A Keynote for AI Literacy in Education Programs
From K-12 to higher ed, Alex makes AI literacy accessible for every learner
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Schools have run literacy pushes before: digital, media, financial. AI literacy is different because the subject talks back, flatters, fabricates, and improves between semesters. A program built like last decade's computer class will be teaching yesterday's tool by launch day, and the students will know it first.
Why AI literacy in education programs is different
Real AI literacy is not tool training. It is judgment: knowing when to trust an output and when to check it, understanding how these systems fail, developing instincts about what should never be pasted into a prompt, and being straightforward about disclosure. Those habits survive model updates; keyboard shortcuts do not. The design work is in scope and sequence, because a ninth grader, a first-year composition instructor, and a registrar's office need different literacy, and a single all-hands course serves none of them.
The bottleneck is nearly always educator development. Teachers and faculty are expected to model judgment with tools they have had no protected time to practice, while policy fear keeps classroom experimentation quiet and vendor-led curriculum drifts into product training with a curriculum's fonts. Equity runs through all of it: learners with home exposure to these tools pull ahead of those without, and a literacy program is the one systematic chance to close that gap rather than widen it.
Rollout order matters more than curriculum polish. A shared floor comes first, the same basic judgment training for every adult in the system, because a program undermined by an unprepared front office loses credibility with families fast. Role-specific layers come second, built with the people who hold the roles rather than delivered to them. In K-12 settings, family communication belongs in the plan from the start, since a parent's first question is usually about safety and fairness, not pedagogy. In higher education, faculty governance means literacy expectations move by persuasion and example, which is slower and sturdier. Two traps deserve flags: treating a vendor's certificate as evidence of literacy, when it mostly evidences familiarity with that vendor, and treating literacy as a one-time inoculation, when the workable model is a refresh cycle, because the tools will not sit still and neither can the judgment.
What this keynote delivers
- A working definition of AI literacy that survives the next model release
- How to sequence literacy differently for students, faculty, and staff without tripling the work
- Educator development that builds practice, not another tool demo
- Guardrails that respect student data privacy laws while keeping classroom practice alive
- Ways to assess literacy that measure judgment instead of vocabulary
Why Alex for AI literacy in education programs
Alex is a member of the AI Working Group advising the California State University system, where AI literacy across students, faculty, and staff is a live design question at enormous scale. He explains AI in plain English because that is the whole assignment.
Frequently Asked Questions
What should we have ready before the session?
Your current program outline, if one exists, and the audiences you must serve first. If nothing exists yet, that is a fine starting point too.
Can one event serve teachers and administrators at once?
Yes, with care. The session separates what each group needs to do while keeping the shared frame, which prevents the usual split where policy and practice stop speaking.
What follows the keynote?
A recap organizers can circulate, including the sequencing frame and suggested first moves for the program team.
How long should we plan for for ai literacy in education programs?
The keynote runs 45 to 60 minutes; program teams often add a facilitated hour to rough out their own scope and sequence.
Work with Alex
To plan an AI literacy session for your faculty, staff, or students, describe your program at /contact.
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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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