Every software vendor we speak with claims their platform is AI-powered, but we suspect most of it is just basic automation wrapped in marketing hype. How do we audit these vendor claims to ensure we are actually getting machine learning capabilities that improve our business operations?

Question

Tyler Smith · Accepted Answer

Software vendors love to throw the term AI into their sales pitches to justify higher subscription fees, but most of what they are selling is simple, rule-based automation that does not require machine learning. To avoid wasting capital on tech theater, your leadership team must focus entirely on the operational improvements the tool will enable.

When evaluating a new software platform, ignore the marketing buzzwords and ask the vendor to explain exactly how their machine learning model works and what specific data it uses to make decisions. Demand that they prove how their AI will solve your specific operational bottlenecks. For example, if a project management tool claims to use AI to optimize scheduling, force the vendor to show you how the algorithm handles real-world resource constraints, technician sick days, and last-minute client changes.

Additionally, refuse to buy into any tool that requires a massive, upfront implementation fee before you can see it work. Instead, demand a low-cost, short-term pilot using a subset of your actual operational data. Use your weekly Level 10 Meeting to monitor the pilot's performance against a clear, measurable target, such as a reduction in scheduling errors. If the tool cannot prove its value during this trial phase, walk away. By maintaining this practical, operational focus, you protect your business from expensive tech theater and ensure you only invest in software that drives real efficiency.