Executive summary
Nintex leaders discuss the state of AI and process automation today and predict the trends of 2026:
- AI and process automation will converge
- Hyperautomation will reign, but requires strong governance
- Process intelligence will be more important than flashy pilots
- Finance teams will become key players in technology decisions
The tech narrative of the past few years has been dominated by the explosive arrival of generative AI. Seemingly every week brought a new breakthrough, a new tool, or a new promise of revolutionized productivity.
But as we look ahead to 2026 and beyond, the conversation is shifting. We are moving from the “wow” phase of AI experimentation to the “how” phase of operational reality.
For IT and line-of-business leaders, the next two years won’t just be about adopting new technology, but about making that technology work at scale.
It’s no longer enough to have a pilot program that writes emails or summarizes meetings. The pressure is on to demonstrate tangible ROI, ensure rock-solid governance, and integrate these advanced AI tools into the complex, human-driven reality of your workflows.
At Nintex, we spend our days right in the middle of that complexity. We see firsthand how organizations are struggling to bridge the gap between AI’s potential and its practical application.
To help you navigate this transition, we’ve gathered insights from our leadership team to predict what the landscape will look like in 2026, so you can develop your roadmap, guide your tech decisions, and hone your focus.
The state of AI and process automation today: From experimentation to execution
Right now, many organizations are suffering from “pilot fatigue.” They may have dozens of disparate AI initiatives running in silos, but those initiatives are often disconnected from core business objectives. The initial excitement is waning as a realization sets in: AI without process is just chaos.
Research backs up this disconnect. MIT’s “State of AI in Business 2025” report found that despite $30–40 billion in enterprise investment, a staggering 95% of generative AI initiatives are producing no return.
However, our perspective at Nintex is a bit more positive than that … and grounded in direct industry feedback. In a recent survey of more than 700 CIOs and CFOs globally, 95% of respondents say their combined automation and AI initiatives are effectively delivering on business outcomes.
The survey also revealed that 84% of CIO and CFO respondents now believe automation is a necessary first step before successfully implementing AI in business processes.
This consensus underscores a key lesson on which our predictions are based: To maximize the value of AI, achieve measurable outcomes, scale effectively, and safeguard your critical operations, organizations must anchor their AI strategy in a strong foundation of automation.
In 2026 and beyond, the winners won’t be the companies with the most AI tools. They will be the companies that have mastered the art of agentic business orchestration. They will understand that you cannot automate what you do not understand, and you cannot govern what you cannot see. The future of automation isn’t about replacing humans, but about building a structured, secure, and efficient foundation where humans and AI agents work side by side.
Let’s explore the four key predictions that will shape this new reality:
Prediction 1: AI and process automation will converge
One of the biggest threats to enterprise efficiency is the unchecked proliferation of AI tools. Just as we saw with SaaS applications a decade ago, organizations are rapidly accumulating AI capabilities without a unified strategy.
Niranjan Vijayaragavan, Chief Product & Technology Officer, warns that this rush is creating a new class of enterprise waste.
“The rush into generative AI is creating a new class of inefficiency: AI sprawl,” he says. “Organizations are rapidly deploying multiple AI tools across departments, often without coordination, governance, or a clear connection to core business processes. Instead of replacing existing complexity, AI is being layered on top of already sprawling SaaS environments: adding new costs, new risks, and new fragmentation to systems that were already difficult to manage.”
The danger here is that AI investments might simply shift work rather than eliminate it. If a team uses AI to generate code faster, but the QA process is manual and bottlenecked, you haven’t gained efficiency … you’ve just moved the traffic jam.
“The organizations that succeed with AI will be those that step back and address sprawl first: consolidating tools, standardizing processes, and rebuilding AI on a unified automation backbone,” Vijayaragavan says. “AI will deliver value only when it operates within well-orchestrated workflows and governed data flows.”
This need for consolidation dovetails with a shift in financial scrutiny. CFOs are no longer writing blank checks for “innovation.”
Burt Y. Chao, Chief Financial Officer, predicts a hard stop to experimentation theater.
“For the past several years, many organizations have invested in AI pilots that look impressive in demos but deliver limited value in day-to-day operations,” says Chao. “These initiatives often fail because they sit on top of fragmented systems, inconsistent data, and manual processes that AI cannot fix on its own. By 2026, tolerance for these low-impact pilots will disappear as executives demand measurable outcomes, not experimentation theater.”
The solution is to anchor AI in automation. Automation provides the infrastructure that allows AI to function reliably within a business context.
“Automation will emerge as the decisive factor separating successful AI programs from failed ones,” he explains. “It provides the clean data, standardized workflows, and operational infrastructure AI needs to scale across real business processes. Without automation, AI remains confined to isolated use cases; with it, AI can be embedded into core operations in ways that are repeatable, auditable, and economically defensible.”
Prediction 2: Hyperautomation will reign, but requires strong governance
As AI agents begin to take actions — approving invoices, routing customer tickets, or updating records — governance becomes the most critical architectural decision you will make. You need to know why an AI agent made a decision, who authorized it, and what data was used.
In 2026, governance won’t just be a compliance checklist; it will be the foundation of trust. If you cannot explain an AI’s decision to an auditor or a board member, you cannot use that AI in production.
“In 2026, the central challenge for organizations won’t be whether AI works, but whether its decisions can be trusted, explained, and defended,” says Vijayaragavan. “As AI systems influence approvals, financial decisions, customer interactions, and compliance outcomes, the lack of traceability will become an unacceptable risk for executives and boards.”
This is where process automation shines. It acts as the “wrapper” around the AI, enforcing rules and capturing data at every step.
“Automation will become the mechanism that makes AI governable at scale. By embedding audit trails, human-in-the-loop checkpoints, permissions, and standardized data pathways directly into workflows, automation will evolve from an efficiency tool into the primary safeguard for enterprise AI.”
This theme of accountability extends globally. In emerging markets, the focus is shifting rapidly toward execution quality and governance.
Samir Akel, RVP for Emerging Markets at Nintex, notes that the Middle East is entering an era of strict accountability.
“Across Saudi Arabia and the UAE, AI is entering a more accountable phase,” he says. “Organizations are now expected to show measurable outcomes, strong governance, and real operational impact. Governance is shaping how AI is designed and deployed across the region. Enterprises are prioritizing transparency, auditability, and control from day one.”
Similarly, Chris Ellis, Director of Solutions Engineering, points out that agentic AI will face a “reality check.”
“In 2026, observability will determine which agentic AI projects scale — and which ones stop at pilot,” he says. “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. Audit trails, decision tracking, and governance controls won’t be optional — they’ll be required before any serious production rollout.”
Prediction 3: Process intelligence will be more important than flashy pilots
One of the most painful lessons of the digital transformation era is that you cannot fix a broken process with even the best software. Layering AI on top of a chaotic, undocumented workflow is a recipe for disaster. In 2026, the most successful organizations will be those that prioritize their processes over technology acquisition.
Vijayaragavan argues that process visibility is the missing link in many failed transformations.
“For years, businesses have tried to buy efficiency through tools … But in 2026, it will become clear that technology alone cannot deliver efficiency if organizations don’t first understand how work actually gets done,” he says. “In many companies, processes exist only as tribal knowledge — undocumented, inconsistent, and constantly changing — making meaningful improvement nearly impossible.”
To effectively deploy AI, you first need a map: You need to know where the data comes from, where the bottlenecks are, and what the “happy path” looks like.
“By 2026, the most efficient organizations will treat process intelligence as foundational infrastructure,” Vijayaragavan continues. “They will continuously capture, model, and refine processes to identify friction, standardize execution, and measure improvement.”
This sentiment is echoed by our leaders across the globe. In the Asia-Pacific region, Keith Payne, RVP, APAC, sees a realization dawning for many business leaders.
“One trend we’ll see emerge next year is the understanding that before AI can deliver value to the business, existing processes will need to first be automated and optimized,” he says. “AI is a force multiplier. If the processes AI is applied to are already broken or inefficient, those problems don’t go away – they get magnified.”
Emilio Rosa, RVP, Europe, notes that the EU’s push for digital maturity is hitting a process roadblock.
“As organizations make the jump from testing to scaling, many will find that existing processes aren’t ready to support it,” Rosa says. “Europe’s complex and often manual workflows cannot simply have AI layered on top. Next year will be all about streamlining and automating these foundations. AI is a gamechanger, but if it’s built on broken processes, businesses won’t get to reap the benefits.”
Prediction 4: Finance teams will become key players in technology decisions
The final major prediction centers on financial sustainability. The era of “growth at all costs” is over. CFOs are taking a hard look at their software stacks, and “SaaS sprawl” is in their crosshairs.
“In 2026, CFOs will no longer view SaaS sprawl as an IT management issue — it will be recognized as a material financial liability,” says Chao. “This level of sprawl drives hidden financial impact: underused licenses, overlapping tools, inconsistent data, and fragmented workflows that increase labor costs and slow critical processes like close, forecasting, and compliance.”
The response will be a rigorous consolidation. Organizations will look for platforms that can handle multiple functions — automation, document generation, process mapping — rather than paying for dozens of point solutions.
“In 2026, finance leaders will increasingly gate new software spend behind clear orchestration and automation strategies — prioritizing consolidation, standardization, and measurable workflow efficiency over incremental tool accumulation,” Chao continued.
But finance teams won’t just be cutting costs. They’ll be transitioning into insight engines. Automation and agentic business orchestration will liberate finance professionals from the drudgery of manual reconciliation, allowing them to focus on strategic insights. In the future Chao envisions, finance teams will be able to operate more proactively and strategically.
“For too long, finance teams have spent too much time on manual work,” he says. “In 2026, automation will take over most of these operational tasks, ensuring they run consistently, accurately, and without manual intervention. With that automated foundation in place, AI will add intelligence on top: detecting anomalies early, predicting cash-flow shifts, surfacing spending risks, and modeling scenarios in real time.”
Ellis suggests that cost control will drive a more disciplined approach to agentic AI. Rather than trying to automate everything at once, companies will target specific, high-value decision points.
“In 2026, the idea of deploying agentic AI everywhere at once will quietly disappear,” he says. “What we’re seeing already is that agentic AI delivers the strongest results when it’s applied to specific decision points inside real processes … Organizations that treat agentic AI as a targeted capability, rather than a universal solution, will see far better outcomes.”
Preparing your organization for 2026
The consensus from our leadership team is clear: The future belongs to those who build on a strong foundation. The shiny allure of AI is powerful, but without the structure of process automation and the safety net of governance, it remains more of a toy than a tool.
To prepare for 2026, you should be asking your team these questions:
- Do we really know our processes? Is our “tribal knowledge” documented and digitized?
- Is our data ready? Do we have the governance in place to guardrail our AI’s decisions?
- Are we consolidating? Are we adding more tools to the pile, or are we building a unified orchestration layer?
The organizations that answer these questions correctly will be the ones that turn AI from a cost center into a competitive advantage.
At Nintex, we are committed to helping you build that foundation. Whether you are looking to map your workflows, automate your complex processes, or govern your AI agents, our platform is designed to provide the control and visibility you need.
Ready to future-proof your operations? Learn more about the Nintex Platform
