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In a competitive business landscape, AI tools are equipping leaders and companies alike with the ability to stay ahead of their competitors in innovative new ways.
Over the past several years, as AI has become an increasingly large part of the modern business landscape, things have begun to shift rapidly. From startups to established enterprises, companies across industries are integrating AI not just to cut costs, but to outthink, outpace, and outperform the competition. The leaders making it work aren’t simply buying software, but rather entirely rethinking how their teams operate, how decisions get made, and what it means to scale. These AI-forward executives are using the technology to stay ahead and leaving their competitors behind in the process.
Untapped Potential
The fashion industry’s biggest inefficiency isn’t bad taste; it’s untapped potential sitting in people’s closets. Nikolai Komarov built Stylin’ on the premise that AI can help anyone dress with intention by maximizing what they already own. As he details, “Based on your preferences, based on your body type, your color type, and all that stuff, based on the items that you own, you receive your personal edition, personal issue of Easy to Digest Fashion Magazine.”
The platform uses AI “squads of agents” to analyze trends, build personalized outfit recommendations, and educate users on why specific looks work for their body type and color profile. “For every outfit that we’re recommending, every outfit that we’re showing to our users, we’re actually explaining why it fits you. It works for you because of this and that, because of your body type, because of your color type,” Komarov explains.
Operating on a zero-employee model, Stylin’ keeps costs low enough to offer 95% of features for free, making sustainability the competitive advantage. This allows the company to continuously innovate in a way that wasn’t feasible prior to AI. As Komarov says, “One recent feature that we released a few weeks ago: you could actually see each outfit on a model that reflects your body type. You can click on changes in settings and say, I have an oval shape; show me models that look more like me. Whatever you see, it resonates better with each individual user.”
An Opportunity for Growth
When Novo’s founder, Michael Rangel, looked at growth, he didn’t see a hiring problem, but rather an AI opportunity. With 150 employees serving more than 250,000 small businesses, Novo has cracked the code on AI-powered scaling. As he explains, “With AI, it’s even a fraction of a fraction of a fraction of what it used to be, which is how we’re able to have 150 employees in service to hundreds of thousands of small businesses.”
The secret: empowering non-technical staff to build their own internal tools through a 10-week live coding program using Claude and VS Code. “We gave our non-technical teams access to Claude and VS Code, gave them some tutorial classes on how to use it, and then we ran a 10-week, twice-weekly vibe coding program just so we can teach them,” Rangel says.
The result has been more than 30 internal apps built by non-engineers, freeing the tech team for strategic initiatives while AI agents automate roughly 70% of repetitive work across customer service, compliance, and fraud detection. “These agents themselves are doing 70% of the work that would have otherwise had to be done manually, and the employees themselves are the ones who just have to check, which is incredible,” Rangel says.
Combining People and AI Adoption
Moving fast with AI sounds appealing until you realize you’re just accelerating toward the wrong destination. Russ Reeder has seen it happen repeatedly: companies deploy AI on broken processes and end up with faster problems, not better ones. “Technology has never been the issue in implementation. We can make technology do whatever you want. People and adoption; that is the most critical thing. And from last year, MIT, 95% of projects fail. Without a doubt, it’s possible,” Reeder says.
His firm, KeyDelta, takes the opposite approach: diagnose and fix the foundation first, then build AI solutions that actually reach production. As Reeder explains, “You can’t just replicate everything you’re doing today, implement AI, and make it faster. Broken processes sped up are still broken processes.”
With an MIT study showing a 95% AI project failure rate largely driven by user adoption failures, Reeder argues that skipping the strategic planning phase is the most expensive mistake a company can make. “With KeyDelta, we really help the clients with their operational efficiencies. We build based on a proper workflow system, and then we maintain it for them as well, so it can grow as they grow,” he concludes.
Gaps That Cost Businesses
Traditional manufacturers and AI experts don’t always speak the same language, and that gap costs businesses real money. Andrew Tjernlund, whose family has been in manufacturing for four generations, built Hambone.AI to bridge that divide.
“There are people who have a technical knowledge of how to use AI and are keeping up with that. And then there are old-school manufacturers. It’s hard to get those two people to talk because they’re just not in each other’s world. And so we’re trying to bridge that gap,” Tjernlund says.
The firm helps manufacturers translate AI’s potential into tangible wins: bots that flag pricing anomalies and trigger smarter purchasing decisions, AI-generated business plans that integrate internal production capacity with market data, and fast-cycle prototyping that moves teams from discussion to execution. “I take all the machines that we have, all the models, all the fasteners, all our sheet metal gauges and sizes, all our different skills and componentry. I put that in with where we sell the stuff. And not only can it generate ideas, but it goes from generation of ideas to, like, a 20-page business plan with all the details,” Tjernlund explains.
But Tjernlund is clear-eyed about what AI can’t do: when a $12,000 AI-estimated mold quote comes back at $26,000 in reality, human judgment has to take over. As he puts it, “I’m not just going to have the robot do all your tasks and slowly replace you. I’m going to have the robot do all the things that you really don’t do, even though you kind of should. As much as you want to do that, you’re trying to put out fires every day, and you don’t have the time to plan for a supply shock three years ahead of time. But maybe the bot could do that for you.”
Experiencing AI Fatigue
After years of AI hype, many businesses are experiencing something less glamorous: disappointment. Sepehr Sisakht calls it “AI fatigue,” the burnout that sets in when pilots fail to deliver ROI in production. “There’s a lot of hype around AI, and now there’s more and more becoming a bit of AI fatigue that you keep hearing about; businesses who, in the past year or year and a half, piloted some projects, and it’s not really going as they were hoping to.”
His firm, AIDOLS Group, tackles this through structured assessment and what he calls “engineered trust,” a framework for determining how much autonomy AI should have based on the consequences of potential errors. High-stakes domains like healthcare and finance compliance keep humans firmly in the loop; lower-risk areas like scheduling can run with more automation. As Sisakht describes, “We approach it in a statistical manner. That’s what I mean by trust should be engineered. You have to question: what is the impact of those wrong decisions? There are sectors where we have a human in the loop as a default. For instance, in healthcare. And in finance, compliance is massive, and one mistake can cost millions of dollars.”
Just as important, says Sisakht, is culture. Successful AI adoption rarely comes from top-down mandates. It requires building internal champions at every level of the organization. “AI is very context-dependent, so you cannot teach the CEO and then expect them to go and implement AI in every part. It is important to make sure every team understands what they can do, what they cannot do, and starts to have that problem-solving perspective within their own team. It’s not an approach that you can go from top to bottom and just basically direct one way,” he concludes.
Final Thoughts
The leaders gaining the edge are not necessarily the quickest movers, but those who move most wisely. Whether developing a zero-employee fashion platform, expanding a fintech to hundreds of thousands of users, stress-testing AI in real-world operations, bridging the AI-manufacturing gap, or embedding trust into every deployment, the common thread is prioritizing strategy over speed. AI alone doesn’t guarantee a competitive advantage. Those who grasp this concept are the ones shaping the future.
