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In recent years, debate over artificial intelligence has split between two extremes: claims that thinking machines are imminent, and assertions that AI is little more than hype. Both views are preoccupied with where AI might eventually end up, rather than with the more immediate questions of what today’s systems can already do, where they are beginning to fall short, and how much progress is realistically likely in the near term.
To understand why this moment matters, it helps to recognise what has changed. Until recently, software followed explicit rules and algorithms encoded directly into programs. Modern AI is different. Built on neural networks, it learns patterns from data rather than executing fixed instructions, making its behaviour powerful but neither fully predictable nor perfectly repeatable. This shift changes how such systems must be understood, tested, and ultimately used.
At its core, this article asks three related questions. Where does AI actually stand today, once we strip away both hype and dismissal? How far is it likely to advance through steady, incremental improvement rather than dramatic breakthroughs? And what kinds of technological disruptions would be required to change that trajectory in a meaningful way? Along the way, it also examines how these answers differ for individual users and for enterprises.
AI in 2026: The Individual View
At the start of the year, individuals already use AI in personal, low-risk situations where final judgment remains with them. These uses are not new breakthroughs; they are simply good enough that many people rely on them.
Over the course of the year, the change is less about what AI can do and more about how widely it is used. By year-end, tasks like drafting emails, summarising documents, translating text, and polishing writing feel routine rather than novel, handled by general-purpose tools whose capabilities have not dramatically changed.
The same pattern holds for learning and information work. AI shifts from an occasional helper to a regular part of how people clarify ideas, test understanding, and work across text, images, and audio. For individuals, this year marks not a leap in capability, but a quiet normalisation of use—where the real constraint is no longer access to AI, but how fully people choose to adopt what is already available.
AI in 2026: The Enterprise View
For enterprises, the starting point is different. A generic AI tool, no matter how capable, cannot simply be adapted to an organisation’s specific needs. Enterprises rely on shared knowledge, internal rules, permissions, and accountability that cannot be added through prompts alone.
This is where platforms like OpenAI come in—not as ready-made enterprise solutions, but as foundations. They provide powerful general models, yet these platforms are still maturing for widespread, low-friction enterprise adoption. Organisations must build the systems that connect models to company data, workflows, and controls, and that work remains complex, which is why enterprise adoption lags behind individual use.
By 2026, this gap may begin to narrow, not because the models suddenly become smarter, but because the surrounding tools and platforms mature. Even then, enterprises will not be “using ChatGPT at scale,” but carefully designed systems built on top of general-purpose models. That is why, for now, enterprises depend on two complementary approaches to make AI usable at work: systems that can act under supervision, and systems that can reliably ground AI in organisational knowledge.
AI Agents (The “Junior Intern”): Instead of just answering questions, agents are given tasks. For example, you don’t just ask how to write a report; you instruct the agent to find the files, analyze them, and produce a draft. Like a real intern, they perform useful work under supervision, acting as helpers rather than decision-makers.
RAG (The “Librarian”): Retrieval-Augmented Generation (RAG) forces the AI to check the organization’s own approved files before responding. Instead of guessing or “hallucinating”, the AI pulls information from specific policies or manuals. This makes the system less creative but far more reliable.
Together, these provide the two essentials for business: agents provide the ability to act, while RAG provides the grounding and trust. Without one, AI is limited; without the other, it is unsafe.
Where we are now
In areas such as software development and technical configuration, early AI agents are already being used inside enterprises. These agents can generate code, check configurations, and carry out limited sequences of tasks—but only within strict company systems, approval workflows, and guardrails. They operate as supervised executors, not independent actors.
When it comes to enterprise knowledge and day-to-day operations, RAG-based systems are already unavoidable. Organisations rely on retrieval and grounding because AI cannot safely internalise company-specific rules, documents, and decisions. Across functions such as customer support, system monitoring, and data cleanup, AI looks things up, prepares material, and speeds up workflows—but it does not make final decisions or carry accountability.
Where we are likely to be by the end of the year
By the end of the year, AI inside organisations becomes clearer rather than radically different. In structured areas like software development, AI agents move from experiments to reliable tools, handling larger pieces of work such as writing code, fixing bugs, and running tests.
Elsewhere, progress is steadier. Systems that make AI look up and use company information become more reliable, and everyday tasks like customer support, monitoring, and data handling get smoother—but remain tightly controlled.
What does not happen is a shift to fully autonomous AI. Instead, organizations settle into a stable pattern where AI handles the heavy lifting within clear limits, while humans provide the final judgment, authority, and accountability.
The “Black Swan” Disclaimer
All of this depends on an important assumption: that the underlying architecture of AI does not fundamentally change. The last major breakthrough in neural networks (the foundation for tools like ChatGPT) came in 2017, and progress since then has been incremental—driven by scale, refinement, and better engineering rather than a new kind of intelligence.
If that assumption breaks, the picture changes quickly. A new architecture, or a major shift in how models handle enterprise knowledge, could make generic platforms far easier to adapt and trust. That kind of breakthrough may come—but it has not yet arrived.
Until it does, the limiting factor for enterprise AI is not ambition or imagination, but architecture. Platforms like OpenAI may mature into true enterprise foundations over time, possibly beginning in 2026, but that transition depends less on promises and more on whether the technology itself evolves beyond its current constraints.
In that world, the real divide is no longer about access to AI, but about who learns to use it effectively—and who remains at the surface, or avoids it altogether.
These pieces are being published as they have been received – they have not been edited/fact-checked by ThePrint.