By 2026, digital transformation is no longer just about “going digital.” The frontier is hyperautomation: a disciplined, enterprise-wide approach to automating as many business and IT processes as possible, end to end, using a fusion of AI, machine learning, event-driven architectures and robotic process automation. Instead of isolated bots and scripts, organizations are assembling full automation fabrics that sense, decide and act at scale.

For leaders, hyperautomation is becoming the connective tissue that links AI-native platforms, platform engineering, spatial computing and preemptive cybersecurity into a single, continuously adapting operating model.

From task automation to intelligent, end-to-end flows

Early automation initiatives focused on narrow tasks such as invoice processing or password resets. Hyperautomation extends that idea by chaining multiple automation technologies across entire processes, including exception handling, analytics and human approvals.

Gartner describes hyperautomation as a staple discipline for ninety percent of large enterprises, driven by renewed demand since the rise of generative AI. Gartner+1 It involves blending process mining, RPA, AI, event-driven software and integration platforms so that decisions and actions can be orchestrated from end to end.

Consultancies and vendors echo this narrative. RSM notes that hyperautomation integrates AI, RPA and advanced analytics to create smarter, adaptive systems that continuously optimize themselves. RSM Global IBM highlights how hyperautomation can transform processes and customer experiences while reducing costs and improving consistency, particularly when combined with intelligent document processing and conversational AI. IBM

The result is a shift from isolated bots to interconnected automation fabrics that touch every function, from finance and HR to supply chain, IT operations and customer service.

A market measured in hundreds of billions

The economic stakes behind hyperautomation are substantial. Gartner forecasts that software enabling hyperautomation represents an ecosystem worth hundreds of billions of dollars, as organizations invest in AI, RPA, low-code, event streaming and integration platforms to support automation at scale. Kissflow

Market research from GMI projects strong growth in the hyperautomation market through 2034, with AI-driven automation platforms like UiPath’s Autopilot already contributing significantly to revenue growth by accelerating routine work. Global Market Insights Inc. Atomicwork’s 2025 guide suggests that organizations combining hyperautomation technologies with redesigned operational processes can reduce operational costs by around thirty percent, particularly in IT and employee service management. Atomicwork

Yet statistics from Vena show that fewer than twenty percent of organizations have effectively measured the business impact of hyperautomation, underlining the maturity gap between technology adoption and value realization. Vena Solutions This gap is one of the central challenges transformation leaders must address.

Hyperautomation as the “operating system” for digital business

Digital transformation programs increasingly treat hyperautomation as an operating system rather than a collection of tools. AscentCore describes hyperautomation as the future business OS, where the most successful organizations use automation to connect data, AI and workflows across every department. AscentCore

This operating system has several layers. At the base is a platform engineering foundation that provides consistent APIs, event buses, identity and observability. On top sits an orchestration layer that coordinates automation tools and AI agents. Above that, business domains define reusable automation building blocks for processes such as order-to-cash, procure-to-pay, incident-to-resolution or hire-to-retire.

Hyperautomation also redefines how people interact with systems. Instead of manually initiating workflows in multiple applications, employees engage through conversational interfaces, AI copilots and intelligent portals that trigger automated flows behind the scenes. The human role shifts from performing tasks to supervising, redesigning and improving automated systems.

GenAI, agentic AI and the next wave of hyperautomation

The convergence of hyperautomation with generative and agentic AI marks a new phase. Generative AI can now read and classify documents, draft responses, generate code and summarize complex records. Agentic AI can plan and execute multi-step workflows on behalf of users.

Hyperautomation frameworks increasingly embed these AI capabilities. ConnectWise’s view on 2025 hyperautomation trends emphasizes the integration of AI models into automation platforms to deliver more adaptive, data-driven processes and personalized experiences. ConnectWise Hyperautomation tools can call LLMs to interpret unstructured inputs, propose next best actions or generate content that feeds downstream steps.

In advanced organizations, AI-native platforms orchestrate multiple agents that perform tasks such as data extraction, compliance checks, routing, approvals and follow-ups. Hyperautomation pipelines become the highways on which these AI agents travel, governed by platform policies and observability tools.

Use cases reshaping front-, mid- and back-office operations

Hyperautomation use cases now span the entire enterprise. Kanerika highlights sectors like financial services, IT service management and insurance as early beneficiaries, with automations handling loan processing, service ticket triage and claims management. Kanerika AgreeYa points to growing adoption of digital twins and intelligent automation in operations as one of the key hyperautomation trends for 2025. agreeya.com

In the front office, hyperautomation powers omnichannel customer service, blending chatbots, human agents, sentiment analysis and back-end process automation to resolve issues quickly and consistently. In the middle office, it supports risk and compliance functions by automatically scanning transactions, documents and logs for anomalies. In the back office, hyperautomation streamlines finance, procurement and HR, reducing cycle times and improving auditability.

Supply chains are a particularly fertile ground. Gartner’s supply chain automation roadmap describes a journey from basic automation to augmented decision-making and, eventually, autonomous operation by the mid-2030s. Gartner Hyperautomation is the mechanism that connects robotics, IoT, AI forecasting and business rules into cohesive, increasingly autonomous supply chain flows.

Organizational design and the rise of automation fabrics

To reap these benefits, organizations must redesign their operating models. Many are establishing automation centers of excellence that evolve into “automation fabrics” woven through business units. These fabrics combine process mining, citizen development, low-code and professional engineering under a shared governance model.

Hyperautomation also changes how IT and business collaborate. Business technologists and citizen developers use low-code tools and process discovery to propose automations, while central IT and platform teams ensure scalability, security and resilience. The most mature organizations adopt a federated model: local teams design and maintain domain-specific automations, guided by global standards and reusable components.

Metrics and incentives must align with this fabric mindset. Rather than celebrating individual bots shipped, teams measure process-level outcomes such as touchless rates, cycle-time reductions, error reductions and business value delivered per automation.

Pitfalls: Fragmentation, “spaghetti bots” and measurement blind spots

Hyperautomation can fail spectacularly if poorly governed. Organizations sometimes build “spaghetti bots,” a tangle of automations with opaque dependencies, limited documentation and conflicting owners. When underlying systems change, failures cascade through the fabric.

Fragmented tool choices are another risk. Departments procuring separate automation platforms create silos that undermine enterprise-wide optimization. Without a strong platform engineering foundation, integrations between automations, core systems and AI models become brittle.

Measurement remains a persistent blind spot. The Vena data that fewer than one in five organizations have measured hyperautomation’s impact demonstrates how easily programs can drift away from value. Vena Solutions To avoid this, leaders must embed analytics and value tracking from the beginning, tying automation metrics directly to revenue, cost, risk and experience outcomes.

Hyperautomation, security and confidential computing

As hyperautomation touches more sensitive processes and data, security and privacy become central concerns. Automations often access confidential information, invoke APIs on critical systems and act on behalf of users.

This is where emerging disciplines like confidential computing and preemptive cybersecurity intersect with hyperautomation. Confidential computing uses hardware-based trusted execution environments to protect data during processing, which can help secure AI models and automation workloads running in public clouds or partner environments. Cloud Security Allianc Preemptive cybersecurity, meanwhile, uses AI-driven analytics and automated response mechanisms to detect and neutralize threats in real time.

Forward-thinking organizations are designing hyperautomation architectures with “security by design,” ensuring that identity, least privilege, attestation and runtime protection are baked into automation platforms rather than bolted on later.

Closing thoughts and looking forward

Hyperautomation is rapidly becoming the mechanism through which digital transformation is operationalized. It translates strategy into thousands of small automated decisions and actions that accumulate into large-scale change.

By 2026 and beyond, the organizations that pull ahead will be those that treat hyperautomation not as a tooling project but as a new operating philosophy. They will pair AI-native platforms and agentic AI with disciplined platform engineering, spatial computing interfaces for frontline workers, confidential computing for sensitive data and preemptive cybersecurity to guard an increasingly automated enterprise.

The opportunity is immense, but so is the responsibility. Hyperautomation can liberate people from repetitive work and unlock new value, or it can entrench complexity and risk. The difference will lie in governance, culture and the willingness of leaders to reimagine how humans and automation collaborate.

References

1. “Gartner Says 30% of Enterprises Will Automate More Than Half of Their Network Activities by 2026” – Gartner Newsroom – https://www.gartner.com/en/newsroom/press-releases/2024-09-18-gartner-says-30-percent-of-enterprises-will-automate-more-than-half-of-their-network-activities-by-2026

2. “Hyperautomation: The Next Frontier in Digital Transformation” – AscentCore – https://ascentcore.com/2025/10/01/hyperautomation-the-next-frontier-in-digital-transformation/

3. “The Ultimate Guide to Hyperautomation in 2025” – Atomicwork – https://www.atomicwork.com/esm/hyperautomation-guide

4. “Hyperautomation: The Benefits and Challenges” – IBM Think – https://www.ibm.com/think/insights/hyperautomation-benefits-and-challenges

5. “Hyperautomation Trends for 2025” – ConnectWise – https://www.connectwise.com/blog/hyperautomation-trends

Phil Giroux, Co-Editor, Digital Transformation, Montreal, Quebec.
Peter Jonathan Wilcheck, Co-Editor, Miami, Florida.

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