If Your Engineers Only Get Thirty Minutes to Learn, That Is Not Their Failure. It Is Yours.
You called to talk about AI adoption. But you do not know your value streams, your engineers get thirty minutes a week to learn, and you are still defending manual QA that produces no ROI. That is not an adoption…
Organizational design and incentive structures dictate the pace and direction of technological adoption.
Invest in capability, not just tooling.
Capability development requires dedicated time and resources, not marginal allocations, to integrate new paradigms into an organization's operating model.
Value stream mapping identifies and quantifies organizational waste, exposing the true cost of delays and inefficient handoffs that dilute the impact of new tools.
Unmeasured processes, defended by instinct rather than data, absorb resources without demonstrating proportional value and impede the adoption of more effective approaches.
Current process frameworks, optimized for past constraints, often become an impediment when new technologies shift the fundamental bottlenecks of software development.
Incentives rooted in existing delivery metrics (e.g., velocity targets) can disincentivize the learning and experimentation necessary for true transformation, rewarding stasis over strategic change.
The choice is to either lubricate the existing system or build a fundamentally new one.
Organizational design and incentive structures dictate the pace and direction of technological adoption.
Invest in capability, not just tooling.
Capability development requires dedicated time and resources, not marginal allocations, to integrate new paradigms into an organization's operating model.
Value stream mapping identifies and quantifies organizational waste, exposing the true cost of delays and inefficient handoffs that dilute the impact of new tools.
Unmeasured processes, defended by instinct rather than data, absorb resources without demonstrating proportional value and impede the adoption of more effective approaches.
Current process frameworks, optimized for past constraints, often become an impediment when new technologies shift the fundamental bottlenecks of software development.
Incentives rooted in existing delivery metrics (e.g., velocity targets) can disincentivize the learning and experimentation necessary for true transformation, rewarding stasis over strategic change.
The choice is to either lubricate the existing system or build a fundamentally new one.
After 20 years in software development, Norman is both a hands-on leader and defining the new age of AI SDLC for some of the biggest brands in the world — and exploring it with the builders. He writes here about things he is hearing and seeing. All posts are his personal points of view and do not reflect any employer or any customer he has ever had contact with.
The views and opinions expressed in this article are the author’s own and do not represent the positions of any employer, client, or affiliated organization.
