Paraphrasing William Gibson, the future of artificial intelligence is already here, but it is far from evenly distributed across the enterprise landscape. Recent conversations in London with two contrasting organizations vividly illustrate this point. At a large hedge fund, the head of engineering described teams operating multiple AI agents in full production, with senior engineers relying on large language models (LLMs) for almost all code generation—though junior hires are prohibited from using them to assist with code. In stark contrast, a data engineer at a major retail bank reported no use of agents and only sporadic adoption of LLMs within his division. This divergence is not about one company embracing AI while another resists; rather, it shows that even within a single organization, different teams can have wildly different adoption curves.

This unevenness is supported by recent data. McKinsey's global survey found that 88% of respondents say their organizations use AI in at least one business function, but only about one-third have begun to scale AI programs across the enterprise. When it comes to autonomous agents, only 23% report scaling an agentic AI system somewhere in the organization, while 39% are still experimenting. And within any given function, no more than 10% of teams are scaling agents. Broad usage, in other words, is not the same as deep institutional change. There is still time to figure out AI strategies; no organization is irredeemably behind.

Not All Financial Firms Are Cautious

Common stereotypes suggest that finance or regulated industries are lagging in AI adoption. However, the reality is more nuanced. Some financial firms are moving aggressively, while others remain cautious—even within the same company, different teams operate at different speeds. Deloitte's 2026 enterprise AI research reinforces this picture. Only 25% of respondents said they had moved 40% or more of their AI pilots into production. Just 34% claim they are using AI to deeply transform their businesses—a figure that likely reflects aspirations more than reality—while 37% are still using it at a surface level with little change to core processes. This is less a tidal wave and more a messy, uneven organizational test.

This unevenness also explains why fears that AI will wipe out software jobs are overblown. The interesting aspect of AI coding tools is not that they make software cheaper to produce, but what companies do with that lower cost. Box CEO Aaron Levie invoked Jevons paradox to explain that when a capability becomes cheaper and easier to consume, demand for it often rises. Cloud computing did not lead to less compute; it led to more. AI-assisted coding may do the same for software itself. Indeed, engineering job openings are at their highest levels in more than three years. Data from TrueUp shows 67,665 open engineering jobs as of March 2026, up 78.2% from the recent low. More importantly, 44.6% of these roles are entry and mid-level, versus 38.3% senior and 13.8% senior-plus. AI is not eliminating roles for junior developers; it is changing what enterprises want from engineers.

Software Engineering Is Alive and Well

The hedge fund leader offers a glimpse of where parts of enterprise engineering are headed: less time hand-authoring code, more time specifying, reviewing, steering, and orchestrating systems that generate code. But the retail bank division is not irrationally lagging; in heavily regulated environments, governance is the hard part. Deloitte reports that only 21% of companies have a mature governance model for autonomous agents, and 73% cite data privacy and security as top risks. This is not bureaucracy for its own sake; it is a recognition that plugging non-deterministic AI systems into deterministic, compliance-heavy environments creates complexity. Caution, however, is not free. Every quarter spent in pilot mode is a quarter in which more aggressive peers build operational muscle. OpenAI's enterprise usage data reveals that frontier workers send six times more messages than the median worker, and frontier firms send twice as many messages per seat. The primary constraints are no longer model performance but organizational readiness and implementation.

This rings true: the real divide is increasingly between teams that have learned to integrate AI into repeatable work and teams that treat it as a promising but dangerous sideshow. The distinction between task and job is critical. Writing 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 operational or regulatory consequences. McKinsey's research shows that high-performing organizations stand out because they redesign workflows and treat AI as a catalyst for innovation and growth, not just efficiency. That is very different from giving everyone a chatbot and expecting to need fewer people.

So no, AI is not leading to a uniform enterprise future in which software engineers quietly fade away. Instead, it is splitting enterprises into fast-learning and slow-learning teams. It rewards 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 fits 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