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From leaner development teams to faster customer service, businesses are using AI to multiply human capacity while keeping people in critical decision-making roles.
Businesses across industries are turning to artificial intelligence not simply as a convenience, but as a strategic way to reduce operating costs and streamline workflows. In software development, customer support, product design, and operations, AI is helping teams reclaim time, reduce bottlenecks, and handle more work without proportionately expanding headcount. This shift is gaining momentum, but the strongest results are emerging where AI works alongside human judgment rather than attempting to replace it.
RKS Design: Tackling the Cost of Misalignment
At RKS Design, founder Ravi Sawhney sees AI’s value extending beyond faster task execution. His EYDORA platform, built around the company’s PsychoAesthetics methodology, uses AI as a human-centric foundation for business strategy, addressing what Sawhney identifies as a major productivity drain: team misalignment.
Instead of building large teams that can gradually move away from a common objective, EYDORA codifies strategic intent into a shared foundation for AI to execute against. Sawhney points to the scale of the resulting efficiency: “We’ve had six developers all doing the work of 10 developers… You could almost estimate that in a year, 10 people did the work of 100 people, but in unison, because the drift of managing 100 people would be so inefficient.”
That focus on alignment also shapes the platform’s commercial impact. “The biggest challenge is the misalignment, and the drift that occurs in business. You’d have 15 people all leaving the room in agreement to something different. They all heard the same thing, and they all walked away with a different impression of what was happening,” Sawhney said.
For Lynx, a pricing test using EYDORA showed consumers were willing to pay $150 rather than $100 for its signal booster. “We pressure tested EYDORA… it showed that people wouldn’t value it at $100 — the right sweet spot is $150. That moved the EV of the company from $100 to $250 million at this stage.”
Lever: Saving Time Without Removing People
Paul Scolieri, founder of Lever Agency, approached AI from finance rather than coding. Running a web development firm serving small and medium-sized businesses, he focused on routine tasks that consumed time better spent on higher-value work.
“It’s taken one task that used to probably take me an hour and chunked it down to five or ten minutes. But then you start to stack those — stack three, four, or five of those tasks — and all of a sudden you are actually gaining back that time on lower-value tasks.”
For Scolieri, however, efficiency does not mean eliminating human involvement. “Not having that human checkpoint, I think, is a big mistake.” He also added: “For me and for a lot of other small businesses, your brand is your reputation. If the goal is still to provide excellent service, what AI should do is multiply what you can do, but not completely remove you from the day-to-day operations.”
The larger gains come when AI connects with existing systems. “If you give AI access to the tools that you regularly use and your knowledge, the output is drastically more improved — now it has everything it needs to give you that extra value, and not just be like kind of a novelty chatbot.”
Journey’s End Games: AI Connects the Operations Chain
At Journey’s End Games, programmer and operator Joshua Pereyda has seen AI expand what smaller development teams can accomplish. New hires can take on larger assignments sooner, while senior staff increasingly manage AI systems and review their outputs rather than writing every piece of code themselves.
“Right off the bat, we’re able to give new hires bigger tasks… just the scope that people are able to start out with is huge. They can work with the customer directly, collect the requirements, and go make it themselves without having to have an intermediary.”
AI is also reducing the manual work involved in linking order intake, fulfillment, shipping, and inventory systems. “In our operations side, there’s just the manual work of connecting systems together. Connecting all those things typically has involved either a lot of manual work from employees, or some kind of custom software. Now you make a piece of custom software that’s very cost-effective to make — in the best case, it can even be made by people internally,” Pereyda said.
Yet human oversight remains important. “You’ve got to have human in the loop for some workflows. Even if our algorithms can present a price, there’s a lot of sanity checking — and even customer service. Having that human customer service point is a big deal.”
bookadabra: Automation Meets Its Limits
For bookadabra co-founder Uri Abramson, AI enabled an entirely new product: personalized 200-page books and comics. “Two years ago, if someone wanted to create something like this, it would require professional writers to write a full book, and it could take weeks. This is not just a way to improve an existing process — this creates a whole new category of products.”
But a rapid-growth customer support crisis exposed the limits of full automation. “They could do 80 or 90% of the work. But eventually, it wasn’t something that could replace a person. And instead, we treated it as a co-pilot. When we shifted to this co-pilot approach, everything improved.”
Abramson found that AI performance can deteriorate when overloaded with context. “Still today, the final decision making requires a human. When you engineer the context you bring to AI, you can’t always predict what it will need to make the best call. If we load the 200-page book into the context, the AI performance degrades. At least for now, you need someone to make the final call.”
AI as a Multiplier
The companies generating meaningful returns from AI are not necessarily those with the largest budgets. Their advantage lies in identifying which tasks machines can accelerate and where human judgment remains essential. Across strategy, development, operations, customer service, and product creation, the emerging model is clear: AI works best as a multiplier of human capacity, not a wholesale replacement for it.
