An AI Keynote Speaker for Academia
From academic conferences to faculty gatherings, Alex connects AI to scholarly goals
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ALEX, BY THE NUMBERS
Universities are expected to teach AI, use AI, and critique AI all at once, with shared governance that moves by the semester and a technology that moves by the month. That mismatch is not a failure of academia. It is the design problem, and pretending otherwise wastes everyone's time.
Why academia is different
Shared governance is a real constraint, not a talking point. Faculty senates deliberate on cycles AI does not respect, tenure incentives reward research over course redesign, and academic freedom means no provost can simply mandate a teaching practice. The result on many campuses is a policy vacuum filled by a thousand individual syllabus statements, with students learning that the rules change every time the room does. Research brings its own questions: AI in grant writing, in peer review, in authorship, each one touching research integrity in ways the existing policies never anticipated.
The institutional stakes are rising underneath the debate. Enrollment pressure is real, public confidence in the degree is under strain, and employers increasingly assume graduates can work with AI regardless of what the curriculum committee decided. The trap is performative adoption: a task force, a report, a news item, and no change in what students actually experience. Campuses that do better tend to treat AI as a curriculum and operations question with governance attached, not a communications question with a committee attached.
Students are the variable most planning forgets. They arrive more fluent with these tools than much of the faculty, calibrate quickly to inconsistency, and talk to each other about which courses feel current. Meanwhile the operational side of the institution, admissions, advising, financial aid, facilities, is adopting AI faster and more quietly than the academic side, often without anything resembling governance, which is where the first embarrassing incident usually comes from. There is also an upside worth naming. Universities are among the few institutions positioned to treat AI as both a tool and an object of study, and campuses that do that work can anchor the public conversation in their regions rather than ceding it to marketing departments. That is a role worth competing for, and it starts with the campus getting its own house in order.
What this keynote delivers
- A shared map of AI and agentic AI that faculty, administrators, and staff can argue from productively
- Where AI belongs in teaching, research, and operations, and where it does not
- A governance approach that respects academic freedom while ending syllabus roulette
- What employers now assume about graduates, said plainly
- How to get from task force to visible change inside a semester
Why Alex for academia
Alex advises the California State University system on AI and AI governance as a member of its AI Working Group, and serves as Innovator-in-Residence at Tulane University's A.B. Freeman School. He knows the difference between a campus and a company, and does not pretend one should behave like the other.
Frequently Asked Questions
Can one session serve faculty, administrators, and trustees together?
Yes, and mixed rooms are often where it works best, because the disagreements surface with everyone present instead of in separate meetings afterward.
Do you speak at virtual campus events?
Yes. Virtual formats work well for system-wide convenings and multi-campus audiences that rarely gather in one place.
What does an academic engagement cost?
Fees are five figures depending on format; virtual sessions are often under $10,000, which many institutions find easier to place within program budgets.
How do you adjust for institution type?
A research university, a regional comprehensive, and a liberal arts college face different versions of this moment. Discovery covers your context, and the examples follow it.
Work with Alex
If your campus is ready for a working conversation about AI, begin at /contact.
Explore more AI keynotes
- Academic Deans & Department Chairs
- Academic Integrity Panels
- Academic Leadership
- Academic Programs
- Accreditation Councils / Boards
Or browse the full directory: AI Keynotes for Education.
310+ Keynotes, Workshops & Advisory Engagements
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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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