The hidden cost of test maintenance. Did you budget for this?
Artificial Intelligence (AI) has changed how software is built. Code appears faster. Release cycles compress. Backlogs move. Most of our clients can point to specific features that landed in days rather than weeks.
What hasn’t changed, in most trading organisations, is the testing model.
Regression packs are still run by hand. Environments still drift. Test data still lives in a tester’s spreadsheet. And as development speeds up, the gap between what teams can build and what they can safely release grows wider, not narrower.
The pattern technology leaders recognise
Release dates slipping for reasons that aren’t the release. Regression windows expanding every quarter. Incidents in production that “should have been caught.” Engineering confidence quietly eroding. Business leaders asking, politely, why nothing is getting faster.
These aren’t isolated pains. They’re symptoms of the same underlying shift. Development velocity has scaled with AI. Testing throughput has stayed human.
Throughput is the word that matters. It isn’t a tooling problem, or an environment problem, or a requirements problem. It’s a capacity problem in disguise.
What the firms getting this right are doing differently
We work with some of the world’s leading energy trading organisations. The ones navigating AI-accelerated change with confidence aren’t the ones generating the most code. They’re the ones who have quietly done something most of their peers haven’t.
What that is, and what it looks like in practice, is the subject of our CEO Chris Jones’s Executive Insight: The AI acceleration paradox. Why testing must evolve at the speed of development.
"Because speed without assurance isn’t innovation. It’s just added risk, delivered faster."
