AI isn't magic, it's leverage

For the past few years, the business world has been consumed by the hype around AI. We're promised a future of magical solutions and intelligent unicorns that will solve every problem with the push of a button. But as someone who has spent two decades finding technologies to solve real-world business problems, I can tell you the true power of AI isn't magic – it's leverage. AI is a game changer, and it's all about scale. It's about taking the same company, with the same number of talented employees, and enabling them to do ten times more.

Walking the walk: Our own AI transformation

At Priority, we don't just talk about this; we live it. We are “walking the walk”. Before we ever offered a major AI-driven feature to our customers, we turned it on ourselves to drive our own internal transformation. A prime example is our own customer support. We developed an AI knowledge base that sits in our customer portal. When a customer starts to open a ticket, the AI tries to answer the question immediately, deflecting the ticket before it's even created. If a conversation shows a problem has been resolved, the AI automatically closes the ticket. And for complex issues, it intelligently routes the ticket to the team member with the right expertise.

The result is a win-win: our customers get faster answers, and our service team can focus on the problems that truly require their attention. This is the core principle: automate the repetitive, boring work to free up human talent for what it does best.

From weeks to one hour: Delivering tangible AI value

This principle of scale becomes even more powerful when applied to our customers' most complex operational challenges. Recently, a customer needed to integrate a new, high-volume robotic warehouse with their Priority ERP system. In the past, this would have been a massive project, requiring a team of programmers working for weeks to build the integration gateway. Using AI, we wrote the entire gateway – with full documentation – in one hour.

Think about that. An hour. This is what I mean by leverage. It's not about replacing programmers; it's about giving them a tool that allows them to solve a complex problem in a fraction of the time, so they can move on to the next one. We are seeing this across the board, from enabling users to create complex business rules using simple natural language to helping a professor integrate a new task management optimization algorithm into our platform.

The “human in the loop” philosophy

The “human in the loop” philosophy is a core strategic principle for implementing AI that focuses on creating a powerful partnership between machine automation and human oversight. Instead of aiming for full autonomy where the AI operates without checks, this approach leverages AI for what it does best—the complex, technical, and repetitive work—while keeping a person involved for critical approval and decision-making. This creates a system that is both highly efficient and reliably accurate.

AI's role in this model is to perform the “heavy lifting”. A clear example provided in the interview is the generation of business rules within the ERP system. A user can state their need in simple, natural language, such as, “if the profit was below something then send an email”. The AI takes this plain-English command and generates the actual, technical code for the rule. The system then presents this generated rule to the user and simply asks for approval. The person doesn't need to know how to program; they only need to validate that the AI correctly interpreted their business intent. This frees the user from tedious, technical work and allows them to focus on their primary job functions.

This approach ensures control and accuracy while achieving massive efficiency gains, a point powerfully illustrated by the robotic warehouse integration. A task that would have traditionally required a team of programmers working for weeks was completed in just one hour using AI. The programmers acted as the “humans in the loop,” guiding the AI and validating its output rather than manually coding every line. This is the practical application of what I call the key benefit of AI: scale. It allows a company to do “10 times more” with the same number of employees by automating repetitive work. Looking forward, this philosophy is expected to evolve into a system of “specialized autonomous agents,” where the human is only brought into the loop when a key decision is needed or when the agent is unsure how to proceed.

Looking ahead: A future of specialized autonomous agents

Looking forward, this trend will only accelerate. We are heading toward a future defined by specialized autonomous agents. These won't be general, all-knowing AIs; they will be highly trained agents that understand the specific domain of your business. They will handle processes autonomously, involving a human only when a strategic decision is required.

I'm already using this model in my own life. While driving, I can talk to an AI, dictate the structure for an article, and find it ready for me when I get back to the office. We are connecting the dots to bring that same level of efficiency to the enterprise. With the rise of robotics and automation to fill labor shortages, these AI agents will become the central nervous system of the modern, scalable business.

Our challenge to you: Demand proof, not presentations

The perception that bigger vendors are safer is a powerful myth. But safety doesn't come from a brand name on a billboard; it comes from a partner who can solve your unique problems with agility and innovation. When we meet with a CIO, we don't just show a PowerPoint; we take their use case and build a solution live, often in minutes.

So, I'll end with this: if you feel your current vendor can't solve your problems or doesn't hear your pain points, challenge us. I'm sure we can deliver.