Every vendor is selling AI right now, and most of them are selling noise. The practical question is not whether the technology works, it often does, but how to separate genuine capability from the marketing machinery that has turned every product update into a press release about intelligence. A recent guide on Towards Data Science outlined three moves to stay clear-eyed when the hype is this loud: slow down, demand specificity, and test against your actual workflow. That advice is sound, but it lands harder when you see what is actually happening in the field. Consider Argon enters the spreadsheet arena, challenging Google's data dominance, an AI-native spreadsheet tool that does not claim to be revolutionary. It simply builds a better data model and lets the AI work within it. That is the difference between a product that uses AI and a product that sells the idea of AI.
The second move, demanding specificity, matters because vague claims are the easiest to make and the hardest to verify. When a vendor says their AI "understands your data," ask what that means in practice. Does it generate formulas? Does it flag inconsistencies? Does it adapt to your naming conventions? Vesta secures $30M to bring AI agents to mortgage lending is a useful example here. The company is not selling general intelligence; it is selling agents that handle a specific, high-stakes process in lending. That focus is what makes the claim testable. You can measure whether those agents reduce processing time or error rates. Spreadsheet users should hold every AI tool to that same standard. If the vendor cannot describe what the AI does in a sentence that a project manager could repeat, the product is probably not ready for your workflow.
The third move, testing against your actual workflow, is where most evaluation efforts break down. It is easy to run a demo with clean data and a simple prompt. Real spreadsheets are messy, inconsistent, and full of edge cases that break assumptions. A tool that impresses in a controlled environment may collapse when handed a real dataset with missing values, mixed formats, and undocumented formulas. This is not a reason to avoid AI tools; it is a reason to trial them with your worst data, not your best. The From prison to purpose: AI opens a second act for returning citizens story is a reminder that AI's most meaningful applications often emerge in contexts where the stakes are personal and the constraints are real. If a tool can handle real constraints in lending or reentry, it can probably handle your quarterly budget.
The specific takeaway is this: treat every AI vendor as a candidate that must pass a practical test, not a visionary who deserves the benefit of the doubt. Ask what the tool does that a well-written formula cannot. Ask how it handles a dataset with 40% missing values. Ask for a trial that runs on your real spreadsheet, not their curated example. The vendors that answer clearly are the ones worth exploring. The ones that deflect with vision statements are selling something else.