- Traditional AI remains the backbone of production
Traditional AI will continue to underpin industrial operations in 2026, powering core systems across manufacturing, energy and logistics. Even as generative AI expands, expect rules‑based and machine‑learning models to still handle the precise, data‑led tasks that keep production stable and efficient.
This resilience comes down to structured, cleansed datasets. Traditional AI reduces bias and ensures more predictable performance, making it key for industries that require consistency and reliability.
- Generative AI scales rapidly across enterprise workflows
Generative AI is now deeply embedded in enterprise back‑office functions, accelerating content creation, analytics and coding at scale. While customer‑facing and revenue‑generating applications are emerging, they remain early‑stage in comparison.
However, this rapid expansion can surface a range of risks. Hallucinations, copyright and accidental data leaks are real threats, making enterprise guardrails and employee training vital for safe, efficient use.
- Agentic AI moves into early but meaningful experimentation
Agentic AI is beginning to take on multi‑step tasks, workflow automation and decision support with limited human oversight. Early applications are emerging across industries – including insurance, where it can support claims handling, fraud analysis and operational triage.
As agentic AI soars in 2026, expect to see a range of risks from loss of control and agent hijacking to reduced visibility into decision paths. For businesses, it’s about putting protocols in place to safeguard users without limiting innovation.
4. Bias risk intensifies as AI systems take on more decisions
As generative and agentic AI interpret vast, unstructured and often imperfect datasets, they become more prone to producing skewed or harmful outputs. This bias is harder to spot because results appear authoritative and, in some cases, may trigger autonomous actions.
Bias has long been a known issue, but AI’s rapid scalability makes it more critical than ever. A single failure can undermine a model’s value in legal or financial analysis, lead to discriminatory outcomes, damage vendor and user reputations – and in industries such as healthcare, the consequences can be life threatening.
5. Governance and AI assurance becomes essential, not optional
Gaining an edge with AI is one thing; proving you’re using it safely and compliantly is another. As hallucinations, opaque decision making and inconsistent employee practices persist, the need for stronger governance, clearer policies and targeted training is growing.
Regulators and stakeholders now expect transparent data controls, human in the loop checkpoints and permission based systems – and increasingly, data sovereignty safeguards on where and how data is stored and processed. Meanwhile, insurers are looking for these same signals as evidence that AI risk is being taken seriously.
