An AI Keynote on Where to Place Your AI Bets
From board-level vision to real-time alignment, Alex makes onsite sessions transformative
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Every executive with an AI budget faces the same quiet problem: too many plausible bets and no reliable way to rank them. Vendors will happily tell you their pilot deserves funding; almost none will tell you what to defer. This keynote is built for the ranking problem, not the introduction to AI that most executives have already sat through.
Why this executive onsite is different
By the time an executive is deciding where AI dollars actually go, the what-is-agentic-AI conversation is usually behind them. What's in front of them is a stack of competing proposals, each with optimistic projections and none directly comparable to the others. The hard part isn't understanding the technology — it's judging the bets.
Executives making these calls also face a specific kind of pressure: moving too slowly risks looking behind competitors, moving too fast on the wrong bet risks a visible, expensive failure. Both outcomes get noticed. That asymmetry pushes some executives toward funding everything a little, which is often worse than funding a few things fully, since diluted bets rarely produce a result strong enough to justify any of them.
Without an independent framework for comparison, the loudest internal advocate or the single most polished vendor deck in the room tends to win the budget, regardless of whether it's actually the strongest underlying bet. That's a costly way to allocate a limited AI budget, and it's the default outcome whenever ranking is left to instinct instead of a repeatable method.
There's also a review problem that compounds the ranking problem. Even a well-reasoned initial bet needs to be revisited as results come in, and without a scheduled check-in, funded initiatives tend to keep their budget by default long after the evidence has turned against them. Ranking well once isn't enough if nothing forces a second look later.
What this keynote delivers
- A framework for ranking competing AI proposals on comparable terms
- Independent judgment on vendor claims, since Alex has no vendor relationships
- Guidance on what to fund fully, fund small, or defer entirely this cycle
- A candid look at what a visible AI failure actually costs versus what moving too slowly costs
- A model for revisiting these bets on a set schedule instead of funding once and forgetting
Why Alex for this executive onsite
Alex advises the California State University system on AI and AI governance and serves as Innovator-in-Residence at Tulane University's A.B. Freeman School, work that keeps him evaluating real proposals rather than theorizing about them. He sells nothing from the stage — there's no incentive to steer the budget anywhere in particular. He is the WSJ-bestselling author of Fearless Innovation and a LinkedIn Top Voice, recognition built on independent judgment rather than proximity to any one vendor's roadmap.
Frequently Asked Questions
Will you evaluate our specific vendor proposals during the session?
The framework is built so you can apply it to your own proposals afterward; the session itself stays independent rather than acting as vendor due diligence.
What's the typical format for this kind of executive onsite?
Often a focused keynote plus a working session — 45–60 minutes of briefing followed by 60–90 minutes applying the framework to real decisions, which tends to produce a ranked list by the end of the day.
Do you recommend specific AI vendors or products?
No. Alex sells nothing from the stage and stays independent of any vendor relationships.
How much does this typically cost?
Fees depend on format, and you get availability and a fee range within one business day.
Work with Alex
To bring independent judgment to your next round of AI investment decisions, 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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