Seven capabilities for an enterprise integration strategy that powers AI at scale

AI is rapidly becoming a catalyst for smarter decisions, faster execution, and better customer experiences. But AI delivers its greatest value when it’s securely connected to the data, systems, and workflows that are already powering your business.

Leading organizations are using this moment to modernize integration and turn AI from isolated experiments into repeatable outcomes. Use the following checklist to identify whether your enterprise integration strategy is ready to support AI at scale, and where focused investments can unlock even more value.

1. Move from point‑to‑point connections to composable integration

Custom scripts and brittle point‑to‑point connections do not scale with AI-powered workflows. It’s the reason many organizations are shifting toward composable, reusable integration building blocks.

This shift enables dynamic AI use cases such as Retrieval‑Augmented Generation (RAG), where real‑time context from multiple sources enriches prompts and improves accuracy. By standardizing and governing these connections, teams can scale AI use cases faster and with greater confidence.

2. Establish a unified control layer across APIs, data, and logic

AI thrives when decisions can trigger meaningful action. A unified orchestration layer allows enterprises to coordinate APIs, data flows, and business logic in one place.

This foundation makes it possible for AI outputs—such as recommendations or classifications—to activate downstream processes across ERP, CRM, and operational systems. With orchestration in place, AI becomes an active participant in business execution, not just analysis.

3. Unlock value from both structured and unstructured data

From PDFs and emails to real-time signals, AI benefits from diverse data types.

Organizations that invest in integrating structured and unstructured data – by ensuring that their ongoing mission-critical solutions can work alongside newer hybrid integration platforms – are better positioned to provide richer context to AI models.

A concrete example of this unification is combining Axway SecureTransport and Amplify Fusion: Managed File Transfer (MFT) continues to handle high‑volume, protocol‑driven file exchanges, while hybrid integration layers add API‑based transformation, real‑time routing, and broader system connectivity – bringing that file‑based data into AI workflows.

See also: How Managed File Transfer Adapts to Modern Hybrid Integration

This hybrid integration strategy improves relevance, reduces hallucination risk, and enables more intelligent automation across knowledge‑heavy and operational workflows.

4. Design integration for orchestration, not just connectivity

Modern AI use cases often involve multiple steps: enrich data, invoke a model, evaluate the response, trigger follow‑up actions… Observability is critical to maintain security and consistency.

Integration platforms that support end‑to‑end orchestration allow teams to design these flows visually and evolve them over time.

This approach accelerates innovation while keeping workflows observable, testable, and governed.

See also: How to Use AI with Enterprise Data Securely at Scale

5. Embed governance across teams and environments to scale AI

Enterprise decision-makers are prioritizing AI investments more than ever before, while also (justifiably) worrying about AI governance risks – from unintended consequences to potential security vulnerabilities.

See the webinar: “Rethinking Integration in an AI-Driven World”, with IDC guest speaker Shari Lava

But governance is not a blocker; it’s what enables AI to move from pilot to production. Enterprises that apply consistent policies for security, access control, auditing, and compliance across integrations can confidently expand AI usage.

With governance built into the integration layer, teams gain visibility into how data and models are used, helping balance innovation with trust and regulatory requirements.

6. Empower users to leverage emerging AI patterns like MCP and agentic workflows

RAG laid the groundwork for bringing enterprise context into AI models. The next wave – MCP‑driven context exchange and agentic workflows – demands integration that is even more dynamic, event‑driven, and reusable.

Low-code tools and self-service capabilities help democratize integration and reduce IT bottlenecks, empowering business users to make changes without disrupting your current systems.

By adopting a composable integration strategy today, enterprises create a flexible foundation that supports both current AI initiatives and future innovations.

7. Treat integration as a strategic asset for AI success

Integration has fast evolved from a behind-the-scenes IT function to a strategic enabler of enterprise intelligence.

In 2026 and beyond, APIs, MFT, B2B, and events need to work together. When integration is designed for orchestration, governance, and reuse, AI initiatives gain speed, reliability, and measurable business impact.

How to unlock the full potential of intelligent integration

As Gartner notes, AI and integration have a symbiotic relationship; each amplifies the value of the other. Organizations that recognize this are better positioned to turn their AI ambitions into impact.

Axway built Amplify Fusion to help enterprises move beyond fragmented tools and tactical fixes, toward a unified orchestration layer where AI, APIs, data, and logic work together – securely, observably, and at scale.

From chaos to coordination – discover enterprise orchestration at scale with Amplify Fusion

Fusion’s true power is unlocked within Axway’s broader ecosystem – alongside MFT, API management, and B2B integration – creating a seamless, end-to-end foundation for intelligent workflows.

The opportunity is clear: with a governed integration foundation, AI stops being a promising idea and becomes a dependable engine for efficiency, insight, and growth.