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How AI as a tool continues to empower business leaders to accomplish greater results.
Over the past several years, artificial intelligence (AI) has evolved significantly. What was once little more than a productivity buzzword has come into its own across the 2020s, becoming a genuine operational multiplier.
As such, business leaders who have embraced AI can now not only automate tasks, but also completely rethink how their companies are built, staffed, and scaled. For this growing cohort of founders and CEOs, AI isn’t just a supplementary tool to slap on top of existing infrastructure; it is the core infrastructure itself. Results from these companies show that AI use in business today can lead to leaner teams, faster decision-making, and competitive advantages that compound over time.
Doing More With Less
Geneva Schlabach, CEO of Vispa, a healthcare revenue cycle automation company, discovered that AI’s biggest internal win wasn’t the product itself; it was applying AI to company operations.
“In our industry, it’s all about having AI in your product. I’m like, ‘Okay, but how are you using it in your operations to be able to increase your output?’ And somehow, I feel like that is the big miss… I think it could be where a lot of the opportunity is and where the speed to deployment lies. It’s all in how you use your time internally.”
By running Claude instances overnight, Vispa’s CTO absorbed what would have been thousands of developer hours in just months. AI now connects legal, finance, and marketing to a single source of truth, eliminating silos and creating operational cohesion.
Geneva details, “There was a cohesion that happened. Now everyone is speaking the same language and is going after the same thing. Transitioning our marketing verbiage; that change instantly synced across development, legal, marketing, and QuickBooks. Everyone speaking the same language. No one left out of the loop.”
The company’s hiring philosophy has shifted accordingly: judgment and AI fluency matter more than traditional technical credentials. Vispa’s future is an “orchestration platform” where AI agents and humans collaborate to expedite healthcare revenue cycles.
Geneva surmises, “My question has always been, first, can we automate something before we hire someone? And inevitably, my team thinks that way now. They’ll be like, ‘Hey, we think we can do X, Y, and Z, so we may need to hire one person, but we don’t need to hire five people.’”
Data First, AI Second
Lee McCabe, Founder of Claymore Partners, a private equity operational platform, argues that most companies are “AI unready” before they even start. Without clean, comprehensive data, AI returns noise instead of insight.
Lee explains, “If you can measure everything, then you are AI-ready, and it will be super effective for you. If you haven’t got the data, then it’s garbage in, garbage out. AI is only as good as the data you’ve got.”
His framework: build the measurement stack, collect six to twelve months of baseline data, then deploy AI. When done right, the results are dramatic; a client diagnostic that once took a junior analyst a week now gets done in a day, freeing teams for the strategic and relational work AI can’t replicate.
“Spending more time with clients; that’s where the value is. Analytics is not taking up my time. Producing reports is not taking my time. That time is now free to spend with clients and colleagues.”
Hiring now prioritizes judgment over throughput. With AI handling grunt work, the key skill is the judgment to challenge AI outputs, ensuring accuracy and preventing blind acceptance of flawed results.
Lee explains, “You should be the chief architect of this. Because if you don’t understand it, you can’t just delegate it to people. AI will eventually transform your entire business. You’ve got to have a point of view and say, here’s where I think it’s going to enable my business.”
Human-in-the-Loop
Invaritech is an AI automation consultancy run by CEO Avishek Majumder. The company’s core strategy relies on its human-in-the-loop philosophy: automate mechanical tasks while designing AI to intentionally fail and flag exceptions for human review, ensuring accuracy and trust.
Avishek details, “If there is any conflict, the system automatically flags it; ‘Okay, I don’t know what to do with this now, it’s up to the human operator.’ Anything outside the regular decision matrix, the model just stops. We allow the AI to fail, because a system that stops is safer than one that hallucinates an answer.”
This design-to-fail philosophy is especially critical in high-stakes domains like EUDR compliance auditing, where accuracy is non-negotiable. This creates substantial back-and-forth between human workers and AI interfaces.
“I just talk to the AI agent that I use, and then it makes changes on the website. I look at the website, give it a thumbs up, and then it just publishes everything. The research and execution are automated; I’m there for strategy and final approval,” Avishek says.
Beyond client work, Invaritech’s own marketing runs on an AI agent that handles SEO research and publishes website changes, with Avishek maintaining strategic oversight.
“Right now with AI, a lot of things can be given out for free because it takes pennies to build. Even the recurring cost of running is extremely minuscule. People upload their invoices, and it costs less than one cent to me, so I can just give it out for free.”
Final Thoughts
The pattern across industries is clear: AI creates leverage when used intentionally, with clean data, clear goals, and humans providing strategic oversight. The companies seeing the greatest returns aren’t replacing people wholesale; they’re building smarter systems that help smaller teams achieve far more.
