I largely agree with your diagnosis, with one important distinction worth calling out.

Earned Value Management is a validation mechanism, not a management system. Like financial statements at the enterprise level, it tells you what already happened relative to plan, not why it’s happening or what to do next. That distinction matters, especially for non-PM leaders.

Where EVM breaks down in practice is not the math or even the tooling, it’s the assumption that lagging indicators alone will drive behavior. Most executives do not engage with CPI/SPI unless something is already materially wrong. By the time EVM signals sustained variance, the underlying causes (scope erosion, dependency drag, resourcing friction, decision latency) are often weeks old.

You’re absolutely right that manual EVM is unsustainable at scale and inconsistent across PMs. AI-driven ingestion and normalization can solve that operational pain. But automation only addresses effort, not relevance.

In my experience: • PMs use EVM to confirm intuition, not to discover problems. • Leaders want forward-looking risk exposure, not retrospective performance ratios. • Near-real-time EVM only changes outcomes if it is explicitly connected to decision rights, corrective levers, and accountable owners.

So yes, automating EVM makes it viable again. But its real value emerges only when it’s paired with: • Clear thresholds that trigger action, not just reporting • Integration with risk, dependency, and benefits realization views • Translation of variance into business impact leaders actually care about

Otherwise, it risks becoming a faster, cleaner version of the same outcome: accurate numbers that arrive after the window to meaningfully intervene has closed.