A traditional-software project manager new to Meridian's customer-service virtual-assistant initiative insists on committing to a fixed go-live date and an exact accuracy target before any data has been reviewed. What key methodological difference is this PM overlooking?

AI projects differ fundamentally from deterministic software because their achievable performance is a function of data that has not yet been examined, not just a function of engineering effort. Committing to an exact accuracy figure or a fixed date before Business Understanding and Data Understanding are complete is a category error — it treats an empirical, data-dependent outcome as if it were a specification that can simply be built to. The claim that traditional software simply requires more testing time than AI projects do misdiagnoses the difference as merely more testing time, which understates the issue: it isn't that AI needs longer QA, it's that the achievable outcome itself is unknown until data is explored. The claim that AI projects always cost significantly more than traditional software projects is an unfounded generalization; relative cost varies by project and is not the defining methodological distinction being tested here. The claim that AI projects do not require any requirements-gathering phase is simply false — AI projects still require requirements/business-question framing, just adapted through practices such as DIKUW and scoping rather than eliminated. The correct framing is central to why CPMAI recommends an iterative approach: initial estimates are refined as Data Understanding findings come in, and premature fixed commitments are a leading driver of stakeholder disappointment later in the project.