As AI subsumes IT, the nature of work and society will change, as it has in previous waves of technological change, from the invention of agriculture, to the mechanization of physical labor, to the rise of the internet.

The mix of jobs undertaken in the economy will have begun a transformation by this point, with associated disruption of social orders and an evolution of public institutions, as in previous waves of technological change. AI will be an inextricable part of almost every occupation, just as the internet and telecommunications today are a part of practically every job, but human workers will remain as important as ever, despite futuristic predictions of singularity and the full replacement of human labor. Ubiquitous physical robots, perennially "just around the corner," will gradually become more common and cost effective beyond the niches they occupy today, though their development cycles will remain sluggish relative to the progress of AI.

Significant fractions of business operations will be automated (or at least, automatable), though legacy systems will remain difficult to fully shake free of, and availability of energy and natural resources will serve as a brake on an otherwise brisk technological acceleration (barring a major energy breakthrough, which seems unlikely).

AI tools will increasingly become a part of scientific discovery, accelerating progress in many fields, but that progress will disappoint those holding more exuberant techno-utopian visions. Science and invention will remain slow, methodical endeavors, albeit empowered with new tools, as in past waves of innovation.

As physical AI or robotics emerge, become more prevalent, and interact with existing enterprise systems depending on their specialty, there will be platforms to observe and govern them, integrating across multiple physical AI vendors.

As AI becomes increasingly capable of tackling tasks that previously resisted automation due to inherent complexities, it will open the door to fully autonomous data systems. That is, multi-agent systems will build entire, self-sufficient data stacks from scratch, ranging from data infrastructure design, to data discovery, integration, governance, and even proactively pinpointing relevant insights. Towards this goal, human interventions, both at design time and ongoing operations, will focus on clarification of goals, approval for state-changing actions, and receiving results.