Paraphrasing William Gibson, the future of AI is here, but it’s nowhere close to evenly distributed yet. The starkly uneven reality of enterprise AI adoption is becoming a defining feature of the technology landscape. Recent conversations with leaders in London highlight this gap: one engineering head at a large hedge fund described having fleets of agents in full production, with all code written by LLMs. In contrast, a data engineer at a large retail bank reported no agents and sparse use of LLMs, suggesting that even within the same company, adoption curves vary wildly.

This isn’t about one company “getting” AI and the other not. Rather, it’s a reminder that even within the same company there are wildly divergent adoption curves for new technologies. AI is widening the gap between teams that can absorb it operationally and teams that can’t. That’s what the best recent data suggests. McKinsey found that 88% of respondents say their organizations are using AI in at least one business function, but only about one-third say their companies have begun scaling AI programs. As for agents, 23% report scaling an agentic AI system somewhere in the enterprise, while 39% are still just experimenting. And in any given function, no more than 10% say they’re scaling agents.

Broad usage, in other words, is not the same thing as deep institutional change. In short, there’s still time to figure out AI. You’re not behind. The gap between broad usage and deep institutional change is significant. Many organizations are still navigating the transition from experimentation to scaled deployment. Deloitte’s 2026 enterprise AI research reinforces this, showing only 25% of respondents have moved 40% or more of their AI pilots into production. Just 34% say they’re using AI to deeply transform their businesses, while 37% are still using it at a surface level with little or no change to core processes.

Cue the engineering boom

Contrary to fears of job losses, engineering openings are at a three-year high. TrueUp data shows 67,665 open engineering jobs as of March 2026, up 78.2% from a recent low. 44.6% of posted engineering roles are entry and mid-level. This indicates that AI is not eliminating jobs but changing what companies want from engineers. Stack Overflow’s 2025 survey found 84% of respondents are using or planning to use AI tools in development, with over half using them daily. McKinsey’s software development research found that high-performing AI-driven software organizations see 16-30% improvements in productivity, customer experience, and time to market, along with 31-45% improvements in software quality.

The hedge fund leader’s description offers a glimpse into the future: less time hand-authoring code, more time specifying, reviewing, steering, and orchestrating systems that generate code. However, in heavily regulated environments like retail banking, governance is the hard part. Deloitte reports that only 21% of surveyed companies currently have a mature governance model for autonomous agents. 73% cite data privacy and security as a top risk, and 46% cite governance capabilities and oversight. This isn’t bureaucracy for its own sake; it’s a recognition that plugging non-deterministic systems into deterministic, compliance-heavy environments gets messy fast.

Software engineering is alive and well

The distinction between task and job matters. Writing boilerplate code is a task; engineering is a job that bundles judgment, trade-offs, accountability, architecture, security, integration, testing, and real-world operations. AI can automate more tasks, but it hasn’t eliminated the need for jobs, especially in environments where bad software decisions have operational or regulatory consequences. McKinsey’s broader AI survey found that high performers redesign workflows and treat AI as a catalyst for innovation and growth, not just efficiency.

OpenAI’s enterprise usage data shows that frontier workers (95th percentile of adoption intensity) send six times more messages than the median worker. Frontier firms send twice as many messages per seat. The primary constraints are organizational readiness and implementation, not model performance or tools. The real divide is increasingly between teams that have learned how to integrate AI into repeatable work and those that are still treating it as a promising but dangerous sideshow.

So no, AI isn’t plodding toward one uniform enterprise future in which software engineers fade away. Instead, AI is splitting enterprises into fast-learning and slow-learning teams. It is rewarding organizations that redesign work, govern risk, and turn lower software costs into more software, not less. The code may be getting cheaper, but the ability to decide what should be built, how it should fit together, and how to keep it from breaking the business continues to increase in value. That’s not the death of software engineering; it’s the repricing of it, and every company and every team is paying different prices.

Source: InfoWorld News