When an Energy Trading and Risk Management (ETRM) upgrade slips, the post-mortem rarely blames the upgrade itself. It blames testing.
Regression windows stretch. Environments misbehave. Critical workflows fail in ways no one saw coming. By the time the business gets the fix, confidence in the whole program has already leaked out.
This isn’t bad luck. It’s the predictable consequence of running modern change through a testing model that hasn’t been modernized to match.
Most leaders recognize all four when they see them.
Most trading teams still run regression packs by hand. That was manageable when releases landed quarterly. It isn’t manageable now, with Artificial Intelligence (AI) accelerated development driving more change into the platform than ever.
When change volume rises and regression windows shrink, something has to give. In practice, it’s the go-live date.
In many organizations, regression knowledge lives in people’s heads, not in engineered test assets. A handful of long-serving testers know which paths matter, which data to use, which integrations misbehave, and what broke last time. These “human integrations” create fragility and inconsistency, especially when staff rotate or capacity is thin.
Upgrades demand repeatable accuracy, not retained memory. And when the people who hold the memory move on, the regression estate ages overnight.
Environment resets. Brittle interfaces. Flaky test data. Defects reopened, retested, reopened again. Every hour spent fighting the test infrastructure is an hour not spent proving the upgrade is safe.
Upgrades reveal how much of a testing team’s time is quietly absorbed by friction, not assurance.
The longer regression takes, the later issues surface, and the more rushed the final stages become. Fatigue, pressure and corner-cutting stack up right before go-live. The pattern is familiar: over-run, leakage, incident, eroded confidence.
Upgrades don’t create this risk. They expose it.
Manual regression isn’t slow because teams aren’t trying hard enough. It’s human-limited by design. It cannot expand to match AI-speed change.
The firms that upgrade smoothly aren’t working harder. They’ve replaced manual regression with engineered, deterministic, Application Programming Interface (API) level coverage that scales. Stable data. Owned environments. Focused coverage on the 10 to 15 business flows that matter most.
The result is upgrades that feel controlled, not heroic.
Our CEO, Chris Jones, has written the full argument: The AI acceleration paradox. Why testing must evolve at the speed of development.