Orchestration as a Strategic Asset: Beyond Chatbots and Point Solutions

By Seb Burrell

AI adoption has accelerated faster than most organisations can keep up with. Nearly every business I speak to has AI somewhere in play — a chatbot in customer service, a code assistant in IT, a marketing tool generating content. But most are still treating AI as a collection of point solutions rather than as an integrated capability.

It reminds me of the pre-cloud era, when enterprises had dozens of on-prem systems strung together with brittle integrations. Everything technically “worked,” but it was fragile, inconsistent, and painful to scale. The shift to the cloud wasn’t just about new technology; it was about centralisation, standardisation, and resilience. AI is at the same crossroads today.

The way forward isn’t to keep piling on more copilots and assistants. It’s orchestration.

From assistants to agents

We’ve moved past the era of rule-based assistants into something more powerful: agents that can plan, reflect, and collaborate. Multi-agent systems can now handle entire workflows, chaining specialised agents together.

But with that intelligence comes complexity. Without orchestration, you end up with a sprawl of disconnected agents — powerful individually, but collectively unmanageable.

The SaaS agent problem

There’s another dynamic at play: the silent arrival of domain-expert agents inside SaaS platforms.

Tools like HubSpot, Salesforce, ServiceNow, and others are all embedding their own copilots. One morning you log in, and a new AI button appears — with access to your CRM data, customer records, or internal workflows. These agents are introduced in isolation, often without explicit consent, and without consideration for how they align with your broader AI governance.

That’s not sustainable. Each SaaS platform is optimising for its own ecosystem, not for the coherence of your business. Without orchestration, you’re effectively outsourcing your AI strategy to whichever vendor moves fastest.

Governance through orchestration

This is where orchestration becomes critical. My recent work with IBM has shown how orchestration isn’t just about efficiency — it’s about governance.

flowchart TD
U[End Users] --> O[Orchestration Layer]

    O -->|Secure Access| A1[AI Agent - Chatbot]
    O -->|Secure Access| A2[AI Agent - CRM Copilot]
    O -->|Secure Access| A3[AI Agent - HR Assistant]
    O -->|Secure Access| A4[AI Agent - Analytics Bot]

    subgraph SaaS Systems
        A2
        A3
    end

    subgraph Governance
        G1[Audit Logs]
        G2[Access Controls]
        G3[Bias & Risk Checks]
    end

    O --> G1
    O --> G2
    O --> G3

By funnelling AI systems through a central platform, orchestration delivers:

Consolidated access — one secure entry point instead of dozens of ad hoc copilots.
Auditability by design — every query, decision, and action logged centrally.
Consistent guardrails — bias detection, access controls, and data usage policies applied across all agents.
Safe scaling — developers can innovate faster because compliance is built-in, not bolted on at the end.

It turns governance from a reactive scramble into a proactive enabler.

From experiments to adoption

The statistics are sobering: 95% of AI investments fail due to lack of integration and operational execution. Experiments stay experiments because they’re never joined up.

If AI is going to move beyond scattered pilots and shadow copilots hiding inside SaaS tools, orchestration has to be seen for what it is: a strategic asset, not a technical bolt-on. It’s the only way to scale AI responsibly, with the governance, auditability, and confidence that businesses — and regulators — now demand.

Orchestration as a Strategic Asset: Beyond Chatbots and Point Solutions

From assistants to agents

The SaaS agent problem

Governance through orchestration

More than an IT problem

From experiments to adoption