Tired of hearing “AI is eating software”? What’s really happening in large, Engg Org’s? Not hype, but change that’s reshaping engineering and business.

Lets connect back first. Pivotal shifts have happened in past too. Nearest one’s being DevOps and Data Warehouse.

DevOps

Pre-DevOps, software design and delivery was optimized for control, predictability. Changes were expensive. Environments were scarce and sacred. Structurally, systems were large, tightly coupled. Interestingly, there were fewer options for everything.

With DevOps, we shifted optimization towards continuity (flow) and learning. Change was continuous. Environments were cheap and reproducible. Systems became smaller, modular, observable. There were more options – specialized products or features.

DevOps lent a philosophical change, not just tooling. It wasn’t about pipelines or containers or branches. It reduced delivery friction. It led to a belief that software systems are never finished – just continuously re-negotiated for priority and needs.

Data Warehouse

This one is tough to land - especially if you are a data lake person. Be with me though please…

Pre Data Warehouse, it was what I call as princely-state-hood. Each system had its own truth. Data supported apps, teams, but not business. Decisions were intuition heavy and data was inconsistency heavy.

Data Warehouse brought some structure to this chaos. It wasn’t a mere storage upgrade. It shifted data systems from system of record to system of insight. But it still left gaps. Businesses felt the need for scale, variety and velocity – which led us to Data Lakes. Data Lake addressed raw-first and rapid experimentation needs, but reality is business kept going back to DW for consistency and structure.

History of progression (not replacement)

DevOps and Data Warehouse both moved businesses forward. Most critically, they represented a progression, not replacement. Businesses and Solutions endured some structural changes. But in most cases, it wasn’t a knockdown rebuild. It was either a recognizable (new) build on top of good bones, or an extension (small or large).

AI as Adoption Multiplier

I see AI as an Adoption Multiplier – not as the one eating or replacing software. Thinking in terms of systems and cloud (not productivity suites, pure play embedded/AI), it doesn’t add a new layer - but rearranges where value lives.

From an engineering perspective, a big shift is that control logic is seen moving out of static middleware and into probabilistic AI reasoning. Agents (built or sourced) decide which APIs to call, how to sequence workflows, how to adapt schemas, and how to recover from errors — dynamically. That’s not a new tool; that’s a new control plane.

From an architecture standpoint, foundations like integration, networking, and other pre-existing infrastructure is still needed. They still execute the real work. APIs are still called. Messages still flow. Data still moves.

It’s critical to recognize that change is where intelligence sits now.

Execution layers become thinner, interchangeable, and more price or personalization sensitive. Intelligence concentrates around AI orchestration, data platforms, and event-driven runtimes. In practice, I expect this to show up as faster growth in AI, data, serverless, and security capabilities – while classic integration and networking remain relevant - perhaps increasingly more commoditized.

From an engineering leadership view, this forces hard decisions:

• Do we want to own the decision-making, or just the plumbing?
• Do we want to optimize deterministic control, or adaptive reasoning?
• Are we measuring value by system complexity, or by business outcomes delivered per unit of compute or infra? And does it matter if we own that compute or infra?

And from an economic perspective, this is the real shift: Value moves towards intelligence. The takeaway isn’t “everything becomes AI.” It’s that, going forward, true architecture value (and cost) is around intelligence, not integration.

Engineering teams that understand this early will design systems that age well, are good value (for business) and truly AI-enabled.