Using AI Where It Actually Creates Business Value

Artificial intelligence is rapidly evolving from an experimental technology to a standard business tool. For owners, the main challenge is no longer identifying where to use AI, but determining whether a specific application will deliver real business value or simply add complexity.

The most effective approach is to begin with the business problem, define what improved performance means, and retain human judgment where outcomes are significant. Four owners and founders explain how they distinguish valuable AI from unnecessary implementations.

Insights From Owners and Founders

Damond Cleveland, owner of ProTeams, identifies AI opportunities by targeting areas where operational friction consumes valuable employee time.

“I don’t start with the technology and ask where we can plug it in. I start with the workflow and ask where the team is spending time that doesn’t require much judgment. In an operational business, that might be repetitive communication, organizing information, or turning field activity into something a manager can actually use. Those are strong candidates because the value is measurable: less administrative work, faster response times, or better visibility. My rule is that AI should give people more capacity to solve problems, not create a new system they have to babysit.”

ProTeams focuses on real-time visibility, field communication, documentation, and operational workflows for commercial cleaning companies, making workflow efficiency especially relevant for Cleveland.

Sam Lusey, Chief Revenue Officer and partner at MindCloud, prioritizes integration when evaluating AI.

“The best AI use cases are usually connected to a process that is already well understood. If you haven’t defined the workflow, inputs, and desired outcome, AI can make a messy process faster without improving it. I look for repetitive work where information is moving between systems or people, and a meaningful amount of effort is being spent just keeping everything synchronized. The evaluation rule I’d use is simple: if we can’t explain what business outcome improves after automation, we’re probably doing AI for the sake of doing AI.”

Lusey describes MindCloud’s mission as eliminating complexity, reducing manual work, improving data accuracy, and automating repetitive tasks. This approach guides their evaluation of AI applications.

Austin Hartley, Managing Partner of Parkland Capital Partners, evaluates AI based on its impact on decision quality.

“I think owners should be careful about confusing efficiency with value. AI can help summarize information, analyze large amounts of data, and surface patterns much faster, but speed only matters if it improves the decision that follows. In our work, I would rather use AI to make analysis more comprehensive and give an advisor more time to think than use it simply to produce more output. The safeguard is keeping a human accountable for the conclusion, especially when the decision has financial or strategic consequences.”

Hartley’s M&A and capital advisory work focuses on financial analysis, valuation, transactions, and strategic decision-making for founder-owned businesses, making the distinction between faster analysis and better decisions especially important.

Peterson Zhu, CEO of DigitBridge, considers high-quality underlying data essential for effective AI.

“AI is only as useful as the business context it can access. If your product data, inventory, orders, customer information, and operational history are fragmented across systems, the AI may produce an impressive answer without having the complete picture. That’s why I look at AI investments in two stages: first, do we have reliable, connected data; second, can AI turn that information into a measurable improvement in a workflow or decision? The practical safeguard is to test the output against the source data before allowing it to influence an important business action.”

DigitBridge’s platform is designed for unified commerce data and AI-ready infrastructure, including product, inventory, orders, fulfillment, suppliers, payments, and sales-channel information.

Conclusion

Across diverse businesses, the common thread is that effective AI starts with an operational or strategic problem, not with the technology’s novelty. Owners can separate substance from hype by asking: What problem are we solving? How will we measure improvement? What level of human oversight is needed?

The most effective applications eliminate repetitive work, improve access to information, strengthen analysis, or help employees make better decisions. The weakest simply add another tool because AI is available.

Ultimately, the goal is not to make a business more “AI-powered,” but to make it faster, clearer, more consistent, or more capable in measurable ways.