The 7 Hidden Frictions Slowing Down AI in Healthcare
How composable architecture helps you move past them, faster.
Introduction: Why AI in Healthcare Still Feels Stuck
Across the healthcare sector, there’s no shortage of ambition when it comes to artificial intelligence. From predictive diagnostics and personalized treatment plans to automated admin and supply chain optimization, the vision is expansive. Yet for many organizations, execution lags far behind the hype.
AI adoption is no longer a question of if, but why not yet? Across the industry, promising proofs of concept have struggled to scale. Despite growing investment and leadership support, results remain fragmented, and often underwhelming.
This isn’t due to a lack of talent or imagination. It’s because of friction— deeply embedded barriers in healthcare’s systems, workflows, and governance. These frictions quietly undermine AI initiatives, preventing them from moving beyond isolated pilots or tightly scoped experiments.
What’s needed isn’t just smarter algorithms, but smarter architecture. Composable architecture—a modular, decoupled approach to digital systems—offers a structural way to accelerate AI readiness without tearing down what’s already in place. It enables real-time data flows, flexible integrations, and regulatory safeguards that make AI not only possible, but practical.
Below, we unpack the seven most persistent frictions and explore how composability helps healthcare leaders clear the path.
1. Legacy Systems and Monolithic Architectures
Many healthcare providers and life sciences organizations still operate on rigid, all-in-one platforms. These monolithic systems are notoriously difficult to update, integrate, or scale—and that’s a serious problem for AI, which depends on fast, flexible access to diverse data sources and processes.
Composable architecture replaces these brittle foundations with modular components. This allows organizations to modernize incrementally, integrating AI capabilities as needed without rebuilding entire systems. Instead of shoehorning AI into outdated platforms, composability lets you plug intelligence into the exact places where it can drive value.
2. Fragmented and Delayed Data
AI needs real-time data to deliver real-world impact, especially in healthcare settings where timing is critical. But many organizations rely on batch processing or isolated data lakes that don’t connect well across systems. By the time AI models receive the information, it's already out of date—or incomplete.
A composable approach solves this by enabling event-driven architectures and real-time APIs. Systems can publish and subscribe to events as they happen, ensuring that AI agents operate on live, contextual data. This responsiveness is essential for applications like adaptive scheduling, personalized outreach, or inventory optimization in high-demand environments.
3. Siloed Workflows and Tooling
Even when AI is introduced into healthcare environments, it often gets trapped in departmental silos—unable to interact across billing, clinical operations, procurement, or marketing. This limits its effectiveness and reinforces fragmented workflows.
Composable systems are designed to integrate and orchestrate tools across domains. They provide a unifying layer that makes it easier to deploy AI agents that work collaboratively across departments. Whether it’s automating prior authorization or improving patient onboarding, composable architecture ensures AI doesn’t just solve one problem, but connects the dots.
4. Regulatory and Compliance Bottlenecks
Healthcare’s strict regulatory environment can slow down innovation. Many organizations are hesitant to adopt AI because of the potential compliance risks—especially when dealing with patient data, insurance processes, or product recommendations.
Composable solutions reduce this risk by embedding compliance into the architecture. For example, pre-built HIPAA-compliant modules and audit-ready infrastructure — such as those offered by Orium’s Industry Solution for Health & Pharma Commerce— allow teams to move fast without compromising safeguards. These industry-specific components help organizations innovate confidently, without starting from scratch or risking non-compliance.
5. Lack of Governance for Agentic Systems
AI systems, especially autonomous agents, introduce new governance challenges. Who’s accountable when an agent makes a recommendation? How do you audit decision logic after the fact?
Composable architecture makes AI governance more manageable by enabling modular oversight. Each component can be governed individually, and changes can be tracked and rolled out incrementally. This aligns with responsible AI principles, supporting transparency, explainability, and human-in-the-loop decision-making.
6. Organizational Change Resistance
Healthcare is a high-stakes, risk-averse industry. Many teams are understandably cautious about adopting AI, especially if it requires dramatic changes to how they work. Unfortunately, rigid systems reinforce that caution by making any change feel disruptive.
Composable systems shift the dynamic. Because they support iterative change and modular experimentation, they make it easier to introduce AI in ways that are low-risk and aligned with existing workflows. Instead of an all-or-nothing transformation, composability enables a path of steady evolution—building trust along the way.
7. One-Off Innovation Instead of Scalable Strategy
Too often, healthcare AI efforts are launched as isolated proofs of concept. They might demonstrate potential, but they don’t scale because the underlying systems weren’t designed for growth. What’s missing is an architectural strategy that supports long-term, enterprise-wide adoption.
Composable thinking flips the script. It encourages organizations to build with reuse, extensibility, and long-term agility in mind. This strategic posture sets the stage for what’s next: agentic transformation, where AI agents aren’t just tools, but teammates embedded in everyday operations.
Conclusion: A Smarter Foundation for AI in Healthcare
If AI in healthcare feels like it’s stuck, it’s because of hidden frictions—not lack of ambition. From outdated infrastructure and data delays to regulatory hurdles and organizational inertia, the barriers are real—but they’re not permanent.
Composable architecture offers a way forward. It creates a flexible, future-ready foundation that supports real-time data, modular governance, and scalable AI integration. And with industry-specific solutions like Orium’s Industry Solution for Health & Pharma Commerce, healthcare leaders don’t have to choose between innovation and compliance.
The opportunity is clear and the cost of waiting is growing. If we want AI to deliver on its promise in healthcare, we need to build for it, structurally. Composability doesn’t just clear the path. It changes the terrain.
Leigh Bryant
Editorial Director, Composable.com
Leigh Bryant is a seasoned content and brand strategist with over a decade of experience in digital storytelling. Starting in retail before shifting to the technology space, she has spent the past ten years crafting compelling narratives as a writer, editor, and strategist.