Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Peter White, CPO, Agentic Solutions at Automation Anywhere
Artificial intelligence has moved beyond the hype cycle and into the realities of day to day operations. Enterprises are no longer experimenting – they are scaling. As organizations shift from early pilots to broader deployments, AI is revealing both its transformative promise and its practical limitations. Heading into 2026, the companies that succeed will be defined not by who adopts AI first, but by who implements it with clarity, governance, and long term discipline. Below are three predictions that outline the direction of enterprise AI in the coming year.
1. Trust will remain essential but it will not be the next big battleground
Trust has dominated AI conversations for years and it will continue to matter in 2026. But it will not become the dramatic market differentiator some expect. Customers already assume that responsible training data, strong security, transparent auditability, and predictable system behavior are standard. Companies that meet these expectations will maintain credibility. Those that do not will fall behind.
Trust is becoming the baseline rather than the next major innovation. As AI becomes more embedded in enterprise workflows, attention will shift away from basic assurances and toward deeper questions about performance, consistency, and applied intelligence.
2. Moving from pilot to scale will expose new gaps
Almost every enterprise has completed a successful AI pilot. Far fewer have successfully scaled one. In 2026, the most revealing insights will not come from controlled tests. They will emerge when AI systems interact with real users across varied environments.
When thousands of employees rely on agents across different workflows and data conditions, reliability challenges will surface that were invisible during testing. Organizations may encounter latency issues, timeout risks, computational strain, and unexpected behaviors driven by unfamiliar edge cases. Many companies will realize that scaling AI is not a milestone. It is an ongoing cycle of monitoring, adjustment, and continuous improvement.
3. Automation and LLMs will become long term partners
In 2026, organizations will stop treating large language models as self contained systems. Instead, they will pair them with automation and the supporting structures required to operate effectively at scale. The scaffolding around a model will become more important than the model itself. Workflows, orchestration layers, controls, evaluation frameworks, and application level intelligence will determine whether an AI system can operate reliably in real world environments.
The real strength of enterprise AI comes from models that can both reason and act within a governed structure. The future will belong not to the strongest model, but to the strongest system.
Conclusion
Together, these three predictions reflect a turning point: AI maturity will no longer be measured by pilots or press releases, but by stable performance at scale. The organizations that invest in governance, operational rigor, and thoughtful system design will lead the next chapter of enterprise AI.
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ABOUT THE AUTHOR
Peter White is the chief product officer at Automation Anywhere, where he drives AI strategy and innovation across AI agents, agentic process automation and developer tools. Prior to this role, he spent over eight years at Salesforce shaping Einstein AI and Service Cloud solutions, following earlier roles at Autodesk and KLA-Tencor. Peter holds an M.S. in mechanical engineering from UC Berkeley and a B.S. in mechanical engineering from UC Santa Barbara.
