AI Orchestration Platforms Set to Transform Enterprise Integration

The Future of AI Orchestration and Integration

As organizations adopt multiple AI agents and large language models (LLMs), managing and connecting these tools requires a dedicated orchestration and integration platform. This approach lets CIOs link AI capabilities across business functions, streamlining processes and reducing manual effort.

Consider the employee onboarding process. Soon, an HR representative could simply ask a chatbot to set up a new hire. Behind the scenes, that chatbot would coordinate with other AI agents to enroll the employee in payroll, guide them through health insurance options, and set up essential communication tools. Additional coordination might handle training delivery, building access, and equipment provisioning, all with minimal human involvement.

Some companies have already begun implementing such AI orchestration. Gartner predicts that by 2028, 70% of organizations deploying multi-LLM applications and AI agents will rely on integration platforms to optimize connectivity and data access. This is a sharp increase from fewer than 5% using these platforms in 2024.

Connecting Data and Decision-Makers

AI agents and LLMs need access to data and the ability to act on it to perform effectively. Without integration, agents remain isolated and limited in function. Orchestration tools bridge this gap, enabling AI components to communicate and execute tasks across enterprise systems.

CIOs face critical questions as AI deployment grows more complex: How can agents easily connect to data sources? How can IT teams monitor AI activity within the architecture? Orchestration platforms provide the necessary control and visibility to answer these challenges.

Today’s enterprises juggle multiple LLMs, legacy systems, and AI agents simultaneously. Without a unifying orchestration layer, this ecosystem risks becoming fragmented and inefficient. Beyond task execution, orchestration maintains standards around governance and trust, making it the vital link between data strategy and AI operations.

A Market Emerges

When selecting orchestration tools, CIOs should prioritize interoperability and AI-agnostic designs. Platforms that can integrate with any LLM or agent type offer flexibility and longevity. They also manage security, versioning, and data lineage behind the scenes, reducing operational risks.

The market for AI integration platforms is growing, with many startups offering ready-made solutions. Larger IT companies are likely to enter this space as demand increases. Some enterprises may build custom orchestration tools, but adapting existing API integrations often proves more efficient than starting from scratch.

Experimenting with Orchestration

IBM is an example of a company developing agent integration internally. After years of experimentation, IBM now uses AI agents across various workflows including sales, IT, and HR. Their HR agents simplify complex processes like salary adjustments and employee transfers, replacing confusing legacy systems with intuitive AI interactions.

An orchestration layer coordinates these agents to handle complex, multi-step workflows such as onboarding. While some organizations build orchestration in-house, vendor-supported tools offer a faster, lower-risk way to begin AI integration.

Starting with simple, high-impact processes like time-off requests can deliver immediate value and build organizational confidence. As adoption grows, AI agents will become integral to reducing process complexity and improving employee experience.

This shift means employees won't need to learn dozens of separate systems. Instead, they’ll interact with AI agents that streamline access, helping them get work done efficiently.