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 depend on format and travel, and you get availability and a fee range within one business day.
Work with Alex
To make your AI budget conversation honest instead of aspirational, get in touch 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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