The AI infrastructure problem: Preparing healthcare networks for the next wave of intelligence

 Note: This is the third of three articles, sponsored by Spectrum Business®, examining how healthcare organizations can strengthen infrastructure resilience across an increasingly distributed care environment. The first article explored closing the gap between system recovery and clinical usability. The second examined the infrastructure demands of distributed care delivery. 

Major technology shifts in healthcare eventually became infrastructure challenges. EHR adoption strained networks and storage. Telehealth exposed gaps in bandwidth and latency. Cloud migration forced a reset on identity, security, and governance. 

AI in healthcare follows the same pattern, at greater speed and with arguably higher stakes. 

Ambient documentation tools are entering clinical workflows, predictive models are surfacing earlier warnings, and decision support is becoming more continuous and more embedded in care delivery. What was once experimental is becoming operational. 

Each of these applications is clinically promising. Each is also computationally intensive, latency-sensitive, and dependent on infrastructure that many health systems are still in the process of building. 

Bridging this gap requires moving beyond the algorithm to build a structural backbone capable of handling continuous, real-time data flows across distributed environments. By prioritizing automated operations, hybrid cloud standardization, and targeted governance, healthcare organizations can ensure their infrastructure delivers intelligence exactly where it is needed in modern care networks. 

When intelligence meets reality

Healthcare has already invested heavily in data infrastructure. Hybrid environments, integration platforms, and analytics pipelines are now common across organizations of all sizes. These investments have made AI possible but not necessarily reliable. 

AI workloads are different from the transactional workloads that healthcare networks were designed around: a clinician requests a record, the system retrieves it, and the interaction completes. The data volumes are bounded, the timing is request-driven, and a few seconds of latency is tolerable in most contexts. 

On the other hand, AI requires low-latency access to data, continuous processing across environments, and the ability to deliver insights at the moment they are needed. An ambient documentation tool listens, processes, and generates in real time. A patient monitoring model evaluates incoming data streams around the clock. No waiting to be triggered by request.  

When a real-time decision support model stalls, the window for its output to be clinically useful may close. These requirements stretch infrastructure in ways traditional systems were not designed to handle. 

This is where infrastructure becomes the limiting factor.  

The new performance requirement: Time-to-decision

Traditional infrastructure metrics focus on availability and throughput. AI introduces a different measure: time-to-decision. 

How quickly can data move from source to model? 
How quickly can a model generate insight?
How quickly can that insight reach the clinician and be acted on? 

Each step depends on infrastructure. 

Latency, network prioritization, identity workflows, and system integration all influence whether AI operates as intended. When these elements are not aligned, delays compound. 

AI rarely fails in obvious ways. Instead, it degrades: alerts arrive late, recommendations are missed, and workflows become inconsistent. Once trust declines, abandonment follows.  

Automate before you innovate

The 2025 Digital Health Most Wired (DHMW) Infrastructure findings offer a useful baseline. Most organizations have made progress on operational fundamentals: log collection is automated, monitoring dashboards are in place, and alerts are routed to the appropriate teams. The foundation for infrastructure visibility is largely established. 

The AI frontier is less mature. 

Fewer than one-third of Most Wired organizations have fully integrated AI into infrastructure alerting workflows, and that low adoption is consistent across organization sizes. This reflects deliberate sequencing choices more than resource gaps.  

The pattern is clear: Automate before you innovate.  

AI depends on clean, consistent telemetry. Models trained on incomplete or noisy infrastructure data produce unreliable outputs. Organizations with disciplined monitoring and alerting practices are better positioned to apply AI effectively. 

Thus, before asking which model to deploy, organizations are starting to lead with, “is our infrastructure instrumented well enough to support one?”  

Distributed intelligence requires distributed infrastructure

Most clinical AI deployments run in multiple locations, including centralized cloud environments, on-premises, near the edge where data is generated, or a hybrid combination. This mimics the trend in distributed care.  

As care extends across clinics, homes, and virtual environments, so too does intelligence. A remote monitoring program may depend on AI to detect early signs of deterioration. A hospital-at-home model may rely on real-time alerts. A clinician may interact with AI-assisted decision support within the EHR.  

Each scenario depends on infrastructure that can move data, process it, and deliver insights consistently across environments that were not designed to operate as one. 

Hybrid cloud as the AI platform

Most AI deployments in healthcare run in hybrid environments. 

Inference often occurs at the edge, where latency matters. Training and large-scale processing occur in the cloud, where compute resources scale. Data flows continuously between the two. 

Hybrid cloud strategies now dominate across virtually every healthcare organization size, according to the Most Wired findings. Nearly all very large systems operate active hybrid models. Smaller organizations are moving in the same direction, often beginning with specific workloads like DR or analytics before expanding. 

What varies dramatically is how well that hybrid environment is managed. Organizations that apply consistent identity, monitoring, security, and incident response practices across cloud and on-premise environments operate with fewer gaps. Those that manage each environment separately introduce fragmentation. 

For AI workloads, that fragmentation has direct consequences. 

A model that performs reliably in a cloud environment may behave differently when its outputs must traverse an inconsistent network to reach a clinician. Variability in latency, access, or policy can alter how — and whether — insights are delivered. 

Standardizing hybrid operations is now an AI requirement.  

The last mile of intelligence

AI inherits the same challenge as any clinical capability: the last mile. An insight is only valuable if it reaches the clinician in a usable form, within the workflow, and without delay. 

Connectivity determines whether it arrives, identity determines whether it can be accessed, and integration determines whether it fits into the workflow. If any of these elements lag, the system may function technically, but not clinically.  

This is the next evolution of the infrastructure gap explored earlier in this series: system availability and delivering care anywhere remain essential, but delivering intelligence exactly when it is needed is paramount.  

One enabler of on-time intelligence is the shift from infrastructure’s role in supporting applications to supporting decision delivery. 

Infrastructure determines whether insights arrive on time, whether workflows remain efficient, and whether clinicians trust the systems they rely on. It connects distributed environments, prioritizes competing demands, and enables intelligence to operate within real-world constraints. 

This is already emerging in organizations moving from AI pilots to operational use.  

Governance enables scale

The Most Wired findings consistently identify governance as the strongest predictor of infrastructure maturity. That pattern holds as organizations move into AI. 

AI introduces new dependencies between data, infrastructure, and clinical workflows. It also introduces new operational questions: how models are validated, how performance is monitored, and how outputs are governed in clinical use. These require coordination across IT, clinical leadership, and the executive team. 

Organizations with established governance frameworks are better positioned to scale AI without introducing fragmentation. Most Wired found those investing 15 to 20 percent of their IT budgets in infrastructure demonstrate measurably higher resilience. Without that discipline, complexity grows faster than capability. 

The infrastructure imperative

Across this series, a consistent pattern has emerged. Systems can be technically available but clinically unusable.
Care can extend beyond the hospital faster than infrastructure can support it. AI can generate insight that never reaches the point of care. In each case, the gap is not conceptual but infrastructural. 

Closing that gap requires the same disciplines: 

• Strong governance 
• Consistent hybrid operations  
• Reliable, intelligent connectivity across environments  
• A focus on operational fundamentals before layering in complexity  

AI is the future already underway. While sophisticated models will continue to push AI in healthcare, the organizations that succeed will be defined by the strength of the infrastructure that carries those models. 

About Spectrum Business

Spectrum Business empowers healthcare organizations to transform the patient experience with networking, security, communications, collaboration and TV solutions. Our certified healthcare IT solutions experts serve 90% of the largest health systems in the US with a network engineered for exceptional performance, end-to-end accountability and 100% US-based support, available 24/7.  For more information, visit  https://www.spectrum.com/business/enterprise/solutions/industries/healthcare.