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In this blog, we draw key insights from our Bring Out the Talent podcast episode featuring AI business growth coach Sergiu Simmel to explore where organizations should actually start when it comes to AI adoption in the workplace. The conversation goes beyond the tools and platforms themselves, diving into what it means to build a real AI vision, shift the habits that keep leaders from treating AI as a genuine thinking partner, and put governance and ownership in place before scaling adoption across a business.
It’s the question at the center of the episode: where do you actually start with AI in the workplace? For Sergiu, the answer is blunt, and it’s the thread that runs through everything else he shared. Before any software gets chosen, a business needs to name what it’s actually trying to solve and form a picture of what the company looks like two or three years from now with AI woven into it. He pushed back on a mindset he sees constantly among CEOs, treating AI as purely a technology problem to hand off to a CIO or CTO, calling it one of the more damaging habits a leadership team can fall into.
To make the point, Sergiu offered an analogy: imagine asking a business owner in 1905 whether they used electricity in their operation. It was a completely legitimate question, and stayed legitimate for three or four decades, until electricity simply became how business got done. He argued AI is at that same starting point now, except the transition happening over decades with electricity is compressing into a handful of years. Most small and midsize businesses, he said, are still in an experimentation phase, issuing tool licenses and letting people “play with it”, while a smaller group has moved further, treating AI adoption as a shift in their actual business operating system rather than a side project.
When a business is ready to move past experimentation, Sergiu described the first real initiative as having two parallel pieces. One is strategic: building the vision, the guardrails, the policies, and a clear list of the pain points AI might actually solve. The other is tactical: identifying one or two use cases, ideally in repetitive, document- or knowledge-heavy work, and getting a genuine early win. People across the business, he said, need concrete evidence that AI makes a real difference before they’ll trust it with anything bigger.
A theme Sergiu returned to more than once: this work can’t start with the staff and skip the leadership team. He believes CEOs and leaders need to reach their own “aha moment” with AI firsthand before they can credibly set a vision for how it should shape the business. Short of that, he said, strategy ends up built on hearsay: a few podcasts and articles standing in for real experience.
One of the skills Sergiu emphasized isn’t technical at all; it’s a mindset shift. Most people approach a problem by asking, “How do I solve this?” He argued the more useful question is, “How can I use AI to help me solve this?” It sounds like a small change in phrasing, but he was candid that it’s genuinely difficult, because it means overriding habits people have carried since childhood.
Guardrails, data policies, and usage rules need to exist before something goes wrong, not as a response to it, Sergiu said, pointing to real cases where employees unknowingly exposed sensitive data through personal AI accounts. He also recommended that businesses name a clear owner of the AI adoption process, often through a dedicated AI or transformation team that includes the CEO. In his experience working with nearly 80 leadership teams, initiatives without a named owner tend to quietly stall.
Asked what separates companies that see real business impact from those still just experimenting, Sergiu pointed to measurement: tying every AI effort to a defined outcome rather than treating it as a novelty. He also encouraged leaders to connect AI adoption back to how work actually flows through the business, and to treat the first use case as a starting point rather than a finish line, expecting to keep iterating as they learn what works.
His closing advice for leaders still on the fence was direct: get yourself genuinely upskilled on AI before forming an opinion about it. A few podcast episodes or articles, he noted, don’t add up to expertise; real understanding comes from using the tools yourself.
Sergiu’s perspective offers a clear throughline for leaders who feel behind: AI adoption in the workplace isn’t about picking the flashiest tool or waiting for a perfect strategy to reveal itself. It’s about naming a real pain point, building enough of a vision to know where you’re headed, and creating the conditions, through governance, upskilling, and a few well-chosen use cases, for AI to actually change how work gets done. The businesses that pull ahead won’t be the ones with the most tools. They’ll be the ones willing to start.
Start with the pain point, not the tool. Sergiu’s advice is to name what the business is actually trying to solve and sketch out what the company should look like with AI in place two or three years out, before any software gets chosen.
No. Sergiu is direct on this point: adoption starts with culture, not headcount. Building curiosity and basic AI literacy across the organization matters more at the outset than hiring specialized technical talent.
It runs on two tracks at once: a strategic track that sets the vision, guardrails, and policies, and a tactical track that delivers one or two early wins, ideally in repetitive, knowledge-heavy work, so employees see real proof that AI makes a difference.
Yes. Sergiu believes leaders need to reach their own hands-on “aha moment” with AI before they can credibly set a vision for the rest of the business, rather than relying on secondhand opinions from podcasts or articles.
The most common reason is a lack of clear ownership. Sergiu recommends naming a specific owner of the AI adoption process, often through a dedicated AI or transformation team that includes the CEO, since initiatives nobody explicitly owns tend to quietly fizzle.
Tie it to a measurable outcome from the start. Sergiu cautions against treating AI as a novelty: without a defined success metric going in, a business has no real way to know whether its investment in tools, upskilling, and outside guidance paid off.
Hear the full conversation, including Sergiu’s take on where AI governance should sit in a growing business, on this episode of Bring Out the Talent.
