Change Management Playbooks for AI: A Keynote on Rewriting the Rollout
From disruption to adoption, Alex helps organizations turn change into lasting results
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Somewhere on a shared drive sits a change playbook written for a world where systems changed once every few years. It still gets copied into every AI rollout, and it still underperforms, because the assumptions underneath it no longer hold.
Why change management playbooks are different
Playbooks encode assumptions, and the classic ones assume a fixed end state, a single training push, communications organized by phase, and resistance as a stage that passes. AI breaks each assumption in turn: capabilities shift mid-rollout, training decays within a quarter, and communication needs a standing cadence rather than a launch sequence. The playbook itself needs new primitives, including iteration loops, feedback channels that actually route somewhere, and living guidance instead of a binder.
Not everything goes. Sponsorship discipline, stakeholder mapping, and the rigor of naming exactly who is affected and how all remain load-bearing. What has to be replaced is the measurement layer: adoption tracked by logins is vanity, while changed workflow outcomes are the truth. Champions also change function, from a broadcast channel for the program's messages into a sensing network that tells the program what is actually happening.
The failure point is usually ownership. Change teams, HR, IT, and the transformation office each hold a piece, and playbooks die in the seams between them. A modern playbook assigns decision rights explicitly: who can pause a rollout, who can adjust guidance, and who retires a practice when the tools move on. Version control sounds administrative and is actually cultural. A playbook that changes needs a visible change log, someone accountable for currency, and a habit of retiring guidance publicly, because stale advice erodes the credibility of everything else in the document. Treating the playbook like a product, with releases, feedback, and an owner, is the clearest signal that the change function understands the environment it now operates in.
What this keynote delivers
- Which classic playbook elements to keep, and which assumptions to retire immediately
- A cadence-based rollout pattern that expects tools to change mid-program
- Adoption measures that reflect changed work rather than opened applications
- How to run champions as a two-way sensing network instead of a megaphone
- Decision rights for a playbook that must be edited while in use
Why Alex for change management playbooks
Alex is the author of the Wall Street Journal bestseller Fearless Innovation and speaks from years of operating experience rather than framework tourism. His material comes from rollouts he owned, which is why the playbook advice survives contact with a real organization. His sessions favor the operator's question, what would you actually do on Monday, over methodology debates, which is why change teams can apply the material without translation.
Frequently Asked Questions
Will we get material we can fold into our own playbook?
Yes. A recap of the frameworks and prompts is available for internal use, so your change team can translate the session into its own documentation.
Can Alex work from our existing methodology?
He can. Share your current playbook or method in discovery, under NDA if preferred, and the session will build on it rather than talking past it.
Where does this sit in a transformation program?
Best at the kickoff or at a reset moment, before the next wave of rollout planning, so the revised assumptions shape the plan instead of critiquing it afterward.
Is our internal material protected if we share it?
Yes. NDAs are routine, and any playbooks or methods shared in discovery stay within the engagement. Teams often share their most embarrassing legacy documents precisely because the session improves fastest when it can react to the real thing rather than a sanitized summary.
Work with Alex
If your playbook needs a rewrite before the next rollout, talk with the team.
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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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