AI Risk and Governance Workshops
From compliance to culture, Alex helps boards and leaders navigate governance with AI
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Most organizations treat AI risk as something the legal team will handle and everyone else can ignore. The reality runs the other way: the riskiest AI decisions are being made every day by people who have never read the policy, using tools no one formally reviewed. A governance workshop exists to close that gap before it becomes an incident.
Why risk and governance workshops are different
This is a working session, not a talk to be admired. The people in the room have to leave with decisions, or at least with a clear map of who decides what. That means the session has to get concrete fast: which uses of AI need review, which are fine to let run, and who is accountable when something goes wrong.
The hard truth is that most AI governance is written to look reassuring rather than to work. A policy that bans a tool everyone is already using does not reduce risk, it just drives the usage underground where no one can see it. Effective governance meets people where the work is and makes the safe path the easy path. Getting there requires arguing through real trade-offs, not adopting a template.
There is also the speed-versus-control tension these rooms exist to resolve. Lock everything down and the organization falls behind; wave everything through and exposure piles up quietly. The job is to place controls where the actual risk is, and to be clear about where it is not.
There is also a timing problem specific to governance. Organizations tend to write their first AI policy either far too early, before anyone understands how the tools are actually used, or far too late, after an incident forces the issue. Neither produces good rules. The better path treats governance as something revised as the organization learns, with a clear owner and a schedule for revisiting it, rather than a document written once and filed away. A workshop is a good moment to decide not only what the rules are, but who keeps them current and how often they get a fresh look.
What this keynote delivers
- A practical way to sort AI uses by real risk, so oversight lands where it matters
- Clear ownership: who approves, who monitors, and who answers when something breaks
- Governance that people will actually follow instead of quietly working around
- A shared reading of your risk appetite, so decisions stop being made case by case in the dark
- A short roadmap for standing up or tightening oversight in the next quarter
Why Alex for risk and governance workshops
AI governance is one of Alex's core themes, and he does this work in a live setting: he serves on the AI Working Group advising the California State University system, one of the largest public institutions in the country, on the exact questions of oversight and accountability these workshops tackle. He approaches governance as a practitioner who has had to make it function, not as a compliance theorist.
Frequently Asked Questions
Which roles should we invite for risk and governance workshops?
The people who set policy and the people who live with it: a mix of leadership, legal or risk, and the functions actually using AI. Governance built without the users tends to fail on contact.
How is this structured?
Usually a short framing talk followed by 60-90 minutes of facilitated discussion, so the group moves from shared understanding to real decisions rather than stopping at concepts.
Will you tailor it to our industry's constraints?
Yes. Alex works with you beforehand to understand your regulatory pressures and internal politics, then shapes the session so the trade-offs discussed are the ones you actually face.
Can this be done under an NDA?
Yes. Governance conversations touch sensitive material, and Alex is comfortable working under a mutual NDA so the discussion can be candid.
Work with Alex
To move your AI governance from reassuring on paper to working in practice, open a conversation 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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