By Marcus Delaney
Something has shifted in how the most effective business operators work. It is not that they are chasing every new tool or rebuilding their companies around technology for its own sake. They have figured out where the real points of advantage sit, and they have moved while others are still debating whether to start.
The data tells part of the story. About 78% of organizations now use AI in at least one business function, up from 55% in 2023, according to McKinsey research, and companies deploying AI report meaningful productivity gains in the functions where it is actively applied. Aggregate numbers miss the more interesting detail. A growing cohort of founders and operators is not simply adopting AI tools but rethinking entire business models around the efficiencies those tools make possible. Some are building the infrastructure other businesses run on. Others are using technology to finally close a gap that has existed in their industry for decades.
The pattern that connects them is consistent. Each one identified a structural problem that incumbents had either missed or had no incentive to fix, and built toward solving it.
Closing Gaps That Incumbents Left Open
Conor Firth’s path to founding Art First Business Services (AFBS) ran through galleries, auction houses, and an early e-commerce platform for contemporary art before moving into financial leadership for mid-sized creative agencies, eventually serving as CFO across entities in three countries. That dual background gave him a clear view of a persistent problem. When creative professionals leave large agencies to go independent, they lose their entire financial support infrastructure overnight. In-house finance teams, legal counsel, and procurement support all disappear at once.
Traditional advisors either did not understand the creative world or priced themselves out of reach for independent operators.
“Business and financial advice was either too expensive for creative businesses, or the advisors offering it had no real understanding of how the creative world actually works,” Firth says. “There was nobody out there built specifically for them.” AFBS was built to fill that gap, and technology is central to how it delivers substantive financial guidance at a price point that works for independent creatives.
The challenge of translating financial complexity into language that resonates with people who think visually and narratively is one Firth describes as ongoing. It is also the kind of communication problem that AI tools are well-suited to support, helping strip away jargon and get to what a client actually needs to understand, without losing the substance behind the advice.
Kevin Brunner approached a related problem from a different angle. His career ran from running a lawn care business with commercial crews before finishing high school, through seven years in the U.S. Marine Corps and work in defense contracting across 14 countries, to entering financial services in 2004. He went independent within eight months. Over the following twenty-two years, he built The Q Companies into a vertically integrated multi-family office, bringing legal, trust, tax, and investment services in-house one function at a time, with the explicit goal of reducing the conflicts of interest that Brunner says cost clients dearly. The analytical infrastructure required to identify tax planning opportunities across complex portfolios, the kind of work behind his installment sale trust and Model Q strategies, is exactly the category where AI-assisted research and data systems are quietly changing what independent advisory firms can deliver at scale.
Building Products Other Businesses Run On
Yasser Elsaid’s path into AI was more direct, though it started with a rejection. After internships at Tesla and Meta during his computer science degree in Canada, he did not receive a return offer from Meta, an outcome he has described publicly as a pivotal turn in his career. In early 2023, while finishing his final year of university, he built the first version of Chatbase. The idea was straightforward. Allow businesses to embed a custom knowledge base in a large language model and query it in a conversational way. No one had yet built it as a clean, standalone product.
He posted a demo on Twitter to sixteen followers. It went viral. “I launched at 1 p.m. I got my first customer 30 minutes after that,” Elsaid said. “At this moment I knew that I need to stop everything else I’m doing in life because it was very obvious to me that this is a special moment and this is a special opportunity.”
Chatbase has since scaled substantially without venture capital, a milestone Elsaid has described as a turning point that now allows the company to invest and operate with the aggression of a funded startup, using its own revenue as fuel. The bootstrapped-founder trap, as he frames it, is being too cost-efficient, too risk-averse, too focused on staying ROI-positive at every step. Reaching that point of scale is what finally allowed him to stop thinking that way.
Kris Krokos has spent six years watching the same tension play out in e-commerce. Companies want to put AI to work but cannot, because the underlying data is fragmented and unstructured. As co-founder of Deltologic, an enterprise AI implementation and software development agency operating across Europe, the Americas, and Asia, Krokos has worked with marketplace-driven businesses long enough to know that the problem is rarely ambition. It is infrastructure. That observation led directly to DataDoe, launched in 2025. The platform connects orders, advertising, inventory, fees, settlements, and profit data into a single structured foundation, starting with Amazon and expanding to additional marketplaces, and makes that data usable by AI tools, analytics platforms, and automation systems. It is not an AI product. It is the layer that makes AI products work, a distinction that matters more the further a business scales.
What Connects Them
What connects Firth, Brunner, Elsaid, and Krokos is not a shared industry or a shared technology stack. It is the way each of them identified a gap that existed for structural reasons. Financial support is absent for independent creatives, conflicted advice is common in wealth management, AI deployment is bottlenecked by disorganized data, and conversational AI is inaccessible to businesses without engineering resources. Each built something designed to close that gap. AI is not the story in any of these cases. It is the accelerant. The story is the problem each of them decided was worth solving, and the discipline with which they have gone about solving it.
Business leaders who moved early on AI are now widening the gap between themselves and those still running pilots. The operators profiled here cleared that bar some time ago. The question worth asking is what they are building next.
