An AI Keynote for Executive MBA Programs, From Someone Who Ran the Budget
From business schools to boardrooms, Alex connects AI with leadership education
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Executive MBA students have read the case studies. What most AI sessions in these programs don't offer is someone who actually had budget authority over the decisions the case studies describe after the fact. Cohorts can tell within minutes whether a speaker has actually made a call like this or is only describing one secondhand.
Why executive MBA programs are different
Executive MBA cohorts are unusual audiences: working professionals, often already senior, sitting through material during hours carved out of demanding jobs. They have low tolerance for theory that doesn't connect to a decision they'll actually face, and they can tell when a speaker has never had to defend an AI budget in front of a board.
These programs also carry a specific credibility problem with AI content. Faculty expertise tends to be strong on frameworks and light on lived operating experience, while outside speakers often bring energy but no real accountability track record. Cohorts notice the gap immediately, and it shapes how much of the content they actually trust enough to use later.
What executive MBA students actually want from a guest session is different from what a conference audience wants: less inspiration, more judgment. Fewer principles, more of the reasoning behind a real decision that went a certain way, including the parts that didn't work.
Case studies are written after the fact, with the messy parts smoothed out and the uncertainty removed. Executive MBA students already know this, which is why a session built entirely around polished case material tends to generate polite attention rather than real engagement.
What holds a room of working professionals is the reasoning behind a decision while it was still uncertain, including the version of events that didn't make it into any published case, and the tradeoffs that looked different in the moment than they do in hindsight.
What this keynote delivers
- A practitioner's account of agentic AI and innovation strategy grounded in a real $1.1B portfolio, not a hypothetical case
- Direct, classroom-ready reasoning about how AI decisions actually get made and defended inside a company
- An honest look at where innovation initiatives fail, not just the ones that make it into a case study
- Discussion-ready material cohorts can debate and apply to their own current employers
- A model of AI governance thinking useful to students who will sit on boards or in the C-suite themselves
Why Alex for executive MBA programs
Alex is Innovator-in-Residence at Tulane University's A.B. Freeman School of Business, and a WSJ-bestselling author of Fearless Innovation — credentials built specifically at the intersection of business education and operating experience that this format needs. He also ran a $1.1B innovation portfolio that generated $400M+ in revenue as former Managing Director of Innovation Strategy and Head of Global Innovation Centers at Cisco, the operating experience behind the classroom reasoning.
Frequently Asked Questions
Can the session be tailored to a specific MBA course or module?
Yes, a short discovery call with faculty can align the session to a specific course theme, such as innovation strategy or the future of work.
Is this delivered as a lecture, a workshop, or a roundtable discussion?
All three are available; many programs choose a keynote followed by a facilitated roundtable discussion with the cohort.
Can this session run virtually for an online or hybrid cohort?
Yes, virtual delivery works well for hybrid executive MBA formats.
How long does a typical classroom session run?
Most run 60–90 minutes to allow for real classroom discussion, though shorter formats are available. Programs can also request a shorter guest-lecture format if the course schedule only allows a single class period.
Work with Alex
To bring real operating experience into your executive MBA classroom, 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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