Insights
Building for AI at scale: How modern engineering unlocks real healthcare impact
By Sai Gade, General Manager – DevOps, SRE & Platform Engineering at UST and Lucas Warren, Sr. Product Marketing Manager
These AI agents continuously analyze observability data to suggest optimizations, flag emerging risks, and even trigger automated responses—such as rolling back faulty deployments or reallocating resources during peak loads.
By Sai Gade, General Manager – DevOps, SRE & Platform Engineering at UST and Lucas Warren, Sr. Product Marketing Manager
AI success begins with a strong engineering foundation
Artificial intelligence is becoming central to healthcare innovation, influencing everything from clinical prediction models to population health strategies. Yet many healthcare organizations struggle to convert AI potential into clinical or operational outcomes. According to Bessemer Venture Partners, only around 30% of AI pilots progress into production, as security, data readiness, integration costs, or a lack of internal expertise hinder most.
Too often, AI is treated as a standalone innovation initiative. However, successful enterprise-scale AI outcomes depend on core capabilities that sit beneath the surface. Reliable delivery pipelines, continuous integration and deployment, resilient systems and real-time visibility are the key differences between AI that works in a lab and AI that makes a measurable impact on clinical operations or member experience.
Healthcare CIOs are now tasked with bridging that gap. The answer begins with how teams build, test, deploy, and operate software. AI is no longer just a data science problem; it is a software delivery challenge. Organizations that recognize this will move faster and with greater confidence.
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Engineering practices that accelerate AI
Site reliability engineering and observability
Site reliability engineering (SRE) brings engineering discipline to operations, ensuring that systems, including AI-driven applications, are dependable and scalable. In healthcare, where system downtime can delay care or compromise safety, SRE helps teams manage complexity, reduce incidents and maintain performance targets under pressure.
For example, Cleveland Clinic’s adoption of SRE practices resulted in a 40% reduction in critical incidents and a 60% decrease in resolution time (Cleveland Clinic & Google Cloud Case Study). These improvements directly support safer and more responsive healthcare services, which is especially important when AI systems are integrated into care coordination or clinical workflows.
Observability complements this effort. It empowers healthcare organizations to gain deep, real-time visibility into the behavior of complex, distributed systems across business processes, infrastructure and the software development lifecycle (SDLC). This visibility is essential for transforming AI from a promising concept into a reliable, scalable and impactful solution.
Business observability enables leaders to align system performance with operational and clinical outcomes by surfacing insights into user behavior, process efficiency, and service-level adherence. For Business Operations teams, this means faster decision-making, improved SLA compliance, and reduced operational risk. In healthcare, observability provides real-time visibility into the Claims and Enrollment Lifecycles—helping teams detect delays, identify root causes of claim denials or enrollment drop-offs and ensure smoother, more responsive workflows that directly impact member satisfaction and reimbursement timelines.
Equally as important, business observability introduces critical controls and balances. By continuously monitoring key metrics and correlating them with system behavior, it ensures that processes are not only efficient but also auditable and compliant. This transparency supports governance, enables proactive issue resolution and reinforces accountability across teams—empowering healthcare organizations to scale AI-enabled services with confidence, consistency and measurable impact.
A recent Cisco report found that healthcare organizations with strong observability practices reduced their incident response time by 61% and improved clinician-facing application uptime by over 30% (Cisco Full-Stack Observability in Healthcare). Observability-driven insights empowered CIOs to improve cross-functional collaboration, enabling faster decision-making on critical issues System observability in healthcare ensures the reliability and performance of critical digital infrastructure that supports clinical, operational and AI-driven workflows. It provides real-time telemetry across applications, services, and infrastructure, enabling teams to detect anomalies, trace root causes and resolve issues before they impact patient care or compliance. Whether it’s a latency spike in an EHR system or a failure in a claims processing engine, observability helps pinpoint the exact source—accelerating resolution and minimizing disruption. When paired with a strong SDLC ethos, observability reinforces non-functional requirements (NFRs) such as reliability, recoverability, performance and security—ensuring that systems are not only functional but also resilient and compliant by design.
Agentic AI further elevates system observability by serving as an intelligent layer for recommendations and autonomous remediation. These AI agents continuously analyze observability data to suggest optimizations, flag emerging risks, and even trigger automated responses—such as rolling back faulty deployments or reallocating resources during peak loads. This reduces manual intervention, shortens recovery time, and strengthens adherence to service level objectives (SLOs). In healthcare, where uptime and data integrity are paramount, this combination of observability, SDLC discipline, and AI-driven automation enables organizations to scale safely, respond faster and deliver high-quality care with confidence.
Internal developer platforms and reusable pipelines
The ability to move fast without breaking things is essential. Internal developer platforms (IDPs) streamline software delivery by providing self-service access to standardized tools, environments, and templates—enabling teams to build and deploy consistently at scale. In healthcare, where AI features often require coordination across engineering, data science, and compliance teams IDPs reduce friction and accelerate the delivery of solutions. The integration of agentic AI takes this further by enabling hyper-automation across the entire Software Development Life Cycle (SDLC). These intelligent agents can auto-generate reusable pipeline components, enforce non-functional requirements (NFRs), validate policy compliance and recommend optimizations in real time. This not only boosts developer productivity but also ensures that every release adheres to enterprise-grade standards, minimizing risk, reducing rework and enabling faster and safer deployment of AI-enabled healthcare solutions. A 2022 Forrester report found that organizations implementing IDPs saw up to a 25% increase in developer productivity and a 35% reduction in deployment-related incidents (CNCF: The Business Impact of Internal Developer Platforms).
Reusable pipelines further streamline delivery. Instead of recreating workflows for each new model or product, organizations can leverage pre-built components that handle tasks such as model validation, policy enforcement, testing, and deployment. These reusable patterns reduce cycle time, improve consistency and allow developers to focus on solving business problems rather than reinventing delivery mechanisms.
UST’s platform, PACE (Platform for Accelerated Cloud Engineering), is built on this premise. It brings together secure CI/CD pipelines, cloud-native tooling, AI observability, and SDLC enforcement into a unified platform designed to support scalable AI software initiatives. At one health insurer, this approach reduced product delivery times by 15% and enabled shared governance across the organization.
AI agents embedded within PACE further amplify this impact by automating key development and operational workflows—boosting developer productivity by over 30%. These agents assist in generating pipeline-as-code templates tailored to project needs, automatically generating Terraform modules for infrastructure provisioning and offering intelligent recommendations during the build and deployment stages. For example, suppose a deployment fails due to a misconfigured environment variable or a policy violation. In that case, the agent can not only identify the root cause but also suggest or trigger a remediation action—such as rolling back the change, adjusting the configuration or re-running the pipeline with corrected parameters. This reduces manual effort, accelerates recovery, and ensures consistent, policy-compliant delivery at scale.
AI-powered development and autonomous operations
AI is increasingly embedded within the engineering process itself. Development teams now use generative AI to accelerate coding, automatically generate tests, or suggest improvements. In one GitHub Copilot study, developers using AI assistance completed tasks 55% faster than those working manually. These tools free up time and improve quality, especially in large-scale environments where small efficiencies compound quickly.
In operations, AI is enabling new levels of automation. Agentic systems can act autonomously within defined boundaries to diagnose issues, resolve incidents, and manage routine system tasks. In an SRE context, an autonomous agent might detect a spike in error rates, trace it to a recent deployment and roll back the change without human intervention.
Companies like Dynatrace have deployed autonomous remediation tools that reduce mean time to resolution (MTTR) by as much as 90% in high-volume environments. These capabilities are particularly relevant in healthcare, where real-time responsiveness and safety are of paramount importance.
Agentic AI should be understood not only as an outcome but as an operational capability. These systems shift the way engineering and operations teams interact with infrastructure and software, allowing healthcare organizations to scale without a corresponding increase in manual effort.
Infrastructure as the foundation
Although deliver and engineering are integral focus points, solid infrastructure remains fundamental. AI cannot scale without secure, scalable and interoperable systems. Cloud platforms provide the elasticity to support AI workloads, while hybrid environments offer flexibility. Secure data pipelines ensure that sensitive health information is protected throughout the process.
Infrastructure is the runway for scalable AI in healthcare, but automation, compliance, and standardization are what make it operationally viable. UST’s PACE platform addresses this through its Infraflow agentic framework, which automates the entire journey from a bill of materials (BoM) to fully provisioned, compliant environments. The workflow begins with an AI agent interpreting the (BoM) to generate cloud cost estimates, followed by the auto-generation of cloud architecture diagrams and dependency blueprints that align with enterprise and healthcare-specific standards.
The agent then produces hardened, policy-compliant Terraform scripts to provision infrastructure across dev, staging, and production environments. This seamless orchestration ensures environments are secure, auditable, and aligned with regulatory frameworks like HIPAA—while dramatically reducing setup time and manual effort. By embedding policy-as-code and automating end-to-end infrastructure provisioning, Infraflow enables healthcare organizations to scale AI initiatives with speed, consistency and confidence.
However, infrastructure is not the differentiator. Many organizations have made investments in cloud and data platforms, but without disciplined engineering practices on top, those investments remain underutilized.
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Making it all work
Healthcare leaders are no longer just exploring AI; they’re tasked with operationalizing it. That means going beyond pilots and algorithms to build the infrastructure, processes and trust required to deploy AI safely and effectively.
Site reliability engineering ensures that clinical and administrative AI tools perform consistently in live environments. Observability provides real-time visibility into systems that impact care. Standardized delivery pipelines and internal developer platforms reduce friction, enabling faster deployment of tools that support both providers and patients. AI-powered development accelerates iteration, while agentic operations reduce cognitive load for IT teams and increase system resilience.
Infrastructure—cloud platforms, secure data flows, and hybrid architectures—provide the runway – but successful AI deployment depends on a critical but often overlooked factor: a mature, modern software engineering foundation. The organizations that succeed with AI are not those with the best ideas; they are those with the best ability to operationalize them. Success comes from getting solutions to patients and providers at scale.
This is the shift taking place across healthcare IT. AI is moving from promise to practice. The CIOs and digital leaders who invest in enterprise readiness—secure systems, scalable processes, and a culture of continuous delivery—won’t just adopt AI. They’ll deliver it at scale, with confidence and with real clinical and operational impact.
Ready to move beyond AI pilots and start delivering real impact?
Discover how UST’s engineering-first approach—built on observability, SRE, internal developer platforms, and agentic AI—can help your healthcare organization scale AI with speed, safety and confidence.