Nintex has forecast a shift in how organisations use agentic AI in 2026, with tighter scrutiny on spend, clearer use cases, and stronger governance requirements before projects move beyond pilots.

Chris Ellis, Director of Solutions Engineering at Nintex, said the market has moved through a period of enthusiasm and experimentation. He expects a more selective approach next year, driven by demands for measurable outcomes at the level of individual business processes.

“In 2026, agentic AI will hit its first real reality check – and that’s when it starts to mature,” said Chris Ellis, Director of Solutions Engineering, Nintex.

Ellis described two years of broad interest in agentic systems. He said many efforts did not produce clear outcomes. He said that dynamic will change as organisations reassess budgets and procurement standards for AI programmes.

“Over the past two years, we’ve seen enormous interest in agentic systems, but also a lot of experimentation without clear outcomes,” said Ellis.

Tighter Pilots

Ellis said organisations will reduce funding for what he called speculative work and shift towards narrower deployments. He expects teams to pick a single use case, validate it, then extend work only after they can demonstrate results.

“As market sentiment tightens and expectations rise, organisations will stop funding broad, speculative pilots,” said Ellis.

“Instead, they’ll focus on one well-defined use case, prove it works, and only then expand orchestration,” said Ellis.

He said agentic AI will still progress, but under stricter conditions tied to performance evidence in day-to-day operations. He also said some teams will struggle to justify moving past experimentation.

“Agentic AI won’t stall in 2026 – but it will become far more disciplined,” said Ellis.

“Teams that can’t clearly show value at the process level will struggle to move beyond proof-of-concept,” said Ellis.

Use Case Focus

Ellis expects a change in deployment strategies. He said the idea of applying agentic AI across an entire organisation in one programme will recede. He said organisations will focus on specific points in workflows where decisions occur and where performance can be measured.

“In 2026, the idea of deploying agentic AI everywhere at once will quietly disappear,” said Ellis.

“What we’re seeing already is that agentic AI delivers the strongest results when it’s applied to specific decision points inside real processes – not as a blanket layer across the enterprise,” said Ellis.

Ellis identified several areas where he expects faster progress. These include process orchestration, customer operations, analytics, and RPA augmentation. He said organisations will test agentic systems in those domains with an emphasis on metrics such as speed, accuracy, and cost.

“The fastest progress will happen in areas like process orchestration, customer operations, analytics, and RPA augmentation, where agents can improve speed, accuracy, or cost in measurable ways,” said Ellis.

He said teams should position agentic AI as one tool among others in the automation and workflow stack. He said a universal approach will not deliver consistent results across different functions and processes.

“Organisations that treat agentic AI as a targeted capability, rather than a universal solution, will see far better outcomes,” said Ellis.

Governance Controls

Ellis said observability will act as a gating factor for scaling agentic AI. He described a need for visibility into decision-making, operational costs, and points where people can step in. He said these requirements will shape which projects receive approval for production deployment.

“In 2026, observability will determine which agentic AI projects scale – and which ones stop at pilot,” said Ellis.

“As agents begin operating across multiple steps in a process, teams will demand clear visibility into how decisions are made, what they cost, and when humans can intervene,” said Ellis.

He said audit trails, decision tracking, and governance controls will shift from optional features to prerequisites. He said organisations will require these controls before they approve larger rollouts.

“Audit trails, decision tracking, and governance controls won’t be optional – they’ll be required before any serious production rollout,” said Ellis.

Ellis also framed the issue as an engineering constraint. He said teams need the ability to observe and explain behaviour if they expect agentic systems to operate safely within business processes at scale.

“From an engineering perspective, if you can’t observe and explain an agent’s behavior, you can’t safely scale it. The teams that build governance in from day one will be the ones that succeed,” said Ellis.

Ellis said he expects organisations to keep investing in agentic AI next year, but he said the market will judge deployments on evidence from live workflows and on governance readiness.