Nintex Tips 2026: Reality Check for Enterprise Agentic AI
Nintex has forecast a significant shift in how organizations will leverage agentic AI in 2026, characterized by tighter scrutiny on spending, clearer use cases, and stronger governance requirements before projects advance beyond pilot stages.
Market Transition
The Director of Solutions Engineering at Nintex indicated that the market has transitioned from a phase of enthusiasm and experimentation to a more selective approach. This change is driven by demands for measurable outcomes at the individual business process level.
“In 2026, agentic AI will hit its first real reality check – and that’s when it starts to mature,” he stated, underscoring the transition from broad interest to more focused implementations.
Tighter Pilots
Organizations are expected to reduce funding for what has been termed speculative work, shifting their focus towards narrower deployments. The strategy will involve selecting a single use case, validating it, and only then expanding the project based on demonstrated results.
“As market sentiment tightens and expectations rise, organizations will stop funding broad, speculative pilots,” he explained. Instead, the focus will be on proving a well-defined use case before scaling.
Use Case Focus
The deployment strategies for agentic AI are anticipated to change. The notion of applying agentic AI across an entire organization in a single initiative will likely recede. Instead, organizations will concentrate on specific decision points within workflows where performance can be accurately measured.
“The idea of deploying agentic AI everywhere at once will quietly disappear,” he noted. The strongest results are expected when agentic AI is applied to specific decision points within real processes rather than as a blanket solution.
Areas of Progress
Several domains are identified where faster progress is anticipated, including process orchestration, customer operations, analytics, and RPA augmentation. Organizations will test agentic systems in these areas with a focus on metrics like speed, accuracy, and cost.
“The fastest progress will happen in areas like process orchestration, where agents can enhance speed, accuracy, or cost in measurable ways,” he stated.
Governance Controls
Observability is set to become a critical gating factor for scaling agentic AI. There will be a growing need for visibility into decision-making processes, operational costs, and opportunities for human intervention.
“In 2026, observability will determine which agentic AI projects scale and which ones stop at pilot,” he remarked. Audit trails, decision tracking, and governance controls are expected to shift from optional features to prerequisites for larger rollouts.
“From an engineering perspective, if you can’t observe and explain an agent’s behavior, you can’t safely scale it,” he concluded. Organizations that incorporate governance from the outset will likely be the ones to succeed in the evolving landscape of agentic AI.
While investment in agentic AI is expected to continue, the market will increasingly evaluate deployments based on evidence from live workflows and governance readiness.
