Agentic AI in 2026: Moving Beyond Pilots to Business Value

Agentic AI was one of the defining technology stories of 2025, and its influence will continue well into 2026. After a year of experimentation, business leaders are now looking for practical results, not just promising pilots.

The latest UiPath report highlights several trends that help explain how organisations are thinking about agentic AI in the year ahead. At Enterprise RPA, we have been closely involved in AI and automation for many years, and what we see in the report closely mirrors what we experience in practice: strong interest in agentic AI, alongside a growing need to translate that interest into real business value.

Agentic AI reshapes how organisations work

Agentic AI is not something that can simply be added to existing systems. It changes how work flows across the business and how decisions are made. This is why many organisations are beginning to rethink their operating models, placing AI agents at the centre of how work is coordinated and delivered.

This shift is already well underway. According to UiPath, 78 percent of C-suite executives agree that gaining the full benefit of agentic AI requires a new operating model built around agentic capabilities. In 2025, around one in five enterprises had already started rethinking their operating models around an agentic core, and many more are expected to follow in 2026.

Ready-made solutions are becoming the preferred route

As the scale of investment grows, so does the need to reduce risk. Many organisations are choosing specialist, industry-focused agentic solutions rather than trying to build everything themselves.

These vertical solutions gained momentum in 2025 and are expected to become a core part of enterprise AI strategies in 2026.

MIT research cited in the report shows that externally delivered or partner-based AI projects are twice as likely to achieve meaningful outcomes as those built entirely in-house.

By reducing build time, cost and performance risk, prebuilt solutions offer a faster and more reliable path to value.

Good data remains the foundation of success

Even the most advanced AI agents depend on the quality of the data they use. As agentic systems become more widespread, organisations are paying closer attention to how their data is collected, structured and governed.

Leaders increasingly see data quality as one of the biggest barriers to scaling agentic AI. Those who invest in trusted, well-managed and context-rich data will give their AI agents the clarity they need to operate accurately and with confidence. Those who do not will struggle to move beyond small gains.

Trust, security and governance are moving centre stage

To use AI agents at scale, businesses must be able to trust them. That means ensuring security, transparency and control at every stage.

Almost all IT and security leaders now see AI agents as a growing risk, yet fewer than half have formal governance policies in place. One solution is to build rules, permissions and approval processes directly into AI systems, so that agents are designed to act safely and responsibly from the start.

A stronger focus on business value

The past year showed just how quickly interest in agentic AI has grown, but it also revealed how difficult it can be to turn experiments into enterprise-wide success. Boards and executive teams now want to see clear strategies and measurable returns.

In 2026, the organisations that succeed will be those that can show how agentic AI improves efficiency, strengthens resilience and delivers real business outcomes.

Turning insight into action

At Enterprise RPA, we work with organisations to identify where AI agents can make the biggest difference. By combining industry insight with hands-on experience, we help businesses design practical approaches that improve processes, reduce risk and deliver lasting value.

If you are exploring agentic AI, now is the right time to seek expert guidance. Contact us today to find out how we can help you build a practical, high-impact and achievable AI strategy.