As AI agents, iPaaS, and AIOps converge, “infrastructure as a platform” in 2026 is rapidly evolving into a hyper-automation engine that can design, execute, monitor, and repair business operations with minimal human intervention.

Hyper-automation moves into the platform core

For most of the last decade, automation lived inside individual tools. RPA bots clicked through legacy screens, workflow engines routed approvals, and scripts kept batch jobs moving overnight. Hyper-automation, as defined by analysts, is the next step: it combines multiple technologies such as RPA, AI, event streaming, and integration platforms into end-to-end automation of business processes.Autonom8

By 2026, that combination is no longer stitched together ad hoc. It is being consolidated into a cohesive Infrastructure-as-a-Platform layer on top of cloud-native infrastructure and iPaaS. The platform becomes the central execution fabric for hyper-automation. This is the place where AI models are invoked, workflows run, events are routed, and data is transformed and stored.

Analyst commentary on iPaaS trends for 2025 already highlights that integration platforms are shifting from simple connectivity to intelligent automation and workflow orchestration.Informatica+1 Platform providers are adding low-code flow designers, embedded AI assistants, and event-driven runtime engines that can react to triggers from any system, channel, or device. In 2026, that means an order update, a sensor anomaly, or a customer support interaction can all be treated as events inside the same platform and fed into shared, reusable automation patterns.

Hyper-automation at this level is not just about speed. It is about consistency and governance. Instead of every business unit bolting on its own bots and scripts, a single Infrastructure-as-a-Platform layer provides shared policies, observability, and security. When compliance teams need to understand how customer data flows through automated processes, they inspect the platform’s lineage views rather than hunting through dozens of disconnected tools.

Agentic AI and iPaaS: Automation that designs automation

One of the clearest signs that Infrastructure as a Platform is maturing is the arrival of agentic AI on top of iPaaS. In a 2025 article, Forvis Mazars describes how AI agents depend on a versatile connectivity foundation, with iPaaS acting as the hub that connects applications, data sources, and workflows so agents can act across the enterprise.Forvis Mazars

In 2026, those AI agents are no longer merely executing predefined flows. They are helping to design them. Informatica’s view of AI-led integration highlights how generative AI is now used to recommend integration patterns, generate mappings, and build workflows that previously required specialist skills.Informatica When these capabilities sit inside an Infrastructure-as-a-Platform layer, they effectively turn the platform into an “automation foundry” where AI can rapidly prototype and refine new flows.

A typical scenario illustrates the shift. A supply chain manager tells an AI copilot inside the platform to “automate back-order notifications and dynamic ETA updates to customers, using data from the ERP, logistics provider, and warehouse sensors.” The agent consults the platform’s catalog of connectors, events, and policies. It designs an event-driven flow that listens for inventory changes, calls out to carrier APIs for shipping status, and updates customer communication channels. It generates the initial mappings and even proposes SLAs and monitoring thresholds.

Humans still review and finalize the design, especially for high-risk processes. But the heavy lifting of plumbing is handled by AI that understands the semantics of events and APIs. Over time, the agent learns from operational data. If a certain carrier consistently lags behind its promised delivery times, the agent may recommend rerouting shipments or adjusting promised ETAs. In essence, automation starts to design better automation.

Gartner’s broader technology trend forecasts point to agentic AI and AI governance platforms as strategic technologies through 2025 and beyond, with a growing share of day-to-day decisions expected to be made autonomously.Gartner+1 Infrastructure-as-a-Platform is the proving ground where those predictions either come true or run into operational reality.

AIOps: From noisy dashboards to self-driving operations

Hyper-automation at the business level is impossible without reliable operations underneath. That is where AIOps comes in. Market research indicates that the global AIOps platform market is growing aggressively, with valuations forecast to reach tens of billions of dollars by the early 2030s as enterprises struggle to manage complex hybrid environments and exploding observability data.Mordor Intelligence+2PR Newswire+2

In 2026, AIOps is increasingly embedded directly into Infrastructure-as-a-Platform stacks. Rather than being a separate monitoring tool, AI-driven operations monitoring is built into the same layer that runs integrations, workflows, and microservices. The platform continuously ingests logs, metrics, traces, and events from applications, cloud services, network devices, and edge gateways.

The key change is autonomy. Early AIOps focused on reducing noise and helping humans identify root causes. Modern implementations go further. They propose remediation actions, simulate their impact, and then execute them within predefined guardrails. If a surge in API latency is detected, the platform might automatically scale out integration runtimes, re-balance load across regions, or temporarily route traffic away from a failing dependency.

Because the Infrastructure-as-a-Platform layer sees both the business process context and the technical signals, it can make smarter decisions than a traditional infrastructure monitor. It knows, for example, that an integration powering real-time fraud detection is more critical than a nightly data sync, so it can prioritize resources accordingly.

This evolution aligns with platform engineering trends that emphasize increased automation, AI-driven development, and cloud-native architectures as hallmarks of 2025 and beyond.DuploCloud+1 In many organizations, platform engineering teams are now responsible for both the automation fabric and its AIOps layer, blurring the line between “building the platform” and “running the platform.”

Platform engineering as the automation backbone

Platform engineering has quickly become the organizational answer to the complexity of modern infrastructure. Rather than leaving teams to assemble their own toolchains, platform engineers build shared “golden paths” that package best practices, automation, and guardrails into reusable self-service products.

In 2026, Infrastructure as a Platform is the natural domain of these teams. They design and maintain the internal developer platforms and integration backplanes that everyone else uses. Industry observers note that platform engineering is moving from hype to reality, adopting proven patterns from adjacent disciplines, and focusing on measurable outcomes such as deployment frequency, failure recovery time, and developer satisfaction.DuploCloud+1

When hyper-automation is layered on top, platform engineers become the architects of self-driving operations. Their responsibilities include defining standard automation building blocks, curating connector catalogs, embedding policy-as-code, and exposing automation capabilities as consumable APIs and templates. For example, a “launch a new data product” path might automatically provision storage, access controls, observability dashboards, and integration pipelines to downstream systems.

This internal product mindset is critical to avoiding chaos. Without it, hyper-automation can degenerate into a sprawl of scripts, bots, and one-off pipelines. With it, automation becomes a set of well-governed services that teams can rely on, similar to how they rely on electricity or networking. Platform engineers treat automation itself as an infrastructure capability to be standardized, secured, and continuously improved.

Business impact: From workflows to autonomous value chains

The promise of Infrastructure as a Platform for hyper-automation is not simply fewer tickets or faster integration projects. It is a structural change in how value chains operate.

Research from McKinsey and others suggests that automation and AI could theoretically automate a significant portion of work activities and boost labor productivity by roughly one percentage point annually through 2030 in some scenarios.Nexford University+3McKinsey & Company+3McKinsey & Company+3 While those are economy-wide estimates, they hint at what is possible inside enterprises that successfully embed hyper-automation into their infrastructure.

Consider a retail and logistics example. Customer orders flow in from e-commerce sites and marketplaces. The Infrastructure-as-a-Platform layer routes each order through credit checks, inventory allocation, warehouse tasking, and carrier selection. AI agents use historical performance and real-time signals to choose the best fulfillment route. Edge integration pipelines monitor conditions in warehouses and trucks, feeding data back into AIOps systems that adjust resource levels and maintenance schedules.

When a disruption occurs—a storm, a labor shortage, a supplier failure—the same platform detects the anomaly, recommends alternatives, and can automatically reconfigure parts of the value chain within policy constraints. Some decisions still require human sign-off, especially those with customer or regulatory implications. But much of the operational adaptation becomes a dialogue between AI agents, platform rules, and human supervisors, rather than a scramble of emails and spreadsheets.

The economics are compelling. Fewer manual handoffs and faster feedback loops reduce error rates and operating costs, while improved resilience protects revenue during disruptions. Data quality improves as integration and automation pipelines are treated as first-class products. And because the platform captures detailed telemetry at every step, organizations gain the analytical foundation needed for continuous optimization.

Risks and realities: Not every process should drive itself

Despite the momentum, 2026 is also a year of course-correction. Analysts warn about “agent washing,” where vendors label conventional automation as agentic AI, and forecast that a significant share of early agentic AI projects will be scrapped by 2027 due to cost and lack of clear business value.Reuters The lesson for Infrastructure-as-a-Platform teams is clear: automation for its own sake is a trap.

Not every process should be fully autonomous. Some workflows involve ethical judgments, nuanced customer interactions, or complex trade-offs that current AI cannot reliably handle. Others span regulatory boundaries where automated decisions must remain explainable and auditable. The danger is that a too-aggressive push toward self-driving operations could undermine trust with customers, employees, and regulators.

Leading organizations in 2026 are adopting a tiered approach. They reserve full autonomy for low-risk, high-volume tasks such as log analysis, routine resource scaling, or standard internal notifications. For higher-stakes processes, they use AI to augment human decision-makers, providing recommendations, simulations, and automated options that humans can accept or override.

Crucially, Infrastructure as a Platform supports this spectrum natively. The same automation engine can support fully automated flows, human-in-the-loop approvals, and human-on-the-loop supervision. Policies specify which category a given process belongs to and under what conditions it can move up or down the autonomy ladder. By formalizing these distinctions in the platform, organizations avoid both under- and over-automation.

Closing thoughts and looking forward

By 2026, Infrastructure as a Platform has become the stage on which hyper-automation, agentic AI, AIOps, and platform engineering all perform together. The vision is a self-driving enterprise where routine operations run themselves, exceptions are caught early, and human expertise is focused on strategy, design, and complex decision-making rather than fire-fighting.

The reality is uneven. Some sectors are already seeing sophisticated AIOps, AI-designed workflows, and near-autonomous value chains. Others are still wrestling with legacy systems, fragmented data, and cultural resistance. Yet the direction of travel is clear. Market signals from AIOps growth, platform engineering adoption, and iPaaS innovation all point toward a world where automation is a platform capability, not a bolt-on project.Autonom8+4Informatica+4Mordor Intelligence+4

Looking ahead to 2027 and beyond, the winners will be those who pair ambition with discipline. They will invest in platform engineering teams that own the automation backbone, build strong governance around AI and autonomy, and design their Infrastructure-as-a-Platform to be transparent, secure, and adaptable. They will also keep humans firmly in the loop for the processes that truly matter, recognizing that the goal is not to eliminate people, but to elevate what people spend their time on.

Hyper-automation is not an endpoint; it is a capability. Infrastructure as a Platform is how that capability becomes repeatable, scalable, and safe. For CIOs, COOs, and CISOs in 2026, the question is no longer whether to embrace it, but how quickly and thoughtfully they can make it the new operating standard of their enterprise.

References

1. “AI-Led Integration: 6 Emerging Trends Shaping the Future of iPaaS” – Informatica – https://www.informatica.com/blogs/ai-led-integration-6-emerging-trends-shaping-the-future-of-ipaas.html Informatica

2. “Hyperautomation: A Comprehensive Overview in 2025” – Autonom8 – https://autonom8.com/hyperautomation/ Autonom8

3. “AIOps Market Size, Demand, Share Analysis & Forecast 2025–2030” – Mordor Intelligence – https://www.mordorintelligence.com/industry-reports/aiops-market Mordor Intelligence

4. “Emerging Trends in Platform Engineering for 2025” – DuploCloud – https://duplocloud.com/blog/emerging-trends-in-platform-engineering-for-2025/ DuploCloud

5. “AI Agents & iPaaS: An Agile Approach to Automation” – Forvis Mazars – https://www.forvismazars.us/forsights/2025/09/ai-agents-ipaas-an-agile-approach-to-automation Forvis Mazars

Benoit Tremblay, Author, IT Security Management, Montreal, Quebec.
Peter Jonathan Wilcheck, Co-Editor, Miami, Florida.

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