AI-native workforce: Future of work and skills in engineering and product value chain
February 2026
Generative AI has moved from assistants to agentic collaborators—reshaping how software is built and how product bets are made. Our research shows 30–35% productivity gains across the Software Development Life Cycle (SDLC) with the largest lift in coding, review, and testing, while new product development (NPD) cycles can compress by up to 50%.
Meanwhile, value creation shifts from writing code to orchestrating human‑agent workflows and making better design and risk trade‑offs. Product management follows suit: from coordination to a “mini‑CEO” role, with AI synthesizing insights so leaders can focus on strategy, stewardship, and speed. Realizing the ~US$7T opportunity demands more than tools—it requires workflow redesign, upskilling, and new governance. Our R.E.A.D.Y. framework shows how to move from pilots to persistent impact.
What is changing - Fast
Organisations must be READY to embrace this change
To capitalise on the ~US$7 trillion opportunity supported by advances in GenAI, natural language processing and automation,organisations must move beyond piecemeal tool adoption. The following recommendations utilise the R.E.A.D.Y. framework toguide the transformation.
Conclusion:
The future software organisations will look fundamentally different from the past. Historically, much of the engineering effort was spent on manual, repetitive activities, such as writing boilerplate code, conducting routine tests and managing low value execution work. With the emergence of AI agents, a sizeable portion of this effort is now automated and accelerated.
In this new model, competitive advantage is no longer defined by how quickly teams can produce code, but by their ability to envision the proper outcomes, make informed trade-offs and consistently deliver high-quality, business-relevant solutions at scale.
Nitin Razdan
