AI-Powered Learning & Development: A Keynote for the Teams That Build Skills
From adaptive learning to corporate academies, Alex shows how AI transforms L&D
FREQUENTLY FEATURED IN:
ALEX, BY THE NUMBERS
What should your training function teach when skills expire faster than curricula get approved? L&D is being asked to reskill the company for AI while AI rewrites how learning itself works. Both halves of that sentence deserve a serious plan, and most teams only have one.
Why learning and development is different
L&D sits in a squeeze. Business leaders want AI capability yesterday, employees want relevance to their actual jobs, and content ages in months rather than years. The course-catalog model, build it, publish it, hope, cannot keep pace. The shift is from content producer to learning-system designer: curating what exists, engineering practice, and moving development into the flow of work where skills actually form.
AI is also L&D's own power tool. Personalized paths, always-available practice partners, and rapid drafting change what a small team can produce. But quality control matters more than speed; machine-generated content that is confidently wrong inside compliance or safety training is a genuine liability. And the old comfort metrics, completions and satisfaction scores, say almost nothing about whether anyone can now do the thing.
There is a credibility test too. An L&D function teaching AI must visibly use AI well, and its hardest audience is managers expected to coach skills they have not built themselves. Development is becoming continuous and social, which is a redesign question, not a content question. Procurement pressure adds a final twist. Every learning vendor now leads with AI features, and L&D teams are being sold personalization engines before they have decided what should be personalized. The discipline is to define capability outcomes first, then let tools compete against that definition, and to insist on seeing how content quality is controlled at the vendor's end. A function that cannot articulate its own quality bar will inherit someone else's, usually without noticing until learners do.
What this keynote delivers
- A realistic division of labor: what AI does well in learning design and delivery, and what still requires human instructors and peers
- How to keep AI-related curricula current without rebuilding courses every quarter
- Practice-first program design, so skills survive contact with real work
- What to measure when completions stop meaning much
- How L&D earns a seat in the company's AI strategy instead of taking requests from it
Why Alex for learning and development
Alex is Innovator-in-Residence at Tulane University's A.B. Freeman School of Business, working inside the question of how institutions teach for a changing economy. The future of work, and the learning it demands, is one of his core themes. He is also independent, with no learning-technology affiliations, so the vendor conversation stays entirely on your side of the table.
Frequently Asked Questions
Does this work virtually for a global L&D and HR audience?
Yes. The virtual format is interactive by design and travels well across time zones, often as the anchor session of a learning summit or enablement week.
What is the investment?
You get availability and a fee range within one business day. Many L&D teams choose a virtual session over a full in-person production.
Can we reuse the material in our LMS?
Recording and internal reuse can be arranged per engagement, and a follow-up recap is available so the ideas keep working after the event ends.
Who should we invite beyond the L&D team?
HR leadership and a few line executives. When the people funding capability building hear the same argument as the people designing it, follow-up decisions happen in days instead of quarters. Line managers are worth including too, since most skill application happens under their deadlines, and their buy-in decides whether practice time survives the quarter.
Work with Alex
To build a learning strategy that keeps pace with the tools, ask about a session.
Explore more AI keynotes
- AI-Powered Team Leadership
- Bias & Fairness in Hiring
- Board & Leadership Succession Planning Sessions
- Board Members & Investors
- Automotive & Mobility
Or browse the full directory: AI Keynotes by Audience & Topic.
310+ Keynotes, Workshops & Advisory Engagements
.svg.webp){kind=logo}
Turn your next event into AI and innovation action.
These aren't just better ways to use ChatGPT, or create short-term buzz. This is what the most influential organizations on earth use to shape the future.
Check Alex's availability
Frequently asked questions
If you don't see what you need, message Alex directly using the form above.
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.
.svg)
