Coforge has launched the AI Adoption Fabric, a framework designed to help enterprises scale agentic software development while addressing governance and change management challenges. The initiative targets common barriers organizations face when scaling AI, including governance structures, operating model redesign, and human-centric change management.
The framework combines three established change management methodologies. Kotter's Change Model provides organizational transformation guidance and executive alignment through a structured, step-by-step approach. Prosci's ADKAR Framework handles individual-level change enablement, covering Awareness, Desire, Knowledge, Ability, and Reinforcement. Operational Change Management focuses on process-level execution, specifically reshaping the software development lifecycle.
By combining these approaches, the framework addresses both strategic and operational dimensions of AI adoption. It targets common failure patterns, including developers reverting to outdated workflows and ineffective governance policies. It also provides a workforce readiness roadmap that redefines code review, testing, and quality control processes.
Enterprise-wide transformation, not a tech project
Anup Nair, Chief AI Commercial Officer at Coforge, said successful organizations treat agentic development as an enterprise-wide transformation initiative rather than a technology project. The fabric provides a structured approach to transforming delivery models, governance, and workforce behaviors, with the goal of translating AI investments into measurable business outcomes.
For IT and development teams, the framework signals a shift in how agentic AI projects get managed. The emphasis on preventing developers from slipping back into old workflows suggests organizations expect resistance to new processes, not just new tools. Professionals looking to prepare for these changes can explore an AI Learning Path for Software Developers, which covers practical applications relevant to reshaping development workflows.
Why this matters for IT and development professionals
Developers will likely see their roles change as agentic AI adoption scales. Code review, testing, and quality control are explicitly named as processes being redefined under this framework. That means the skills that matter in day-to-day development work are shifting. Teams that treat AI adoption as a change management problem, rather than a tool rollout, are the ones most likely to see their investments pay off. Staying current with AI for IT & Development resources can help professionals track these evolving expectations.
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