2025 was the year most companies moved past AI experiments. 2026 is the year they will be judged on results. We talked to more than 200 Arshan customers and reviewed thousands of production deployments to identify the trends that matter most.
The ten trends
01Agentic workflows go mainstream
AI agents that can plan, use tools and check their own work are moving from demos into day-to-day operations. Expect the first wave in finance operations, IT support and customer service, where the steps are well defined and the value is easy to measure.
02Multimodal AI becomes the default
Models that understand text, images, audio and video together are now cheap enough for everyday use. Insurance claims, field inspections and retail shelf audits are early winners.
03Small, specialised models win on cost
Not every task needs the largest model. Distilled models fine-tuned on a narrow domain often match accuracy at a fraction of the cost and latency, and can run on-premise.
04Retrieval grounds everything
Retrieval-augmented generation is becoming standard architecture. The differentiator is no longer the model but the quality of your knowledge base, permissions and citations.
“In 2026 the model is a commodity. Your data, your evaluations and your workflows are the moat.”Emily Carter, Head of AI Research at Arshan
05AI governance gets real
With the EU AI Act in force and similar rules elsewhere, model inventories, risk assessments and audit trails become board-level requirements.
06AI moves to the edge
On-device and on-premise inference grows for privacy, latency and cost reasons, especially in healthcare, manufacturing and the public sector.
07Synthetic data fills the gaps
Teams use generated data to test edge cases, protect privacy and train models where real examples are scarce.
08Evaluation becomes a discipline
Automated evaluation suites, red-teaming and continuous monitoring replace one-off accuracy checks before launch.
09Copilots for every role
Role-specific assistants for sales, legal, finance and HR spread quickly, built on shared company knowledge.
10ROI over experimentation
Budgets shift from pilots to programmes with clear owners, metrics and payback periods.
Key takeaways
- Start with agents for well-defined, high-volume processes.
- Invest in your knowledge base and permissions before you scale generative AI.
- Treat evaluation and governance as product features, not paperwork.
Emily Carter
Head of AI Research at Arshan. Emily leads applied research on evaluation and agent reliability, and previously worked on large-scale recommendation systems.
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