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Small teams are using AI to move faster, share knowledge, and rethink where human expertise creates the most value
A company can add capacity without immediately adding another desk. AI tools are taking over pieces of research, administration, and production that once demanded substantial employee time, allowing lean teams to stretch further. That changes the conversation around growth because efficiency alone offers an incomplete measure of success. Leaders still have to decide where automation earns its place and when a person’s expertise contributes more.
Small Teams Build Bigger Capacity
At Exalt Growth, founder and principal consultant Jack Boutchard has seen a five-person team expand its capacity through 49 AI “skills” that handle pieces of research and execution. One prospecting tool can turn a company URL into a detailed research document within minutes, compressing work that once stretched across several days.
“What previously might have taken a week can be condensed into a day,” Boutchard said. “It’s just really unlocked the ability to go from problem to solution in a much quicker and, in a lot of cases, [a] more effective way.”
Shared knowledge becomes equally important. “You have almost the ability to have everyone have the same sort of context and understanding,” he said, describing a system that carries information from one day to the next.
Automation has consequently changed what Boutchard needs most from his staff. “My team of five could almost just become me in the sense that everything is able to be systemized and automated to a degree where the only thing that is actually needed is the judgment.”
Data Work Gives Way to Analysis
QuickData.ai founder David Bratslavsky encountered a different bottleneck in real estate underwriting, where teams could spend nearly all their time manually entering information. His company initially built an Excel add-in for extraction before pivoting toward teaching clients to create their own AI skills.
“What we’ve seen consistently is that it frees up people to do things that only people can do: investing in relationships and making decisions,” Bratslavsky said. “A lot of times you don’t have time for those because you’re busy with the busy work.”
Improving model performance has widened the range of tasks his clients can explore. “The accuracy of these tools when dealing with important data has gotten so good that you could really just prompt and say, ‘double-check your work,’ and it does it,” he said.
Bratslavsky expects adoption to become a competitive dividing line. “Within three years, either you’re a company that is leveraging AI and the efficiencies inherent in it, or you’re a company that’s on your way out.”
Productivity Changes the Business Model
For Kreios CEO Ulf Herbig, faster software development raises questions that extend beyond workflow. “If you compare 10 years ago from now, you can have the same individual being 10, 15, maybe even 20 times more productive than before,” he said.
That gain has pushed Herbig to reconsider hourly billing. “Times and means in software development should rather become outcome-based pricing,” he said. “Because as a customer, what do we care most about? The outcome.”
Kreios has applied a different division of labor to Regmatics, its financial document management platform. AI handles data extraction, while human-defined rules govern comparison and control.
“In the nature of Gen AI, the result is probabilistic,” Herbig said. “And what you don’t want in a regulated environment is probabilistic outcomes.”
He added, “At the moment where we use AI to create code that represents a regulatory requirement, it’s deterministic and therefore reproducible as often as you want.”
Expertise Still Comes Before Automation
Dev Centre House CTO Richard Robu approaches adoption with expertise as the starting requirement. “We truly believe in being the expert first and using AI second,” he said. “That is super, super critical, because leveraging AI just to speed things up can cause more trouble in the long term.”
The software firm has already applied the technology to its own online visibility. “In today’s world, with people leveraging AI more and more to find search results, it was a very critical thing for us to make sure we come up in the right places to the right person,” Robu said, pointing to search optimization for newer AI-powered discovery tools.
His longer-term interest reaches into physical production. “I’m very excited to see how we can leverage AI, especially in the manufacturing space, to improve accuracy and speed of processes in things that are actually being produced by machines.”
Scaling Becomes a Question of Judgment
Faster work is only one part of what AI is changing inside businesses. Teams can spend less time assembling research, entering data, or completing repetitive production tasks and devote more attention to problems that require experience.
As companies experiment with where automation fits, the strongest applications are likely to have a clear purpose behind them. Technology is taking on defined work, with people determining what happens next.
