Why Most GenAI Rollouts Fail
Most GenAI rollouts fail for the same reason the first ML projects failed. Everyone budgeted for a subscription and got a systems integration.
In a mid-market logistics business, the plan sounds reasonable: buy Microsoft Copilot, add an OpenAI-powered chatbot to the customer portal, and let it “learn” from policies in SharePoint and tickets in Salesforce.
Then reality shows up.
Fine-tuning is not configuration. Validation is not QA. Deployment is not “turn it on.” You are building a behavioral system that needs training data, evaluation criteria, drift monitoring, and someone who owns what “good” looks like when the model is wrong.
Most SaaS leaders never lived through that enterprise rhythm. They assume the hard part is selecting the vendor. The hard part is accepting that accountability cannot be outsourced, because the model is now part of operations, like pricing rules or compliance workflows.
The tell is when a two-week pilot becomes a three-month debate about sources, edge cases, and who approves answers.