What Is an Orchestration Platform?

Why “Platform” Has Become a Central Question 

The word platform now appears in almost every conversation about healthcare AI, yet it is often used to describe very different approaches.

Earlier in adoption, that ambiguity mattered less. Organisations were focused on testing individual applications and establishing clinical value, rather than examining the structures sitting underneath them.

As deployment broadens, however, the environment changes. Introducing more than one application into a clinical setting brings integration, coordination and support considerations that do not arise when tools are evaluated in isolation. At that point, some form of shared structure becomes necessary to reduce complexity and ongoing operational effort

This shift reflects a more deliberate phase of adoption. Rather than asking only what AI can do, healthcare organisations are increasingly examining how AI is delivered at scale. Requirements of platforms continue to increase as AI becomes part of routine clinical and operational use across the healthcare enterprise.

What an Orchestration Platform Actually Is

As the term platform has become more widely used, it has also come to describe a broad range of solutions that behave very differently in practice. Clarifying what does not constitute a platform is an important step in understanding what organisations are evaluating as AI adoption grows.

A Platform Is Something You Can Build On

In practical terms, a platform is defined less by what it delivers out of the box and more by what it enables others to do on top of it.

At its core, a platform provides a foundation that supports the development, deployment and ongoing evolution of applications. Rather than being limited to a fixed set of tools, it is designed to be extended, allowing new capabilities to be added without rebuilding core functionality each time.

This is typically reflected in the presence of well-defined interfaces and services, such as APIs and software development kits, alongside the documentation and tooling required to use them effectively. These elements make it possible for additional applications, whether developed by partners, customers or internal teams, to be built and run consistently within the same environment.

Crucially, this also includes the ability to host and manage applications over time. Versioning, updates, monitoring, workflow and lifecycle management are handled at the platform level, providing a stable base on which applications can evolve without introducing unnecessary fragmentation or operational overhead.

Many solutions can be deployed and used successfully in isolation. A platform, by contrast, is intentionally designed to support extension.

Shared Capabilities That Remove Duplication

A key characteristic of a platform is that it centralises capabilities that would otherwise be rebuilt within every individual application.

Many of the requirements associated with deploying AI at scale, such as security, access control, auditing and traceability, apply regardless of the specific clinical use case. When handled at the application level, these capabilities must be implemented repeatedly, increasing complexity and the risk of inconsistency across deployments.

A platform approach provides these functions once, at a shared layer. This includes managing authentication and access, tracking system activity, and maintaining consistent oversight of how data and results are handled. Applications running on the platform can rely on these capabilities rather than recreating them independently.

The same principle applies to interoperability and data flows, where a shared approach reduces the need for separate integrations as additional applications are introduced.

By centralising these shared services platforms reduce duplication, improve consistency and lower the operational burden of scale. This allows organisations to expand AI use without introducing fragmentation or unnecessary cost.

From Single Solutions to Platform Thinking

Early healthcare AI adoption was shaped by very practical priorities. Organisations were focused on moving quickly, testing individual AI applications and establishing whether they could deliver clinical value in real-world settings.

In that context, standalone solutions made sense. Individual tools were easier to evaluate and faster to deploy, particularly when adoption was centred on a specific use case. The focus was on performance and immediate impact, rather than how solutions might interact, be managed collectively or operate over time.

As adoption expands, the environment changes. Multiple applications are introduced across departments and workflows, often from different vendors and with different operational and workflow requirements. What begins as a set of useful tools can in time become additional effort and a growing operational burden for clinical and IT teams.

It is at this stage that platform thinking emerges. Once complexity is experienced attention shifts from individual applications to how they coexist, how they are managed together and how AI is supported as part of a broader clinical and enterprise infrastructure.

Open Platform vs Closed Ecosystem

The term platform is often used to describe approaches that differ in important ways, but broadly, these fall into two models:

Closed Ecosystems

Closed ecosystems are typically built around a single vendor offering a tightly controlled set of applications and capabilities. In this model, the vendor defines what tools are available, how they are integrated and how the system evolves over time.

This approach can simplify early decision-making by providing a single, end-to-end solution with clearly defined responsibility. For some organisations and use cases, that level of control can be appealing.

However, closed ecosystems are inherently limited in how they can be extended. Introducing additional applications, whether from third parties or developed internally, can be difficult, particularly when those tools fall outside the vendor’s own portfolio. Over time, this can reduce flexibility as organisational needs change, and may increase switching costs if a health system later decides to introduce alternative technologies.

Open Platforms

Open platforms are designed around a different premise. Rather than centring on a single vendor’s applications, they provide a foundation that supports a broader ecosystem of tools and contributors.

In this model, the platform itself focuses on shared services and infrastructure, while applications, whether developed by partners, customers or internal teams, operate on top of it. This vendor-neutral approach is intended to support coexistence rather than exclusivity, allowing organisations to combine third-party AI with internally developed models within the same operational environment.

A key feature of this approach is extensibility. Open platforms are built to accommodate change, enabling new applications to be added without requiring a redesign of the underlying system. This becomes increasingly relevant as health systems experiment, innovate and evolve their use of AI over time.

Open Does Not Mean Risky

Openness is sometimes interpreted as a lack of control. In practice, the opposite is often true.

Open platforms rely on clearly defined standards to enable interoperability and governance at scale. This typically includes the use of non-proprietary data formats, recognised terminologies and transparent interfaces that define how applications interact with the platform and with each other.

By establishing predictable structures for data access, integration and oversight, this standards-based approach supports consistency and trust. Rather than introducing uncertainty, openness provides a disciplined framework within which multiple applications can operate reliably, securely and in line with organisational requirements.

Putting Enterprise Platforms to the Test

As AI becomes more embedded across clinical environments, the meaning of enterprise becomes more specific, and more demanding.

Organisations are no longer evaluating AI solely on individual application performance. Attention shifts to how solutions operate within existing enterprise environments, and whether they can meet the practical realities of large, complex health systems.

Enterprise expectations now centre on a small set of baseline operational requirements: robust security and governance, flexibility across different deployment environments and visibility over time into what is deployed and how it behaves in practice.

In this context, enterprise is less a label and more a test. Platforms are assessed on their ability to meet these operational realities consistently, not just at procurement, but in day-to-day use as adoption grows.

Supporting the Build-Your-Own AI Reality

Alongside commercial AI adoption, many healthcare organisations are increasingly developing AI models internally. These may be created for research, clinical trials, operational improvement or to address use cases not yet met by commercially available third-party tools.

As internal development grows, expectations around how these models are deployed and managed also change. Health systems are not looking to run internally developed AI in isolation, but to apply the same standards of oversight, traceability and operational consistency used for commercial applications. In practice, this means internal models need to be versioned, monitored and governed alongside third-party AI within a shared environment with results, performance data and audit trails handled consistently regardless of where a model originates.

This is where platform design becomes particularly important. Supporting internally developed AI requires an approach that can accommodate different model sources while maintaining a single operational framework. Platforms built to support extension and standardised data handling are better positioned to integrate internal innovation without fragmenting oversight or governance.

As AI use continues to expand, the ability to support both commercial and internally developed models within the same structure reflects how healthcare organisations are actually innovating today and highlights why flexibility is becoming a core platform requirement rather than a future consideration.

The Commercial and Operational Case for Platforms

As AI adoption expands, the commercial and operational implications of how solutions are delivered become more significant.

Deploying AI at scale requires sustained investment in capabilities such as security, compliance, integration, workflow and ongoing support. As the number of applications grows, these responsibilities accumulate. When they sit at the level of individual tools, effort and cost are repeated across deployments, increasing operational overhead over time.

A platform approach changes where that effort sits. By centralising shared capabilities, platforms absorb much of the underlying complexity, allowing applications to focus on delivering clinical value rather than rebuilding infrastructure. This reduces duplication and simplifies deployment, support and governance as adoption grows.

At scale, this shapes total cost of ownership, reinforcing the importance of consistency, shared services and long-term manageability.

What Will Matter More

As healthcare AI becomes part of routine clinical infrastructure, expectations are sharpening. The focus is moving away from isolated proof points toward characteristics that support sustained, real-world use.

What will matter more is transparency. Not just whether an AI model performs well in controlled conditions, but how it behaves in practice, how results are generated, how performance can be understood over time and how that behaviour can be monitored across workflows and applications.

Durability is also becoming a differentiator. Platforms are being evaluated on their ability to support multiple applications, accommodate emerging model types, scale as adoption grows and evolve without disruption. In this context, trust is built less through claims or one-off demonstrations, and more through consistency and how reliably systems integrate, operate and adapt over time.

What a Platform Is Not

A single AI application, even one that delivers significant clinical value, is not a platform. Many applications are designed to perform a specific task well and do so successfully. However, they are typically built to be used as standalone tools rather than as foundations others can build on or extend.

Similarly, a collection of applications from a single vendor does not automatically become a platform. In some cases, multiple tools may be delivered through a shared interface or purchasing model, but that alone does not imply the presence of shared services, extensibility, or the ability to support additional applications beyond those provided by the vendor itself.

The same distinction applies to solutions positioned as turnkey or single source. These approaches can simplify early deployment by offering a tightly controlled, end-to-end solution. What they do not necessarily provide is flexibility, particularly when organisations want to introduce new applications, integrate third-party tools, or support internally developed AI alongside commercial models.

These distinctions are not value judgements. Tools, bundled solutions and single-source offerings all have a role to play. The key point is that usefulness alone does not define a platform. What matters is whether a solution is designed to support extension, integration and evolution over time, particularly as AI moves from isolated use cases into broader, enterprise-wide deployment.

Orchestration Platforms as Long-Term Infrastructure

Platforms are not short-term technical choices. They are infrastructure decisions that shape how AI is introduced, governed and sustained over time.

Clear definitions help create that confidence. When organisations understand what a platform is designed to do, and what it is not, they are better placed to adopt AI in ways that are sustainable, scalable and aligned with long-term clinical and operational goals.

At Blackford, this view of orchestration platforms reflects how we think about long-term adoption in practice. Our focus has been on building infrastructure that supports choice, scale and evolution over time, enabling organisations to deploy, manage and grow AI in ways that align with real clinical and operational needs. As healthcare AI continues to mature, we believe clarity around what platforms are, and what they are designed to support, will be central to building confidence, trust and sustainable value.