An AI Keynote for Budgeting AI Into Next Year's Plan
From long-term planning to immediate execution, Alex makes offsites productive
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Every strategy and planning offsite eventually reaches the same blunt question: how much money actually goes toward AI next year, and which line items lose out to make room for it. This keynote sits squarely on that budget conversation, not the aspirational version of AI strategy that's easier to talk about but harder to fund.
Why this budgeting conversation is different
It's one thing to agree, in principle, that AI matters; it's another thing entirely to agree which existing budget line actually shrinks to fund it. Planning offsites often skip straight past this tension, producing a plan that says AI is a priority without specifying what got deprioritized to pay for it — which means, in practice, nothing actually changed.
Resourcing AI also carries meaningfully more uncertainty than most budget lines, since returns are less predictable and slower to show up than a known product line or an established market. That uncertainty makes it tempting to either overfund AI on optimism or underfund it out of caution, and neither response reflects a genuine read on the opportunity.
The organizations that handle this well treat AI budgeting like they'd treat any uncertain but potentially important bet — sized deliberately, reviewed on a schedule, and funded from a specific, named tradeoff rather than found money that appears from nowhere.
There's a political dimension too. Naming which budget line loses out to fund AI is an uncomfortable conversation, and it's tempting to avoid it by looking for money that doesn't seem to belong to anyone. That money rarely exists in practice, and pretending it does just delays the real tradeoff conversation to a worse moment, usually mid-year when the gap becomes obvious.
What this keynote delivers
- A grounded way to size AI investment relative to other planning priorities, not evaluated in isolation from everything else competing for the same budget
- Direct, specific language for naming what gets deprioritized to fund AI, rather than avoiding the tradeoff entirely
- A repeatable framework for reviewing AI investment on a schedule, instead of setting a budget once and forgetting about it entirely
- An honest, unhedged read on where AI spend is likely to pay off versus where it's still speculative
- Practical guidance on structuring AI governance so that budget decisions always have one clear, named owner
Why Alex for this strategy and planning offsite
Alex is a practitioner, not a futurist, having personally managed a $1.1B innovation portfolio that generated $400M+ in revenue at Cisco where funding tradeoffs were made constantly, week to week, not hypothetically. He sells nothing from the stage, so there's no incentive to inflate the case for AI spend beyond what the evidence supports. He's also delivered 310+ keynotes across 6 continents and 14 countries, which means he's seen how this budgeting conversation plays out across very different planning cultures and sizes of organization.
Frequently Asked Questions
Will this session help us decide an actual dollar figure for AI next year?
It gives you a framework for sizing the investment relative to your other priorities; the specific number still depends on your organization's own plan, appetite, and existing constraints.
Does the keynote recommend specific AI tools to fund?
No — Alex stays independent of any vendor relationships and focuses on judgment, not product recommendations.
What's the typical format for this session?
A 45–60 minute keynote, often followed by facilitated discussion to apply the framework to your actual budget conversation.
What does this cost for a strategy and planning offsite?
Fees are five figures depending on format and travel required; virtual sessions are often under $10,000.
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To make your AI budget conversation honest instead of aspirational, get in touch 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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