The term "AI-Powered" has become the new "cloud-based"—a meaningless marketing term often used to justify a price hike for a feature that is, at best, a glorified if/else statement. As engineers and technical buyers, our job is to look past the buzzwords and systematically dismantle the vendor's claims.

Having evaluated dozens of so-called "AI" tools, I've developed a simple framework for spotting the AI-washing. Here are the red flags to look for.

Red Flag #1: They Can't Explain the "How" If a vendor uses terms like "intelligent algorithms" but can't articulate whether they are using NLP topic modeling, a forecasting model, or a simple heuristic, it's a major red flag. Real AI applications are built on specific methodologies. A vague explanation often masks a superficial implementation or a complete lack of in-house expertise.

Red Flag #2: They Pitch Features, Not Outcomes A demo that is a whirlwind tour of flashy "AI features" without a clear connection to a measurable outcome (e.g., reduced latency, lower error rates, improved conversion) is a sign of tech for tech's sake. Transformative AI doesn't just add features; it solves a quantifiable problem.

Red Flag #3: The "Magic Black Box" Defense When you ask about the data model, training requirements, or how they measure accuracy, and the answer is "it's proprietary" or "it just works," be wary. This lack of transparency is a massive governance and risk issue. It raises immediate concerns about hidden biases, data privacy, and simple ineffectiveness. A real AI vendor can discuss their conceptual approach to model training and explainability without giving away their IP.

Red Flag #4: The "AI Island" Architecture An AI solution that doesn't have a clear, robust integration strategy with your existing systems is a recipe for data silos and manual workarounds. AI rarely delivers value in isolation; it needs to consume data from and feed insights back into your core operational workflows via well-documented APIs.

Red Flag #5: They Have No Real-World Proof Grandiose claims of near-perfect accuracy and universal applicability are easy to make on a marketing slide. The ultimate proof is in the implementation. If a vendor cannot provide you with detailed, relevant case studies with measurable results from a company of a similar scale and complexity to yours, they are likely selling a promise, not a product.

Conclusion: Demand Proof, Not Promises The potential of AI is real, but the current vendor landscape is rife with hype. Approach it with the same critical thinking you would apply to a code review. Ask the hard questions, demand transparency, and focus relentlessly on tangible, measurable outcomes.