Busy Beaver Building Centers

Adam Gunnett

Operational Excellence Authority

For years, business intelligence was viewed primarily as reporting. Organizations built dashboards, reviewed KPIs and analyzed historical trends. While those tools remain important, my approach to business intelligence and strategy has evolved significantly over time. Today, I see BI less as a reporting function and more as an operational enablement function, one that should help employees make better decisions in real time.

In retail, especially independent retail, speed and execution matter just as much as strategy. The most valuable technology initiatives are often not the ones with the most advanced features, but the ones that solve real operational problems in ways that are practical, scalable and easy for teams to adopt.

One of the biggest lessons I have learned is that AI alone does not solve broken or inefficient processes. In fact, throwing AI at a bad process often just creates a more expensive bad process. Organizations sometimes become so focused on implementing the latest technology that they skip an important step: clearly identifying the actual business problem they are trying to solve.

At Busy Beaver Building Centers, we pride ourselves on delivering legendary customer service. That commitment influences every technology decision we make. A few years ago, we identified a growing operational challenge inside our stores. Associates often needed to answer customer questions about pricing, product details, inventory availability, aisle locations or stock at nearby locations, but access to information was limited by the number of available store terminals.

The issue was not that our associates lacked willingness or customer focus. The issue was access. In many cases, employees had to leave the customer, wait for a terminal to become available or rely on another associate to retrieve information. Even small delays impact the customer experience in retail.

Before implementing AI, we spent time understanding the workflow itself. We looked at where delays occurred, how associates searched for information and what customers were actually asking most frequently. Once we understood the process, the technology decision became much clearer.

That is where FastQuery AI became successful for us.

Rather than deploying AI simply because it was trending, we used it to solve a clearly defined operational problem. FastQuery AI gave associates immediate access to pricing, inventory, product information and store-level data directly from mobile devices or desktops. Instead of walking away from customers to find a terminal, associates could stay engaged in the aisle and provide answers immediately.

The technology supported our existing culture instead of replacing it. AI did not create legendary customer service on its own. Our people already cared deeply about helping customers. What FastQuery AI did was remove friction that prevented associates from delivering the level of service they wanted to provide.

  ​The organizations that will succeed with AI are not necessarily the ones implementing the most tools. They will be the organizations that combine strong processes, clear strategy, operational discipline and practical technology adoption.  

That distinction is important because one of the biggest challenges organizations face today is turning data into actionable decisions. Many businesses have more data than ever before, yet employees still struggle to act on it quickly enough to create meaningful operational impact. Dashboards alone do not improve performance. Data must be delivered in a way that fits naturally into how employees already work.

Alignment between business intelligence teams and broader organizational goals is critical in achieving that outcome. BI teams cannot operate in isolation. At Busy Beaver, our most successful projects happen when technology, operations, merchandising, marketing and store leadership are aligned around a common objective.

For us, that objective is to make it easier for associates to serve customers while improving operational efficiency. Every technology initiative must support that mission. If a project creates complexity without improving execution, adoption becomes difficult regardless of how advanced the technology may be.

This is also why the role of business intelligence is changing so rapidly. Traditional BI focused heavily on historical reporting and centralized analysis. Modern BI is becoming increasingly operational, predictive and embedded directly into frontline workflows. AI is accelerating that transformation by allowing organizations to surface insights instantly instead of requiring employees to search through multiple systems or reports.

However, successful AI adoption still depends on trust, process design and organizational alignment. Employees must understand how tools help them perform their jobs better. Leadership teams must ensure that technology supports business objectives instead of becoming disconnected innovation projects.

For future leaders in business intelligence and strategy, my advice is to stay focused on operational reality. Learn the business deeply before trying to transform it. Spend time with frontline employees. Understand where friction exists. Identify problems clearly before selecting technology solutions.

The organizations that will succeed with AI are not necessarily the ones implementing the most tools. They will be the organizations that combine strong processes, clear strategy, operational discipline and practical technology adoption.

AI is incredibly powerful, but process still matters first.