Artificial intelligence is quickly becoming another tool in the restoration toolbox. Whether you’re drafting a customer email, summarizing a drying report, researching an insurance guideline, or brainstorming a solution to a difficult problem, AI has become an incredible productivity tool. As AI adoption continues to grow, you’re likely to hear terms like tokens, AI credits, and compute. They sound technical, but the underlying concepts are easier to understand than you might think.
Think about a typical water loss. Running one air mover consumes electricity. Running twenty air movers and five dehumidifiers also consumes electricity. Both are accomplishing useful work, but one drying chamber requires significantly more power because there’s simply more work to do. Artificial intelligence works much the same way. Every request requires computing power, but not every request requires the same amount.
Behind the scenes, AI doesn’t read information the way people do. Instead, it breaks everything into small pieces called tokens. Your prompt, the conversation history, emails, estimates, drying reports, PDFs, standard operating procedures, and even the AI’s response are all processed as tokens. You don’t need to understand how tokens are counted, just like you don’t need to understand how many watts an air mover consumes. What matters is knowing that the more information AI must process before answering your question, the more computing resources it generally requires.
That brings us to AI credits. Think of tokens as the individual units of information AI processes, while AI credits are one way some enterprise AI platforms measure the computing resources required to complete that work. Continuing the restoration analogy, if tokens are the electricity flowing through the equipment, AI credits are similar to the electric bill. The bill isn’t based on how many times you flipped the switch; it reflects how much power was required to complete the job.
For example, imagine asking AI to draft a follow-up email thanking a customer for choosing your company. That’s a relatively lightweight request, much like plugging in a single air mover. Now imagine asking AI to review the estimate, drying reports, moisture readings, customer emails, equipment logs, insurance guidelines, and your company’s standard operating procedures before recommending the next best course of action. Both requests begin with a single prompt, but the second requires substantially more computing power because the AI must first locate the relevant information, retrieve it, compare it, reason through it, and then generate a response.
This is why organizations building AI assistant spend time designing them intentionally. Rather than giving every AI assistant access to every document and every system, thoughtful design limits what the AI needs to search so it can answer accurately and efficiently. An AI assistant that only needs yesterday’s drying report will generally require fewer computing resources than one searching years of project documentation, hundreds of PDF estimates, and multiple knowledge bases before responding.
The good news is that, for most restoration professionals, everyday AI use isn’t something to worry about. Drafting emails, summarizing meetings, improving documentation, or asking general questions are the types of tasks modern AI platforms are designed to handle efficiently. In our experience, those everyday activities haven’t been where consumption becomes noticeable. More complex AI workflows that analyze large collections of documents or interact with multiple business systems naturally require more computing resources, but they also have the potential to deliver some of the greatest returns by eliminating hours of manual research and improving consistency.
As our industry continues embracing artificial intelligence, I don’t believe success will be measured by who submits the most prompts. It will be measured by who solves the right problems. Just as we wouldn’t fill a small bedroom with twenty air movers when three will properly dry the space, we should build AI solutions that use the right amount of computing power for the task at hand. Sometimes a simple prompt is all that’s needed. Other times, the problem justifies an AI assistant capable of searching thousands of documents before providing an answer.
Technology has always been about removing friction, and AI is no different. The organizations that thrive won’t necessarily be the ones consuming the most AI credits. They’ll be the ones building thoughtful solutions that create measurable value for their customers, their team members, and their business. Because just like on a restoration project, it’s not about how much power you consume; it’s about the results you achieve with it.
Taylor Carmichael
Taylor Carmichael is the Director of Systems at Southeast Restoration, where she leads the integration of technology and operations across the cleaning and restoration industry. With a Master of Information Systems and over a decade of industry experience, Taylor focuses on streamlining workflows, improving communication, and driving scalable solutions. She’s passionate about making technology practical by bridging innovation with day-to-day execution to help restoration teams work smarter and grow stronger.
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