An Executives Meeting to Align Before Your AI Position Goes Public
From strategy sessions to board reviews, Alex makes executive meetings decisive
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ALEX, BY THE NUMBERS
Someone on this executive team is about to be quoted, in a memo, an interview, or a company update, saying what the organization thinks about AI — and right now, no two people in that room would say quite the same thing. A single unscripted answer to a reporter or an employee can undo weeks of careful internal messaging in one sentence.
Why this executives meeting is different
Once an executive speaks publicly, internally or externally, about the company's AI direction, that statement becomes the reference point everyone else measures future comments against. If the executive team hasn't actually agreed on the underlying position beforehand, the public version gets built on assumptions rather than consensus, and inconsistencies surface later at the worst possible moments.
This is different from a general strategy debate because the clock is running. There's a specific upcoming moment, a town hall, a board update, a press question, that will force someone to characterize the company's AI stance whether the team is ready or not. Waiting for perfect alignment isn't an option; the meeting has to produce a workable position on a deadline.
The risk of skipping this step isn't abstract. Executives who improvise their own version of the company's AI position in different rooms create exactly the contradiction that erodes trust the fastest, both with employees and with anyone else paying attention.
Executives rarely intend to contradict each other publicly. It happens because each of them answers from their own honest read of the situation, absent any agreement on what the shared read actually is.
The fix isn't a communications script handed down after the fact. It's giving the executive group the chance to actually work out their disagreements privately, before any of them are speaking for the company in a room without the others present.
What this keynote delivers
- A structured session that produces one workable executive position on AI before it has to be stated publicly
- Identification of where executives currently disagree, surfaced early enough to resolve rather than air later
- A framework for talking about agentic AI and governance that holds up under outside scrutiny
- Specific language the group can use consistently across town halls, board updates, and external questions
- A realistic view of which AI claims are safe to make publicly and which aren't yet
Why Alex for this executives meeting
Alex advises the California State University system's AI Working Group on governance positions that have to hold up across many stakeholders and public scrutiny, which is exactly the alignment problem this meeting is solving for on a smaller scale. He is also a WSJ-bestselling author and a Forbes-featured voice on innovation, built on stating plainly what a room needs to hear rather than what's comfortable.
Frequently Asked Questions
How does this get tailored to our upcoming public moment?
A short pre-call identifies the specific announcement, meeting, or question the executive team is preparing for, and the session is built around producing a position in time for it. Pre-call scheduling is flexible and can usually be arranged within a few business days.
Is what's discussed treated as confidential ahead of any announcement?
Yes, pre-announcement discussions are treated as confidential by default. The session can also be adjusted quickly if the public moment it's preparing for changes timing unexpectedly.
How long does this session typically run?
Most run 60–90 minutes to allow the group to actually reach agreement, not just discuss the topic.
Can this be combined with a broader executive offsite or planning meeting?
Yes, it works well as a focused segment inside a longer executive agenda. If the public moment shifts or gets delayed, scheduling can typically flex to match the new timeline.
Work with Alex
Before your AI position goes public, align your executives first 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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