Orchestrating AI Agents into one System

AI Agents are multiplying across enterprise software, promising productivity gains but creating new risk of fragmentation. Will companies be able to orchestrate them into one coherent system? 

AI agents are rapidly spreading into nearly every corner of the enterprise – from frontline, customer-facing teams to deep back-office functions. Adoption isn’t uniform across departments, but the trajectory is clear: usage is scaling fast. Recent global surveys reveal a steep climb in generative AI deployment, with 71% of companies reporting regular use in at least one function by mid-2024, and close to 78% doing so by early 2025. 

A major driver of this surge is the embedding of AI copilots and assistants directly into enterprise software ecosystems, allowing employees to leverage AI in the flow of work without switching tools or contexts. Microsoft 365 Copilot, Salesforce’s Agentforce, SAP’s Joule, Notion AI, and ServiceNow’s AI Copilot are just a few examples of this universal trend. 

The breadth of adoption is striking. While sales, marketing, customer service, and operations lead the way, AI agents are gaining traction in HR, finance, procurement, and other support areas. Crucially, companies are moving beyond short-term pilot projects with limited ROI, increasingly embedding AI into core business processes to drive sustained improvements in operating profit. As capabilities mature and best practices spread, the vision of every department having an AI counterpart assisting in day-to-day work is moving from possibility to inevitability – raising the urgent question of how these agents will ultimately be orchestrated and managed at scale. 

Current landscape & challenge 

The rapid rollout of tool-specific AI agents by every major software vendor has created a new kind of complexity for enterprises: a fragmented ecosystem of assistants, each living in its own silo. Instead of one intelligent, company-wide aide, many organisations are ending up with a patchwork of mini-assistants – each with its own rules, strengths, and blind spots. 

Inconsistent user experience & capabilities 

Employees may now have one AI in their CRM, another in their HR system, another in their ERP or S2P platforms – each with a different interface, command style, and set of abilities. One agent might excel at drafting text, while another only handles analytics. This inconsistency not only overwhelms users, who may not know which assistant to turn to, but also means the most powerful capabilities are not uniformly available across the business. Some departments racing ahead, others left behind. The result: uneven adoption and uneven value creation. 

Training & onboarding overheads 

Multiple agents with different behaviours increase the learning curve for employees, who must master each system’s quirks, prompts, and workflows. For IT teams, the burden grows too – every new agent requires deployment, integration, governance, and maintenance. Fragmentation hurts scalability, and in highly siloed environments, employees may simply give up on using the AI tools altogether. In effect, the more scattered the AI experience, the higher the cost of adoption – and the lower the return. 

Vendor lock-in risk 

As AI agents become deeply embedded in enterprise workflows, the risk of vendor lock-in grows. Organisations that commit heavily to one provider’s AI ecosystem may find themselves dependent on that vendor’s models, connectors, and data architecture. While this can accelerate adoption in the short term, it limits flexibility to switch vendors or integrate best-in-class tools from elsewhere. Over time, the cost of switching – both financial and organisational – can become prohibitively high, reducing strategic leverage and ability to innovate. 

Data silos & lack of interoperability 

Perhaps the most fundamental challenge is that AI agents tied to individual applications lock intelligence inside platform-specific boundaries. Each agent typically sees only its own system’s data, making cross-system insights very challenging. Today, integration between agents is minimal: there’s no shared memory, no common context about the user, and no seamless hand-off of tasks. Valuable information, and critical decisions can remain trapped in a single-tool AI. 

Ironically, this creates a fragmentation paradox: Having a patchwork of mini assistants reinforces the very data fragmentation that years of digital transformation have worked to dismantle. 

The more AI agents an organisation deploys, the less cohesive and effective the overall experience becomes. Without a unified approach, companies risk higher total cost of ownership, reduced productivity, and uneven access to AI’s benefits. 

Towards orchestration and unified layers  

AI agents in enterprise software don’t just automate tasks – they open new possibilities for organisational intelligence. Solutions like IFS.AI demonstrate the power of combining internal performance data with anonymised global benchmarks, enabling companies to see how they perform against peers worldwide. This kind of embedded intelligence is transformative: it lives where employees already work, turning every interaction into an opportunity for insight. This is why the solution doesn’t lie within getting rid of embedded agents. 

But to address fragmentation, the future lies in orchestration and unification. The goal is not to replace vendor-built AI agents, but to create a central AI “front door” – a single entry point where employees can make any request, and the system routes it behind the scenes to the most capable agent. This is the shift from isolated bots to multi-agent orchestration: AI assistants working as a coordinated team. 

Alongside this back-end coordination, we can expect unified front-end interfaces – a consistent conversational surface across all AI-powered workflows. This eliminates the app-switching problem, reduces training overheads, and makes it easier to add new AI capabilities without cluttering the experience. 

In this emerging landscape, and for companies using the Microsoft Business Suite, Microsoft Copilot could become the default gateway to enterprise AI – much as Internet Explorer became the default gateway to the internet in the late 1990s. Like IE, Copilot’s advantage comes from distribution, trust, and extensibility. 

By being omnipresent, secure, and unified, Copilot could become the “browser” for enterprise AI — the place where any query starts, any task can be delegated, and any insight can be retrieved, regardless of the underlying system. 

The rapid rise of AI agents in enterprise software has unlocked enormous potential — but also created silos, inconsistency, dependence and user fatigue. The next stage of maturity will be about orchestration: unifying these agents under a consistent, interoperable, and user-friendly experience. Whether through platforms like Microsoft Copilot, specialised orchestration middleware, or an internally built “AI mesh,” enterprises that adopt a central AI front door will be able to scale AI’s benefits across the organisation while avoiding the fragmentation trap.