Why AI transformation begins when leaders stop watching and start building.
Artificial intelligence has reached an interesting point in business. No one needs to be convinced that it’s important anymore. We’ve all attended the webinars, watched the demonstrations and heard predictions about how AI will reshape every industry. Awareness is no longer the challenge—action is.
After working alongside nearly 20 direct selling companies over the past two years and helping develop hundreds of AI use cases, I’ve become convinced that the companies making the fastest progress share one characteristic. They aren’t necessarily the biggest or the best funded. They simply have leaders who have stopped treating AI as someone else’s responsibility.
When organizations struggle to gain traction with AI, the problem usually isn’t the technology. It isn’t the budget, the strategy or the board. More often than not, it’s that the people leading the organization haven’t become practitioners themselves. AI transformation is personal before it becomes organizational, and until leaders embrace it that way, the rest of the business rarely follows.
The Difference Between Supporting AI and Using It
Many organizations can point to AI initiatives already underway. They’ve hired specialists, formed committees or invested in new tools. Those are all positive steps, but they’re often mistaken for transformation when they’re really just preparation.
One of the biggest mistakes I see is believing that hiring a Head of AI solves the problem. Experienced AI leaders absolutely add value, but they can’t singlehandedly create a culture around AI. Too often, organizations hand AI to one person or one department, expecting innovation to spread naturally throughout the business. What develops instead are isolated experiments with little connection to day-to-day operations.
AI isn’t a department. To truly become effective AI has to become part of the way leaders think about solving problems. That happens when executives stop asking what AI can do for the company in theory and start asking what it can do for them today.
When we evaluate organizations, one question matters more than almost any other: Who owns the outcome? Not who owns AI itself, but who is accountable for improving productivity, customer experience or operational efficiency with AI. Those are very different conversations, and companies making the most progress have leaders who see AI as another management tool rather than another technology initiative.
Too often, organizations are focused on deploying AI instead of helping people adopt it. That’s why so many initiatives stall after the initial excitement wears off. AI is happening to people, not for people, and that’s the gap to close.
Experience Changes Everything
The same principle applies to governance. Many companies create AI committees made up of senior leaders because that feels like the responsible thing to do. The problem is that committees often reflect titles rather than experience. If the people making decisions about AI aren’t actively using it themselves, conversations quickly become theoretical.
The most valuable AI discussions happen when leaders have spent time experimenting. They’ve written prompts that failed, discovered workflows they never considered and experienced firsthand how quickly ideas can move from concept to execution. Those experiences change expectations because they replace assumptions with understanding.
Interestingly, some of the fastest-moving organizations we’ve worked with are companies under $150 million in annual revenue. They don’t have unlimited budgets or large innovation teams. What they do have are leaders willing to experiment personally. They identify a problem, build a solution, learn from it and improve it. That cycle happens in days rather than quarters.
Large organizations certainly have advantages, but they also have more opportunities to delay action. AI rewards curiosity and speed as much as investment.
Don’t Just Reinvent Yesterday
As organizations begin using AI, they often focus on making existing processes more efficient. That’s a logical starting point, but it shouldn’t become the destination.
Take AI meeting assistants. Most of us now use tools that record meetings, summarize discussions and generate action items automatically. They’re helpful, but in many ways we’ve simply replaced handwritten notes with digital ones.
The bigger opportunity comes when we stop asking how AI can document work and start asking how it can improve work.
Imagine using those same meeting transcripts to identify coaching opportunities across distributor presentations, compare conversations against proven scripts or detect compliance concerns before they become larger issues. Suddenly the value isn’t in producing better notes. It’s in uncovering patterns that improve performance across the organization.
That’s how we approach AI inside our own business. We don’t simply archive conversations. We analyze them. Every meeting becomes another source of insight about repetitive work, recurring challenges and opportunities to automate tasks that people have accepted simply because they’ve always done them that way.
Simply put, transformation begins when we stop reinventing the past and start reimagining the future.
Builders, Not Bystanders
One of my favorite examples involves two CFOs. Neither worked in IT. Neither considered themselves software developers. Both were frustrated by recurring business problems.
One was overwhelmed by commission-related questions coming into his department. Instead of waiting for someone else to build a solution, he created an AI agent that answered many of those questions automatically. Another faced a logistics challenge that a vendor estimated would take months and a substantial investment to solve. Rather than waiting, he built a working solution himself over a weekend, deployed it and refined it afterward.
The lesson here isn’t that every executive should become a programmer. The lesson is that AI has dramatically lowered the barrier between identifying a problem and testing a solution. The people closest to the work now have tools that allow them to prototype ideas before they ever become formal projects.
That changes the pace of innovation because learning no longer depends on lengthy development cycles.
Why We Tell Leaders to Play
One of the simplest exercises we run is also one of the most effective. We bring teams together for a 75-minute “hackathon” with one unusual rule: don’t build something for work.
That surprises people. Most expect to spend the session solving business problems. Instead, we encourage them to create something fun because it removes the pressure of getting everything right. The objective isn’t the finished product. It’s building confidence.
During one session, team members created everything from AI-powered meeting analytics to personalized news briefings. I decided to build an online business that turned uploaded photos into cartoon greeting cards, connected it to a fulfillment company and placed a real order before time expired. The finished card wasn’t perfect, but that wasn’t the point.
Seventy-five minutes earlier, the only tangible thing that existed was the idea.
The real impact came the following morning. People returned to work looking at their own responsibilities differently. They started identifying repetitive tasks they could eliminate and problems they could solve themselves. AI had shifted from being something they admired to something they used.
Start with One Problem
Leaders often ask where they should begin their AI journey. My answer is always the same: don’t start with a company-wide strategy—start with one problem.
Find one repetitive task that consumes too much time. One workflow your team quietly complains about every week. One process you’ve accepted because “that’s the way we’ve always done it.” Then try to make it disappear.
Governance, security and long-term strategy all matter, but they shouldn’t become reasons to postpone experimentation. Leaders who build confidence through personal experience make better decisions about how AI should be deployed across the organization because they’ve already seen what’s possible.
The companies pulling ahead aren’t waiting for perfect roadmaps. They’re led by people who are curious enough to experiment, practical enough to solve real problems and willing to learn in public. The world has already changed. It’s time for leadership to change with it.
DAN DEBNAN, Founder and CEO, Inovara, is a seasoned entrepreneur and marketing expert. At 19, he launched a digital agency that was later acquired. In 2016, he joined a European direct selling company as Head of Marketing, driving key innovations and growth. After five years, Dan moved on and founded Conturae, an AI startup offering a global content creation platform. His focus on leveraging new technologies helps businesses operate efficiently and scale effectively.
An Online Exclusive from Direct Selling News magazine.
