Before You Scale AI, Fix This First
AI is everywhere right now.
It is in headlines, in boardrooms, in sales meetings, and in the quiet corners of someone’s browser while they draft an email. Some leaders are racing toward it. Others are hesitating. Most are somewhere in the middle, trying to figure out how to use it responsibly without getting left behind.
That tension is exactly why my latest conversation with Hunter Jensen matters.
Hunter is the CEO of Barefoot Solutions and founder of Barefoot Labs, where he now focuses on building secure AI tools for businesses. He has spent more than twenty years staying on the front edge of technology shifts. From web to mobile to IoT to blockchain, he has seen waves come and go. What makes AI different, in his view, is not the hype. It is the speed and the scale of impact.
He traces today’s acceleration back to a breakthrough called the transformer model, which fundamentally changed how machine learning systems are trained. What once would have taken decades can now happen in months. That technical leap is why generative AI exploded into the mainstream in 2022 and why it continues to evolve at a pace that feels impossible to track.
But the most important part of this conversation was not the history lesson. It was the warning.
Hunter believes too many businesses are trying to layer AI onto unstable foundations. They want the tool before they have the structure. They want the output before they have the discipline. And that approach creates risk.
For Hunter, the starting point is data. Clean, current, and complete. Clean means no duplicates, no conflicting versions, no scattered sources of truth. Current means the most up-to-date information is the only information feeding the system. Complete means you are actually capturing the data you will need and capturing enough of it to make it meaningful.
AI does not fix messy systems. It amplifies them. If your organization has three versions of the same warranty document floating around, the model will not know which one to trust. If your internal documentation is inconsistent, the answers AI produces will be inconsistent too. Garbage in, garbage out is not just a cliché. It is a practical reality.
From there, the conversation shifts to governance. Hunter is direct about this. If leaders do not provide clear direction and secure tools, employees will still use AI. They will paste content into public models. They will upload documents. They will experiment. Most of the time with good intentions. And in doing so, they may unknowingly expose confidential information.
That is why strategy and policy cannot be optional.
Hunter encourages leaders to define an AI strategy that focuses on return. Not on novelty. Not on trying to add AI everywhere. He talks about identifying a killer use case. One area where the return on investment is clear and measurable. Shorter sales cycles. Faster customer support responses. Reduced time spent answering repetitive HR questions. Pick one. Prove value. Then expand.
Alongside strategy comes guardrails. An AI policy that defines what tools are approved, what data can be used, what must remain private, and where human review is required. Hunter emphasizes that governance should not be a document that sits quietly in a handbook. It needs to be discussed. Explained. Understood. Because whether leaders formalize it or not, AI adoption is already happening inside their organizations.
One of the most practical insights Hunter shares is that AI can help build the very policies meant to govern it. His advice is clear. Yes, use it. But never without a human in the loop. Treat large language models like brilliant assistants who know nothing about your business until you give them context. Ask them to draft. Then ask them what is missing. Ask them to critique the gaps. Refine. Edit. Review with legal counsel when appropriate. Use the speed of AI without surrendering judgment.
We also explored real use cases that go beyond theory. Customer support teams using internal documentation to respond instantly instead of in days. Sales teams querying complex price books or specifications in real time during conversations. Manufacturers searching past specials to determine feasibility without waiting for back-and-forth approvals. In each case, the opportunity is not replacing people. It is elevating them. Reducing friction. Compressing timelines. Freeing teams to focus on higher-value work.
Perhaps the most compelling part of Hunter’s story is personal. He describes the moment he realized AI could write code faster than his engineers. His business model was built on selling engineering hours. Suddenly, what once took one hundred hours could take three. Instead of ignoring the threat, he pivoted. He built a secure AI product designed to live inside a company’s own cloud infrastructure. That decision reflects the mindset he advocates for leaders. Pay attention. Adapt early. Build the next model before the old one collapses.
Keeping up with AI isn't about chasing every new tool that appears in your feed. It is about building responsibly so that AI becomes a competitive advantage instead of a liability.
The opportunity is real. The speed is undeniable. But the foundation still matters. Before you scale AI across your organization, pause and ask yourself one simple question: What’s one piece of data or documentation in your business that has to become the single source of truth before you scale AI? That answer may tell you exactly where to begin.