Three AI Trends to Watch in 2026

In 2026, the most important AI story is not simply that models are getting better. It is the transition to systems that can perform tasks, be deployed across organisations and be managed under real-world constraints on cost, energy, security and accountability.

1. AI agents move into operational work

AI is evolving beyond chat-based assistance and content generation. Agentic systems can use tools, connect with company data and workflows, and carry out multi-step tasks in areas such as software development, customer support, operations and data processing.

The real question for organisations is no longer whether to try agents, but whether they can make them dependable: with appropriate permissions, evaluation, human oversight and clear accountability. Deloitte reports that businesses are shifting from pilots towards broader deployment, although adoption remains uneven; 85% expect to customise agents for their own business needs.129

2. Reasoning, multimodal AI and AI for science accelerate

Frontier models are improving rapidly in complex reasoning, coding and multimodal tasks—work involving combinations of text, images and other formats. Stanford’s AI Index notes that several models now meet or exceed human baselines on PhD-level science questions, multimodal reasoning and competition mathematics, while results on the SWE-bench Verified coding benchmark rose from 60% to nearly 100% in one year.4

AI’s role in biology, chemistry, physics and astronomy is also expanding. That makes AI for Science, along with specialist models built for particular professional domains, a major area to watch for high-value applications.3

3. Inference costs, computing infrastructure and governance become competitive advantages

Once AI is used at scale, the cost of serving requests—known as inference—can matter more than the one-off cost of training a model. Stanford HAI says cumulative energy use for inference can exceed the energy required for training within months after deployment.1

At the same time, the most advanced models are becoming less transparent. Industry produced more than 90% of notable AI models in 2025, while important details—including training code, parameter counts, dataset sizes and training duration—are no longer disclosed for some of the most resource-intensive systems.2

For organisations, this raises the importance of chip and cloud capacity, model routing and cost optimisation, data sovereignty, security and compliance, as well as systems that can be audited. Governance is especially urgent for autonomous agents: Deloitte found that only one in five companies has a mature governance model for them.9

Sources

Stanford HAI, 2026 AI Index Report.1234
Deloitte, State of AI in the Enterprise 2026.912