The layer ordering resonated, but in retail deployments I've seen it flip.
Governance and ROI doesn't come last - finance is asking for the business case before the orchestration is even stable.
Most retail AI projects I've been part of didn't collapse because the system failed technically. They stalled because nobody agreed on what "measurable outcome" meant before the build started and the stack is real.
The sequence in which it catches you is different in enterprise.
This is one of the most important distinctions nobody talks about clearly enough, the difference between how the stack is architected and the order in which it catches you.
You're right. In enterprise, the value layer isn't a final step. It's a precondition. Finance asks for the ROI model before orchestration is stable, sometimes before the foundational layer is even clean.
What I'd add though and this might be where we agree more than it looks, is that the layer ordering describes technical dependency, not organisational sequence. You can't have reliable observability without stable deployment. You can't have stable deployment without sound orchestration. The stack flows bottom up technically even when the business pressure flows top down.
The real trap in enterprise isn't ignoring the value layer. It's trying to define measurable outcomes before the system is stable enough to measure anything meaningfully.
That's a whole issue on its own. Thank you for sharing your perspective.
Why do you think there's been no measurable ROI for companies using genAI yet? What do you think of the studies that show personal productivity increases are negligible and don't translate to organizational success?
The layer ordering resonated, but in retail deployments I've seen it flip.
Governance and ROI doesn't come last - finance is asking for the business case before the orchestration is even stable.
Most retail AI projects I've been part of didn't collapse because the system failed technically. They stalled because nobody agreed on what "measurable outcome" meant before the build started and the stack is real.
The sequence in which it catches you is different in enterprise.
This is one of the most important distinctions nobody talks about clearly enough, the difference between how the stack is architected and the order in which it catches you.
You're right. In enterprise, the value layer isn't a final step. It's a precondition. Finance asks for the ROI model before orchestration is stable, sometimes before the foundational layer is even clean.
What I'd add though and this might be where we agree more than it looks, is that the layer ordering describes technical dependency, not organisational sequence. You can't have reliable observability without stable deployment. You can't have stable deployment without sound orchestration. The stack flows bottom up technically even when the business pressure flows top down.
The real trap in enterprise isn't ignoring the value layer. It's trying to define measurable outcomes before the system is stable enough to measure anything meaningfully.
That's a whole issue on its own. Thank you for sharing your perspective.
Why do you think there's been no measurable ROI for companies using genAI yet? What do you think of the studies that show personal productivity increases are negligible and don't translate to organizational success?
The lack of measurable ROI is often structural rather than technical.
AI can increase task throughput, but that does not automatically produce organizational ROI.
If workflows, coordination structures, and incentives remain unchanged, efficiency gains are absorbed rather than compounded.
Tool-level productivity is not the same as system-level impact.