On 31 August 2026, Coforge launched its “AI Adoption Fabric” for agentic software development — focused not only on deploying AI tools, but on assessing workforce readiness, redesigning operating models, measuring adoption, and linking AI usage to measurable business outcomes.
Coforge is explicit: early productivity gains from AI coding tools often fail to translate into enterprise transformation, because organisations struggle with workflow change, governance, workforce behaviour and measuring adoption.
A second signal reinforces it: India’s IT-services providers are moving contracts away from hour/seat-based billing toward measurable outcomes and productivity, as customers expect AI to produce tangible efficiency gains.
The enterprise problem is moving from “give employees AI tools” to “can we prove employees are actually capable of working effectively with AI?”
That elevates AI Workforce Readiness + Adoption Measurement to a first-class proposition — measuring Role → workflow → AI-assisted task → demonstrated performance → evidence → proficiency → adoption → productivity improvement → re-verification. For software teams: agent capability across requirements → coding → testing → code review → debugging → documentation → quality controls.
What this points to: a management layer that answers — who is genuinely AI-ready, which teams use AI only superficially, where productivity is actually improving, which workflows to redesign, who needs upskilling, and whether training produced measurable improvement. A workforce-transformation measurement layer, not just an assessment tool.