Enterprise AI is maturing - will 2026 be the year it breaks free?

For the past few years, artificial intelligence has lived in a strange duality. On the surface, it looked magical - conversational systems, image generators, copilots that seem to reason. Underneath, enterprises have struggled with a more sober reality: hallucinations, brittle workflows, governance risks, and systems that work impressively in demos but falter at scale. In my recent conversation with Dwarak Rajagopal, Head of AI Research at Snowflake, what stood out was not optimism or scepticism, but maturity. AI, he suggested, is finally moving out of its adolescence. And the changes ahead, in 2026, will be less about bigger models and more about how intelligence is structured, verified, and distributed. One of the most consequential shifts underway is the quiet erosion of monopoly power in foundation models . For much of the current AI cycle, a handful of companies have dominated the narrative, capital, and capabilities around large models. But Mr. Rajagopal pointed to a critical inflection: the centre of gravity is moving from pre-training to post-training. The biggest breakthroughs are no longer coming from simply scaling parameters, but from how models are refined with specialized, high-quality data. This is opening the door for open source foundation models that can be customized, fine-tuned, and deployed for specific enterprise needs. Weakening grip of a few AI giants In 2026, this will fundamentally weaken the grip of a few AI giants and enable a more distributed innovation ecosystem where startups, researchers, and enterprises build on shared foundations rather than proprietary silos. This shift has deep implications for how intelligence is deployed inside organisations. Enterprises do not need general intelligence; they need contextual intelligence. They need systems that understand their data, their workflows, and their constraints. Open source models, combined with enterprise data platforms, make that possible. AI is no longer being treated as an oracle. It is being treated as infrastructure. The next major frontier, according to Mr. Rajagopal, will not be another leap in raw model capability but advances in agentic AI, driven by improvements in context windows and memory. Today’s models remain constrained by working memory. They are powerful in single interactions but fragile across long, multi-step tasks. In 2026, innovation will focus on giving agents persistent memory that will let them retain longer contexts, learn from prior actions, and operate across extended timelines. This will allow AI systems to move beyond reactive assistance and toward sustained problem-solving, supporting complex business processes rather than isolated queries. Automation won’t be that easy Yet autonomy brings risk. One of the biggest bottlenecks in deploying agentic systems today is error accumulation. Small mistakes compound across multi-step workflows, forcing enterprises to rely on human oversight as a safety net. Mr. Rajagopal’s view is that this dependency will not scale. The breakthrough will come from self-verification. Instead of inserting humans into every loop, AI systems will develop internal feedback mechanisms. They will be able to judge, validate, and correct their own outputs. These “auto-judging” agents will mark a shift from supervised automation to self-regulating systems, making...

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