Integration stands at a crossroads as we head into 2026. From one perspective, the rise of agentic AI threatens to make traditional integration irrelevant as deterministic processes are replaced by probabilistic agents tying the enterprise together. Another perspective has the traditional integration toolkit becoming more important than ever as it provides the capabilities to “AI enable” the long tail of enterprise platforms while still offering deterministic processing for the places where near enough isn’t good enough. Which view will prevail? Only time will tell — in the meantime, it’s worth examining what these forces may mean for the year ahead.
1. The year of agentic AI, enabled by integration
Over the last two years, many organisations have experimented with AI that turned out to mostly be good for summarising emails and taking minutes. With a shift away from this more assistive AI and towards agentic, 2026 may be a turning point for companies seeing a return on their enterprise AI investment.
It doesn’t matter how fancy the agentic capabilities of an AI platform are, however, if there’s nothing for it to do. Integration, with its ability to interact with both legacy and modern systems in a cohesive and coherent manner, will become key in providing an interface for AI systems to use. Having clear auditing across all systems to track changes and actions performed by the AI will be a safety net, compliance requirement and essential diagnostic tool — another area where integration can provide a consistent approach across disparate systems.
It’s still too early to tell whether the promise of agentic AI will eventuate or if it will be another false dawn and we need some fundamentally new technology to achieve truly transformative outcomes. What is clear, however, is that integration is going to be key to getting us there. Investing in integration allows you to address business challenges today and set yourself up for AI success tomorrow.
2. APIs become “AI-ready”
Over the last year there has been a push for the “AI-ready” enterprise where organisations groom their business data to feed into enterprise AI models. The new wave of agentic AI, however, requires more than just data to ingest — it requires the ability to act independently against enterprise systems. What does this mean? It’s time for APIs to take their turn becoming “AI-ready”.
What will an “AI-ready” API need? Consistency, clarity and explicit definition, allowing an AI to infer system behaviour accurately. Discoverability also becomes critical: it is no longer enough for an API to exist, consumers, be they human or machine, must be able to find and understand what it does without hand holding. This is also where emerging standards such as MCP enter the picture, providing details on how to use an API and placing it within the business context.
The irony here is that the APIs which are most likely to already have high quality, well designed contracts and documentation — external facing APIs — are the APIs least likely to be interacted with by enterprise AI agents. It is the internal APIs — the ones which wrap key systems, the ones which were hacked together years ago, the ones no-one wants to touch — which are paradoxically more likely to need to be AI accessible as enterprises trial agentic AI on lower-risk internal processes. Tooling vendors will be key to bridging this gap, with API Management vendors who can automate the discovery and documentation process, as well as generation of MCP servers, well placed to succeed.
One day AI agents may be able to deduce context and process by merely observing the API operations, but we are not anywhere near that yet. AI agents should be treated as a junior developer on their first day — full of enthusiasm but lacking any context. Harnessing this and channelling it into the right outcomes will be the key to delivering on the promise of agentic AI, and AI-ready APIs will be a key component in this.
3. AI security to the fore
In 2025 it became increasingly apparent that LLMs are unlikely to ever be a fully secure technology. Regardless of this, enterprises are forging ahead with using them because of the allure of the productivity they can deliver. How can we reconcile these two points? By wrapping security around the agents, and watching them like a hawk.
We are already seeing nascent AI security capabilities being built in leading AI management tooling, with traceability and anomaly detection useful for detecting issues like rogue agents and data leakage. Over 2026 we expect to see more formalisation of tool and agent standards to support not only reactive monitoring but proactive security evaluation by AI management tooling. Using an external inspector system, operating independently of the agent runtime, to apply governance over the operation of agents will become best practice.
4. The rise of the “mega-platform”
Over the last few years we have seen a steady increase of mergers and acquisitions in the integration space. Vendors are expanding into new areas, and we are seeing the rise of “mega-platforms” covering all aspects of integration: iPaaS, data integration, API management, streaming and AI.
What is driving this trend? Customers are looking for a single control plane for all integration aspects in their enterprise. Having multiple overlapping platforms in play leads to fractured governance, creating friction and complexity in systems which are supposed to be reducing complexity, not adding to it. If a vendor can truly integrate their capabilities to create a unified experience then they will be well positioned to benefit — and in the consolidated world, there is more to play for.
In 2024, we saw Boomi acquire Rivery and in 2025, Salesforce acquired Informatica. In 2026 we have the purchase of Confluent by IBM to lead off the year with a bang. We have also seen platforms adding early-stage or lightly integrated features to broaden their perceived scope to compete with more established players. We expect that one way or another the trend towards platform consolidation will continue to gain speed in the coming year.
5. Sovereignty and “geopatriation” give hybrid integration a second wind
With the rise of the hyperscalers over the 2010s many roadmaps assumed a steady march toward consolidation in a single cloud, with location being immaterial. Reality has proven more complex. Looking at the coming year, regulatory requirements, geopolitical uncertainty, third-party risk management, and the need for credible exit strategies will start to drive a renewed focus on location-aware design. Certain data and processing must remain within specific jurisdictions or controlled environments, while other workloads continue to operate globally. Integration becomes the mechanism that makes this fragmentation manageable rather than chaotic.
In practice, this means more intentionality around hybrid architectures. Systems span multiple clouds, sovereign platforms, on-prem environments, and partner networks. Integration patterns must support portability, consistent security, and explicit data movement rules. Clear classification of data and well-defined routing decisions become as important as throughput and latency. Integration platforms themselves must be adaptable and flexible to span environments — platforms which are limited to SaaS only or a single hyperscaler will face headwinds meeting these new (old) challenges.
Rather than viewing hybrid integration as a temporary compromise, many organisations will treat it as a durable operating reality. The quality of the integration layer often determines whether this complexity is controlled or corrosive.
What 2026 means for integration strategy
Taken together, these trends point to a clear shift in how integration is perceived and governed. Integration is no longer just tying systems together; it is becoming a shared enterprise capability that underpins automation, AI, security, and resilience.
In 2026, the most effective integration strategies will focus on publishing governed business actions rather than building isolated connections. Interfaces will be treated as products, designed to be consumed safely by both humans and machines. Platforms will be chosen and rationalised with an eye toward long-term operability, not short-term feature coverage. Hybrid architectures will be designed explicitly, rather than tolerated reluctantly.
The key question is no longer which individual technologies to adopt, but instead whether the organisation is building an integration foundation that can support automation, change, and control at the same time. Those that are will find themselves well positioned for whatever 2026 delivers next.
