MY POINT OF VIEW
AI is a multiplier
In strong systems, AI accelerates value, learning, and delivery. In weak systems, it accelerates defects, risk, and fragility.
Enterprise AI adoption must start with literacy, strong DevOps, clear guardrails, a full value stream mindset, and human accountability. This is a system conversation, not a tooling conversation.
Where I stand
In late 2024 and throughout 2025, much of the industry conversation around AI focused on adoption rates, usage metrics, and individual productivity gains. My focus was elsewhere: on the delivery system itself, how work flows from idea to production, how teams maintain operational resilience, and whether organizations were building the discipline to safely absorb acceleration.
That perspective is gaining traction now. More leaders are recognizing that AI adoption is a system conversation. I’ve been making that case since the beginning.
The one-minute version
AI is reshaping how code is written and how technology organizations think about the full system that delivers software.
To truly leverage what AI can do, organizations have to get exceptionally good at DevOps so code can move from idea to production quickly, reliably, consistently, and securely.
AI reveals the truth about your delivery system.
If organizations apply AI only to code generation while ignoring the surrounding system, they risk achieving local gains while causing broader dysfunction.
Full perspective
AI adoption starts with literacy
The question for most companies is no longer whether AI matters. It does. The question is how to begin adopting it practically and responsibly.
That starts with exposure, hands-on use, and shared learning. Teams need time to understand the tools, compare options, and build judgment through real experience.
Teams build AI judgment through real experience and shared learning.
But fluency alone is not enough. Without engineering depth, judgment, and systems thinking, AI adoption gets shallow fast. Organizations still need strong fundamentals to make AI durable at enterprise scale.
Leadership still matters
The old model, which overvalued the technical leader who could personally grind out the most code, was already limited. In an AI-enabled environment, it becomes even less useful.
What matters more now is the ability to build strong teams, create repeatable systems, develop experienced people, and lead through change.
Experienced engineers become force multipliers when paired with AI. Their value is in what they can shape, guide, review, and orchestrate across increasingly AI-enabled workflows.
Humans remain accountable for what AI helps create.
The leaders who create the most value with AI will be the ones who understand people, systems, flow, and change.
AI across the value stream
I say this not only as a technology leader, but as a technologist and active user of AI. I use it. I see how it can accelerate thinking, improve analysis, and increase the leverage of experienced professionals.
But I also see the temptation to confuse more activity with more value. More AI-generated work does not automatically improve outcomes. Without strong systems and disciplined ways of working, AI can create the appearance of progress while increasing waste and risk underneath.
Guardrails are the structure that enables speed.
AI is a value stream conversation. If enterprises want the full benefit, they have to think beyond build speed and look at the full path from idea to outcome.
AI, operations, and the human loop
Much of today’s AI discussion centers on whether engineers will be replaced or teams will become much smaller. Some of that may happen, especially if organizations optimize the system that moves software from idea to production.
But software delivery does not end at production. When the enterprise system fails, who gets the call? Who understands the architecture, the dependencies, and the safest path to recovery?
AI may increasingly help diagnose incidents and accelerate response. But at an enterprise level, operating and maintaining production systems still requires human judgment, accountability, and deep contextual knowledge.
Speed without operational resilience is just risk in a different form.
Software does not stop at deployment. Enterprise responsibility includes operating, maintaining, restoring, and improving systems in production.
How I approach AI adoption
My approach is practical:
- Define risk boundaries: Know what data can be used, what cannot leave the environment, and what requires review.
- Choose tools with intent: Different use cases require different tools, trust levels, and constraints.
- Build literacy through real use: Hands-on learning, shared practices, and room for experimentation.
- Integrate into the delivery system: Better requirements, stronger tests, safer reviews, faster debugging.
- Add evaluation workflows: Checks for correctness, security, regressions, and operational impact.
- Measure the system: System-level outcomes matter more than individual output metrics.
The real question
I am optimistic about AI. I have been using it since February 2023 and have seen significant gains in the recent wave of frontier models and agents. I can already see how it is reshaping how modern organizations work.
But I am also practical. AI will not rescue a broken operating model. It will reveal it. The organizations that benefit most will be the ones with the strongest systems, clearest priorities, healthiest teams, and most disciplined path from idea to outcome.
Is your system strong enough for AI to make you better, or will it simply make your existing problems move faster?
Let’s talk
If you’re navigating AI adoption at the enterprise level, I’m happy to compare notes.