Hey there, it's Brandi Lawson with Elevated. Today we're talking about something that's creating both excitement and anxiety in the kitchen and bath industry. Artificial intelligence. There's so much noise about AI right now. It's like trying to have a conversation at a construction site. What I'm seeing across the industry is fascinating. While everyone's talking about AI for design, vis visualization and renderings. Which has its place, the real game changer is happening on the business side. Yet that's exactly where there seems to be the most resistance and confusion. The truth is, most kitchen and bath professionals are leaving money on the table by focusing on the wrong AI applications. Today I'm giving you a practical framework to evaluate which AI capabilities actually deliver value for your business so you can cut through the hype. And focus on technologies that impact your bottom line. Let's cut through the noise and break down what matters when evaluating AI technologies for your kitchen and bath business. First, identify what I call AI that matters versus AI that markets real. AI solves specific business problems. It's not just a flashy demo. You might be using AI for automatic cabinet placement optimization. Designers still make the creative decisions, but the AI handles the mundane spatial calculation. This can save approximately three hours per kitchen design while improving accuracy. That is AI that matters. By contrast, I've seen software with impressive looking AI visualizations that look fantastic in demos, but don't integrate with actual working drawings. That's ai, that markets it looks good in a sales pitch, but doesn't improve your workflow. Second, evaluate the quality of the AI's training data. All AI is only as good as the data it was trained on. Ask vendors, what data trained your AI for kitchen design? Specifically, was it trained on actual architectural plans and building codes or just internet images? Third, consider the human AI workflow. The most effective AI tools in our industry don't replace designers. They augment them. Look for software where AI handles repetitive tasks while keeping humans in control of creative and client facing decisions. The sweet spot is AI that handles the procedural work. Freeing your team to focus on the work that requires human creativity and relationship building. Fourth, assess integration capabilities. Standalone AI tools often create more work than they solve the most valuable AI solutions integrate seamlessly with your existing design and project management workflow. Finally evaluate the learning curve versus benefit ratio. Some AI tools deliver amazing results, but require weeks of training to use effectively. Others might be less powerful, but can be implemented immediately. Be realistic about your team's capacity to adopt new technology. The best AI is the one your team will actually use. Let me share a practical framework that you can use to evaluate AI capabilities in your software selection process. Create a simple grid with your key business processes. Down one side, let's say design creation specifications, client presentation, revisions, ordering, and these evaluation questions across the top. What specific task does the AI automate or enhance? How much time could this realistically say per project? Does it require extensive training or setup? How does it integrate with our existing workflow and. What creative control do we maintain for each potential AI feature? Score these questions from one to five. This helps transform vague promises into concrete evaluation. For example, when evaluating AI rendering tools, you may discover that one option generates photorealistic images 70% faster than the existing manual process. It requires minimal training, works directly with your existing design files, and still allows designers to control material selection and lighting that with got a score high across the board. Meanwhile, an AI specification writer that promises to automatically generate complete specifications. But requires extensive manual data entry, doesn't integrate with the current design software, and frequently makes errors requiring human correction. This one would score poorly on the evaluation grid. This framework can help identify which AI features deliver genuine value versus which ones might just be expensive distractions. This week, you can map your way to AI feature clarity. Head to fiery effects.com/choose and download the worksheet. Take a software solution you're already considering or maybe already using, and put its claims to the test. Ask these specific questions. What measurable business outcome will this AI feature improve? How much setup and training is required before seeing benefits? What control do we maintain over the process and output? How does this integrate with our existing workflow and what happens when the AI makes a mistake? Because it will remember, the goal isn't AI for AI's sake. The goal is solving real business problems and creating competitive advantage. Some of the most valuable AI tools in our industry right now focus on the mundane tasks like automated measurements, specification generation and error detection, not the flashy stuff that makes for great demo. Next week we'll cover implementing your chosen software and creating a roadmap that ensures successful adoption across your team. If this episode helped you cut through some of the AI confusion, share it with another design professional who's trying to sort out this AI mystery.