• An orchestration layer operating across systems, data sources and workflows is essential to unlock the business value promised by enterprise AI agents
• Human-AI hybrid workflows are necessary for successful agent orchestration to ensure governance, data integrity and adoption
• UNRVLD is building enterprise-grade AI orchestration platforms and agents for organisations, turning them into first-to-market industry leaders

AI has arrived inside the tools your teams already use. Email drafts itself. CRM platforms spawn agents. Corporate software vendors are bolting generative capability onto their core platforms. Productivity gains are real and immediate, but tool-by-tool adoption is not a strategy. Without deliberate enterprise AI orchestration across systems, data and workflows, each deployment operates in isolation. The gap between organisations where AI is embedded in tools and those where AI is orchestrated as infrastructure is where enterprise value is actually created — or quietly lost.

The 2026 Mulesoft Connectivity Benchmark Report found that 50% of AI agents currently operate in isolated silos. When agents are disconnected from each other, and your business systems, data and goals, their ability to unlock the efficiency and capability promises of enterprise AI are greatly reduced.

Exploiting the full potential of AI requires enterprise orchestration; coordinating agents so they work intelligently together across infrastructure, data sources and workflows to drive measurable, business-focused outcomes. UNRVLD is working with enterprise organisations to define their AI orchestration strategies and enable the level of digital maturity required to effectively execute their AI roadmaps.

Below we explain what we’ve learnt in the past 18 months about why agent orchestration is critical to an enterprise AI strategy.

Why an agent orchestration platform matters

Fragmented AI agents can boost individual productivity but will struggle to bring meaningful value across businesses. Purposeful transformation leveraging orchestration allows agentic systems to co-ordinate and deliver within business-critical operations.

That’s why an agent orchestration platform is required. It understands the context and nuance of your business and initiatives, and is guided by your team’s inputs to build and deploy agents with frameworks and guardrails that you set.

Gartner predicts that by 2028, at least 15% of everyday work decisions will be made autonomously through agentic AI, up from virtually zero in 2024. Organisations that build the right AI orchestration foundations now will be positioned to realise value sooner.

Digital experience platform providers such as Optimizely are rising to this challenge, with its Opal orchestration platform and pre-built agents to support entire marketing and digital content processes. One example of this is for SEO and GEO.

As Optimizely VP Product Nazanin Ramezadi explains in her 2026 LinkedIn post, Optimizely has built six specialist agents in Opal to support SEO teams, with more to come soon. They include agents to make GEO and technical SEO recommendations, write and implement web page metadata and schemas, write FAQs and review a site for content compliance and freshness.

This level of agent orchestration supports teams to identify issues and gaps in their website’s SEO and GEO performance and, crucially, automatically improve it. Multiple agents can run in parallel, for instance to automatically optimise a new site page.

As we’ve started to work with clients on their adoption of platforms such as Opal for full-scale orchestration we’ve determined the primary factors which are crucial to successful implementation: robust infrastructure, trusted data and a culture that supports and governs how AI operates.

Measuring AI maturity: UNRVLD’s framework for agentic adoption

To help enterprises scale AI in a logical and sustainable manner, UNRVLD’s approach begins by evaluating the organisation’s AI maturity. Our AI readiness framework assesses not just your ability to adopt and scale AI, but also the opportunities available to create smarter operations and elevated customer experiences.

The framework identifies where you are today, the quick wins available now and a clear path for scaling AI adoption – while keeping your teams informed and your systems and data safe.

The framework maps your business operation against the five critical AI enablers we have identified:

• ‍Strategy and vision: Alignment between organisational goals and AI initiatives
• People and culture: Team capabilities, roles and attitudes towards AI
• Processes and workflows: Readiness to safely implement AI across the organisation
• ‍Data and infrastructure: The quality, availability and governance of your data
• ‍Tools and technologies: The infrastructure needed to support AI implementation – including appropriate orchestration platforms for different departments

From there, we map how agents can be designed to work together to operate entire workflows, identifying the highest-value use cases for a business across three key solution areas:

• ‍Conversational AI – systems that can process tasks autonomously through natural language communication (customer support, sales prospecting, recruitment chatbots, internal help desks)
• ‍AI automation - automating repetitive and time-consuming processes (data entry and processing, marketing automation, segmentation and personalisation, content and campaign creation)
• ‍AI business intelligence – analysing data to deliver insights that enhance enterprise operational performance (predictive analytics, customer insights, reporting autmation, experimentation and optimisation).

As our Chief Growth Officer Tom Dougherty explains in his write-up of Vercel’s March 2026 AI event, most office-based workers are not yet adequately enabled to build and deploy agents to drive efficiencies within their roles:

“Building agents remains a step removed for most of the workforce at present. Democratisation is an ambition for the teams pushing for wider adoption, but it feels like there is still work to do on the tooling to encourage wider usage.”

At the same time, governance is crucial once you remove the technical barriers to building and deploying agents. Tom Dougherty adds: “Removing the technical barrier doesn't remove the need to think clearly about the problem you're trying to solve. If anything, it makes that discipline more important, because the barrier to building the wrong thing has effectively disappeared. Use case definition and investing the right amount of time in the thinking still has to come before reaching for the tools.”

Building enterprise AI: a B2B commerce case study

UNRVLD’s work within the commerce space includes our teams developing AI solutions to support online merchants to Sell and Run better. Most recently we have built a customer-facing AI agent for one of our B2B commerce clients, utilising conversational AI: to improve customer experience whilst driving customer service efficiencies.

Before building the agent, we worked with the client to understand its wider AI orchestration strategy: and ensured the platform we deployed could handle the requirements surfaced within the use case.

The business sells thousands of products in a complex category, and the drive was to be the first-to-market with a next-generation B2B online buying experience, offering personalised recommendations with the agent acting as a product expert. When a customer logs in, the agent references their location, industry and account profile to offer an individually tailored product catalogue with real-time product inventory and customised pricing.

Built on Azure AI Foundry, the product expert agent is more sophisticated than a Q&A-based chatbot. It connects to a selection of resources including CRM, stock and pricing data, brand tone-of-voice rules and a set of operational and commercial guardrails controlled by the client.

To counter concerns on data integrity and hallucinations we’ve architected a private retrieval-augmented generation (RAG) framework, meaning the agent queries only the client’s trusted and approved data sources from its back-end systems.

Human oversight of the agentic system is crucial so an evaluation framework dashboard is in place. This sits on top of the orchestration layer, giving administrators a full view of inputs, outputs, logs and conversations in a secure environment.

This is the first step in an AI roadmap for the client, with expanded customer services capabilities and commercial modelling already in development.

Where to start: plan, pilot, scale

Core business systems likely don’t need to be replaced to begin your enterprise AI orchestration journey. Most large enterprises are already operating with DXP, ERP and CRM systems, plus potentially ecommerce engines and custom integrations. The goal is to add an orchestration layer that allows agents to work intelligently and securely across your existing technology stack.

A practical starting point is to review the critical data points that feed your most important business processes: identify where AI can add the most value and build from there. We suggest a three-step approach that doesn’t overwhelm teams but allows for scalability.

• ‍Plan – Identify high-value use cases aligned to business outcomes‍
• Pilot – Launch in a controlled environment, with governance and measurement in place‍
• Scale – Expand orchestration across workflows once impact is proven

Leading enterprises are already moving beyond pilot projects to create appropriate top-down tech leadership and structures needed to scale AI effectively. For example, large real estate companies such as Landsec are appointing dedicated technology leaders and setting up formal AI test labs to bridge the gaps between experimentation and strategic AI adoption.

Management consulting firms are also beginning to surface stories of complete business process redesign projects that have been enabled by the adoption of end-to-end agentic solutions, including a B2B manufacturer which has shaved 30% off staffing costs for its quote-to-order process with this bolder approach.

Governance, data and cultural: what makes or breaks agent orchestration

Scaling AI as part of your digital transformation requires careful consideration of governance, data integrity and adoption.

The most effective AI implementations are human-AI hybrid workflows. Having an evaluation framework and human-led guardrails that the AI adheres to is critical.

It’s also important to recognise that AI doesn’t fix underlying data problems. In fact, executing an agent orchestration strategy can amplify existing data issues. If your data architecture is fragmented, inconsistent or poorly governed, agents will inherit those limitations. The set-up must be robust enough for humans to know exactly how, and where data is moving between agents.

And even the most advanced AI strategy will underperform if teams are not prepared to embrace it. Adoption and team training through structured onboarding, workshops and proof-of-concept reviews ensures maximum value from early AI deployments while building internal confidence alongside technical capability.  

At UNRVLD, we work with enterprises on orchestration strategy, governance and the cultural change needed to make it sustainable. From AI readiness assessments to focused half-day AI acceleration workshops that help define a practical proof of concept, get in touch to speak to one of our experts and understand how we can accelerate your AI adoption journey.

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