Paraphrasing William Gibson, the future of AI is here, but it's nowhere close to evenly distributed yet. This observation rings especially true when examining how enterprises are adopting artificial intelligence. While headlines often paint a picture of rapid, uniform transformation, the reality on the ground is far messier and more fragmented. The divide is not merely between companies that "get" AI and those that don't, but also within organizations themselves, where different teams and divisions can be operating at vastly different speeds and levels of sophistication.

The tale of two enterprises

Consider two recent conversations that illustrate this stark contrast. In one meeting, the head of engineering at a large hedge fund described teams with fleets of AI agents in full production. In his personal workflow, all code is written by large language models (LLMs). Interestingly, junior hires at this fund are not allowed to use LLMs for code assistance, a policy that reflects concerns about skill development and code quality. In another meeting, a data engineer at a large retail bank painted the opposite picture: no agents in production and sparse use of LLMs. While other parts of the bank may be moving faster on AI, his division clearly is not. These two examples are not outliers; they represent the broad spectrum of enterprise AI readiness.

This divergence is reinforced by data from major consulting firms. McKinsey's latest survey found that 88% of respondents say their organizations are using AI in at least one business function. However, only about one-third report that their companies have begun scaling AI programs. When it comes to agentic AI, the numbers are even more sobering: just 23% of organizations report scaling an agentic AI system somewhere in the enterprise, while 39% are still in the experimentation phase. In any given function, no more than 10% say they are scaling agents. Broad usage, in other words, is not the same thing as deep institutional change. There is still time to figure out AI; many teams are not behind.

Engineering job market defies doom predictions

One of the most persistent fears surrounding AI is that it will eliminate software engineering jobs. Yet the data tells a different story. Lenny Rachitsky recently highlighted that engineering job openings are at their highest levels in more than three years. According to TrueUp data, there were 67,665 open engineering jobs as of March 2026, up 78.2% from the recent low. Importantly, this growth is not concentrated at the senior level. TrueUp's breakdown shows 44.6% of posted engineering roles within tech companies are entry- and mid-level, compared to 38.3% at senior level and 13.8% at senior-plus. This suggests that companies still want a large number of engineers, even as AI tools become more prevalent.

Box CEO Aaron Levie invoked Jevons paradox to explain this phenomenon: when a capability becomes cheaper and easier to consume, demand for it often rises rather than falls. Cloud computing did not lead companies to need less compute; it made them build more things that consumed compute. Similarly, AI-assisted coding is likely to increase the demand for software engineers, not reduce it. Cheaper code production enables companies to tackle more ambitious projects, thereby increasing the overall need for engineering talent who can specify, review, steer, and orchestrate systems that generate code.

The role of governance and caution

In regulated industries such as banking, governance is a primary concern. Deloitte reports that only 21% of surveyed companies currently have a mature governance model for autonomous agents, and those 21% may be overly optimistic. Meanwhile, 73% cite data privacy and security as a top risk, and 46% cite governance capabilities and oversight. These are not bureaucratic hurdles for their own sake; they reflect the real challenge of integrating non-deterministic systems into deterministic, compliance-heavy environments. Every quarter a team spends in pilot mode is a quarter in which more aggressive peers are building operational muscle. However, caution is not without merit, especially when the consequences of a bad AI decision can be severe.

OpenAI's enterprise usage data highlights how uneven this muscle-building already is. Frontier workers, defined as the 95th percentile of adoption intensity, send six times more messages than the median worker. Frontier firms send twice as many messages per seat. OpenAI notes that the primary constraints are no longer model performance or tools, but rather organizational readiness and implementation. This aligns with anecdotal evidence: the real divide is between teams that have learned how to integrate AI into repeatable work and those still treating it as a promising but dangerous sideshow.

Redesigning workflows for AI

McKinsey's software development research found that the highest-performing AI-driven software organizations are seeing 16% to 30% improvements in productivity, customer experience, and time to market, along with 31% to 45% improvements in software quality. However, these gains do not come from simply sprinkling copilots over an unchanged process. They come from reworking roles, workflows, and the full product development system. That is a much harder organizational challenge than buying licenses for a coding assistant. Stack Overflow's 2025 survey found that 84% of developers are using or planning to use AI tools, and just over half of professional developers use them daily. Yet adoption alone does not guarantee results.

Task versus job is a critical distinction here. Writing a chunk of boilerplate code is a task. Engineering is a job. Jobs bundle judgment, trade-offs, accountability, architecture, security, integration, testing, and the reality of operating systems in the real world. AI can automate more tasks, but it has not eliminated the need for jobs, especially in environments where bad software decisions carry real operational or regulatory consequences. In fact, McKinsey's broader AI survey found that most organizations are still navigating the transition from experimentation to scaled deployment, and high performers stand out precisely because they redesign workflows and treat AI as a catalyst for innovation and growth, not just efficiency.

So no, AI is not plodding or rocketing toward one uniform enterprise future in which software engineers quietly fade away. Instead, AI is splitting enterprises into fast-learning and slow-learning teams, 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 is not the death of software engineering; it is the repricing of it, and every company and every team is paying different prices.

Source:InfoWorld News