AI Keynote Speaker for Research Administration and Sponsored Programs
From funding proposals to compliance, Alex equips administrators with AI tools
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It is deadline week in the research office: several proposals were drafted with AI help nobody disclosed, one funder just revised its AI policy, and the institutional guidance memo is still a blank page. Sponsored programs teams are living the AI transition, not theorizing about it.
Why research administration is different
Research administrators sit at the junction of funder rules, institutional risk, and researcher behavior, and AI is pulling all three in different directions. Funders are diverging on AI-use and disclosure policies, sometimes revising them mid-cycle, and it falls to sponsored programs to interpret ambiguity into guidance that keeps proposals moving without exposing the institution. Saying nothing is not neutral; it just moves the decision to each individual researcher. Offices that publish even provisional positions give their institutions something to align on while the ground keeps moving.
The workload paradox makes it personal. Pre-award and post-award teams are chronically stretched, and AI is well suited to much of what stretches them: drafting support, budget checks, compliance summaries, progress reporting. Yet the material involved is exactly what an office cannot treat casually, from unfunded ideas and confidential budgets to data carrying sensitive-handling obligations. The opportunity and the exposure share the same desk. The offices getting this right pick narrow, low-sensitivity workflows first and expand only as controls prove out.
Above everything hangs audit culture. Research administration lives by the rule that anything done today must be defensible years later, and AI adoption without documentation habits plants future findings. The office that writes its playbook early, covering what is permitted, what gets disclosed, and what gets logged, sets the tone for the entire institution's research enterprise. That is influence research administrators rarely get credit for, and it is worth claiming deliberately.
What this keynote delivers
- A map of AI use across the grant cycle, from proposal development through closeout
- What a workable institutional AI position covers, and how to draft one that survives contact with faculty
- Where agentic AI can relieve pre- and post-award workload without breaching confidentiality
- Disclosure and integrity questions to align with researchers before funders force the issue
- A defensibility habit: documenting AI-assisted work so future audits find answers, not surprises
Why Alex for research administration
Alex sits on the AI Working Group that advises the California State University system on questions of AI governance, and at Cisco he ran a $1.1B innovation portfolio that generated $400M+ in revenue, which is to say he has owned process, accountability, and documentation at serious scale. This audience gets a speaker who respects compliance work instead of joking about it. The frameworks are built to survive the follow-up questions compliance professionals actually ask.
Frequently Asked Questions
Who should attend: central office, department admins, or both?
Both, ideally. Central offices set policy, but department administrators live closest to researcher behavior, and sessions that include both produce the most realistic conversation about what guidance will actually hold. Faculty leaders join in some designs, which sharpens the discussion further.
Is a virtual session workable for a distributed research enterprise?
Yes. Multi-campus systems and consortia often run this virtually so every unit hears the same framing at once, and virtual delivery is typically the most economical option, often under $10,000.
How long does the session take?
Plan for 45-60 minutes of keynote plus discussion. Research administration audiences ask precise questions, so protecting a full discussion block is worth it, and half-day workshop extensions are available. Annual research administration conferences use the keynote format too.
Is content adapted to institution type?
Yes. An R1 with a large portfolio, a regional comprehensive, and a hospital-affiliated research office face different funder mixes and risk postures, and discovery calibrates the session accordingly.
Work with Alex
Get your research enterprise ahead of its next AI policy question; book a briefing.
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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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