included. His experience running a cross-functional innovation portfolio means he's used to spotting exactly this kind of quiet duplication before it becomes an expensive pattern.
Frequently Asked Questions
Can this session help surface duplicated AI efforts across departments?
That is a common outcome, the informal format tends to surface overlap that formal reporting structures miss.
What is the ideal group size for this format?
Smaller, cross-departmental groups work best, generally enough to represent multiple functions without losing the informal, conversational quality.
Do you need advance information about our departmental AI initiatives?
A short discovery call helps but is not required; the framework works generally and can incorporate specifics you share ahead of time.
What is the typical fee range?
Fees are five figures depending on format, with virtual delivery often under $10,000. If your departments are spread across locations, the session can also be run as a hybrid format that includes remote participants.
Work with Alex
To get an honest cross-departmental read on where AI stands, reach out to Alex's team.
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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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