As generative AI moves past its infancy, 2026 is shaping up as the year the technology works through the growing pains that will define it for years to come — with agentic AI dominating industry attention and enterprises focusing on how to orchestrate agents productively. Over the last three years, generative AI has evolved from a scramble of vendors and experiments into a standard layer of software platforms, with most major technology vendors now offering agentic capabilities or copilots. The early period brought a flood of large language models and an assumption that bigger parameters and context windows meant better results; the market then shifted toward smaller and more efficient models — including prominent open-weight releases from Chinese vendors such as DeepSeek and Alibaba — and, more recently, toward reasoning models and a decisive pivot from models to agents, alongside renewed emphasis on data center capacity.
This analysis draws on predictions gathered from industry analysts in AI Business’s “Navigating the Next Phase of GenAI” coverage.
Continued acceleration — and the bubble question
The rapid advances seen every year since OpenAI released ChatGPT in November 2022 are expected to continue into 2026. At the same time, many inside and outside the industry are watching whether the AI market is in a financial bubble and, if so, whether it will deflate or burst. Futurum Group analyst Brad Shimmin characterizes 2026 as a year of acceleration — not in investment, which has already surged, but in optimizing data spending in service of AI. Decision-makers, in his view, will need to choose what to fund and what to cut, and outcomes range from a sharp correction, to a gradual shrinking back to normal, to sustained growth grounded in real capacity. The uncertainty reflects how young the technology remains: even practitioners are still learning what these systems actually do and how they work.
Counting costs
With most enterprises still in the experimentation phase, 2026 could be the year of the push to “get it right,” according to Mark Beccue, an analyst at Omdia (a division of Informa TechTarget). A central challenge is understanding risk and ensuring AI tools genuinely address a specific problem. Cost is the other lever: pressure is building across the industry to bring down the high costs associated with AI technology — data centers in particular — because if the cost per use does not fall, enterprises may simply use the technology less, feeding the bubble scenario.
Better agentic AI, better data — and shadow agents
2026 is also expected to bring more capable multimodal models that better support AI agents and enable broader agent orchestration. Getting multi-vendor agent ecosystems to cooperate predictably and productively has been difficult, notes Forrester Research analyst William McKeon-White, and progress on interoperability should make orchestration more successful — though it will remain a process of growth and learning, and not every platform will prove usable.
Organizations will also need to build data environments that are more secure, with better permission systems, optimized for their intended use, so agents and tools behave as intended. Without a healthy data environment, improvements from model makers will only go so far — and governance needs to mature as the checks and balances that keep models working correctly. Practical reasons agent projects stall are examined in this look at AI agent governance, and coordination patterns in this guide to multi-agent orchestration.
A related concern is the rise of shadow agents — agents employees use without organizational approval, according to Suja Viswesan, vice president of products at IBM. Enterprises need an inventory of what is running, lifecycle metrics, and governance that can stop an unapproved agent when it tries to access something it should not. Visibility across all running agents and applications, including knowing when an agent’s lifecycle has ended, becomes an enterprise responsibility.
Sovereign AI
Beyond agentic innovation, 2026 is expected to bring continued growth in sovereign AI — the idea of a nation controlling its own AI technology, including infrastructure and software, to serve national interests. Beccue expects popularity and adoption to increase in the UK, EU countries, and India, displacing some US- and China-based vendors — though not in chips, where manufacturing capacity remains concentrated. The sovereign movement is likely to matter most for multimodal models rooted in local languages: models built for languages other than English give regional vendors a better chance to succeed and give enterprises in those regions access to models tailored to their markets, driving more AI initiatives into production. India’s market, in this view, is large enough to compete with China and the US if sovereign AI policy can energize its domestic ecosystem.
More industries feel the impact
Retail stands out among verticals. During the most recent holiday season, more shoppers consulted AI chatbots before making purchases, and that behavior is expected to accelerate as buyers fold AI into research and discovery, according to Greg Zakowicz, e-commerce consultant at marketing automation platform Omnisend. Beyond assistant platforms such as ChatGPT driving traffic to retail sites, the next opportunity is on-site AI chatbots — such as Walmart’s Sparky and Amazon’s Rufus — which offer a natural handoff from a general AI platform to the on-site experience and help buyers refine searches. By the end of the year, shoppers may increasingly complete purchases directly through AI platforms such as ChatGPT or Perplexity.
Media is the other industry to watch. While some fear AI will eliminate media jobs, the technology is also solving unglamorous problems: Paul Pastor, co-founder and chief business officer of streaming-technology company Quickplay, argues AI will not replace creativity but will address the industry’s metadata challenges. Studios already use generative AI to dub long-form content and create clips; the next step is making those tools work together across recommendation engines, ad tech, content management, and analytics — with AI acting as connective tissue between disparate systems, extracting more value from existing assets and better predicting what viewers want.
Limitations and what to watch
- These are analyst predictions, not measured outcomes; forecasts about the AI market — especially bubble timing — have a poor track record in both directions.
- Analyst remarks are paraphrased from press coverage; original phrasing and full context are in the linked source.
- Agentic interoperability standards are still forming, and vendor claims about orchestration maturity should be validated in pilots.
- Sovereign AI momentum depends heavily on regulation and funding decisions that can shift within a single budget cycle.
The bottom line
With so much innovation expected in 2026, enterprises may feel overwhelmed by the pace of technology. The steadier view, per Shimmin: the upside of AI adoption has been demonstrated and is solid — what remains is a matter of time, investment, and effort to maintain that value and grow it in directions not yet imagined.
