How should I manage AI expectations with my CEO?
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We are positioning AI as force multiplier and not as a magic fix.
Backing this with small & measurable pilot case studies /projects is much needed.
While aligned with AI being game changer however a calculated and balanced approach with humans leading is the need of hour.
Key is- AI with ethical guardrails, data support and showing measurable metrics and values helps to have a more practical and sustainable approach
AI systems and solutions should be approached with the controls and security development lifecycle like any other software project, tool, system, etc.
I’d start by painting a clear picture of what AI can and can’t do today. CEOs don’t need hype, they need clarity. That means explaining the difference between a proof of concept and a production‑ready system, and making sure they understand that AI isn’t magic — it’s powerful, but it has limits.
I'd also talk about time horizons. Some AI projects deliver quick wins, others take longer because they depend on data cleanup, workflow changes, or cultural adoption. Setting milestones and check‑ins helps everyone see progress without overpromising.
And finally, I'd frame AI as a long‑term capability, not a one‑off project. The goal isn’t to have one shiny AI win — it’s to build the foundation for an organization that can keep adapting as the technology evolves. When your CEO sees AI as a strategic investment instead of a fad, expectations naturally become more realistic and constructive.
I hope this is helpful and it is, in fact, my first posting here in the Gartner Peer Community! I'm new to Gartner and would welcome connecting with anyone who might run across this.
GenAI: currently it's an accelerator if used properly. If use without guidelines and guardrails, it can have disastrous outcome.
Other form of AI: tech products that don't integrate AI will slowly lose competitive advantage.
Spark meaningful AI progress by tying each pilot directly to core ambitions with tangible roadmap.
Break initiative into clear phases (PoC, pilot, scale) with hard “go/no-go” gates so wins and learnings arrive fast.