.png){kind=logo}
The growth strategy and operations leader believes some of the most meaningful gains from AI will come from traditional industries that still rely on manual workflows, fragmented systems, and outdated processes.
Some of the businesses that shape everyday life still run on manual handoffs, disconnected tools, and processes built long before modern software became standard. Darius Booth sees those businesses as one of the clearest places where AI-enabled systems can create practical change, especially in sectors that have not received the same attention as flashier parts of the technology market.
“The biggest opportunities are not always in the places getting the most attention,” Booth says. “A lot of the businesses people depend on still rely on work that is harder, slower, and more fragmented than it needs to be.”
That is the part of the AI conversation that interests him most. Booth is not focused on the loudest predictions or the tools that attract the most attention online. He is focused on the businesses where people are still coordinating across disconnected systems, repeating manual tasks, and making decisions without a clear view of what is happening across the organization.
Those companies may not always look like obvious technology stories from the outside. Yet they often serve customers directly, employ large teams, and keep essential parts of the economy moving. Booth believes that is exactly why they matter.
“Some industries are treated as old-fashioned because they are not built around software,” Booth says. “That does not make them less important. It means the opportunity to improve how they operate may be much larger.”
As a Director of Growth Strategy and Operations, Booth works at the intersection of finance, operations, and technology. His background gives him a practical lens on modernization. He began in finance and investment banking, where he learned how large organizations make decisions and scale. He later moved into startup and operational roles, where he worked more directly on growth initiatives, systems implementation, team development, and operational scaling.
That path taught him that traditional sectors do not change because a new tool exists. They change when a business can take that tool and make it useful inside the pressures of daily work.
“Modernization fails when people treat technology like a layer they can place on top of a broken process,” Booth says. “If the underlying work is unclear, the tool just gives the confusion a new interface.”
That view is especially relevant in industries that have been slower to adopt modern software. Many of these businesses are not lacking ambition. They may want to improve customer service, speed up response times, reduce errors, or make better decisions. The challenge is that their operations can be difficult to untangle. Work may move through old systems, informal handoffs, spreadsheets, calls, messages, and individual memory.
Booth sees that complexity as the point, not the obstacle. AI and automation can help, but only when they are applied with respect for the people who understand the work already.
“The people closest to the work usually know where the friction is,” Booth says. “A modernization effort should learn from them, not treat them like a problem to work around.”
That is one reason he believes the best AI opportunities in traditional industries may look less dramatic than many people expect. The value may come from improving visibility, reducing repeated manual effort, helping teams respond faster, or giving decision-makers better information at the right moment. Those changes are not always flashy, but they can affect how a company performs every day.
Booth has seen how operational improvements can compound. In one prior role, he helped scale a flagship product from five-figure to seven-figure revenue. That kind of growth required more than demand. It required systems, communication, training, and the ability to support more activity without letting the work become chaotic.
“Growth is not only a sales story,” Booth says. “It is also a systems story. If the business cannot support the demand, the opportunity can start to create its own problems.”
His current work continues to sit near that same set of questions. Booth works in growth and operations at a technology-enabled company focused on improving how businesses run. The work reflects his belief that the next phase of AI adoption will depend on operators who can understand both the business problem and the practical limits of implementation.
That operator perspective matters because many companies are now moving beyond AI experimentation. The early stage was filled with tests, demos, and broad claims about what the technology might do. Booth believes the harder phase is now beginning. Businesses have to decide where AI belongs, what it should improve, who should own it, and how to make sure it supports customers and employees rather than adding another layer of complexity.
“AI adoption is going to need operators, not just technologists,” Booth says. “Someone has to understand the business well enough to know where the system should change and where it should not.”
That is also where he sees a risk. If companies approach AI adoption as a race to add tools, they may miss the deeper work. They may automate steps that should have been redesigned first. They may introduce systems that create confusion for teams. They may collect more data without improving judgment.
For Booth, the better approach starts with the business itself. What is slowing people down? Where do customers feel the delay? Where are teams making the same decision repeatedly without enough information? Where is expertise trapped in one person instead of reflected in a stronger system?
Those questions keep the focus on real outcomes instead of technology for its own sake.
“The goal is not to make the business look more modern,” Booth says. “The goal is to make the business work better for the people running it and the people depending on it.”
That human side is important to him. Booth does not see AI as a replacement for the experience that exists inside traditional industries. He sees it as a way to support that experience with better infrastructure. In many overlooked sectors, workers have learned to solve problems despite old systems. The opportunity is to give those teams better support, not erase the knowledge they have built.
Serving as a judge at the Caltech Hackathon gave Booth another view of emerging technical talent and innovation trends. It also reinforced his belief that a strong idea has to be tested against real-world use. Technical creativity matters, but usefulness depends on whether a solution can handle actual customers, actual teams, and actual constraints.
Looking ahead, Booth wants to keep helping bridge emerging technology and the businesses that make up everyday life. He believes AI may drive one of the largest economic transformations in decades, but the impact will depend on thoughtful implementation in the places where work actually happens.
For him, the overlooked industries are not behind the story. They may be where the next chapter becomes real.
“AI will create lasting value when it reaches businesses that have been waiting too long for better systems,” Booth says. “That is where the work matters. Not in the promise, but in what changes for the people using it every day.”
For more information on Darius Booth, visit LinkedIn.
