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Safeguard Healthcare Innovations with AIOps - Part 1

Sean McDermott
Windward Consulting Group

You've likely heard it before: every business is a digital business. That saying is increasingly true; however, some businesses and sectors digitize more quickly than others. Healthcare has traditionally been on the slower side of digital transformation and technology adoption, but that's changing.

A vast majority (81%) of worldwide healthcare executives say digital transformation is accelerating within their organizations, according to Accenture's Digital Health Technology Vision report. And 93% say they're innovating with urgency.

Even as a regular consumer, healthcare's digital transformation isn't hard to see. Readily available telehealth platforms connect patients and providers for more convenient and accessible healthcare services. And some caregivers use innovations like wearable devices to track patient health from afar.

Technology is also working behind the scenes to modernize healthcare organizations. Smart technologies can help this data-heavy industry modernize its internal systems, providing fast, accurate information on things like the number of available beds, patient status and supply inventory. Artificial intelligence (AI) can also do the heavy lifting typically involved in mundane administrative processes like paying bills, maintaining patient databases and scheduling appointments.

Many digital-first healthcare organizations are finding a new challenge as they embrace technology: businesses must guarantee seamless digital experiences. In other words, they must keep their apps and services always on and performing well.

Too often, there is so much attention on buying or developing apps and services that sustaining these innovations is overlooked. But system downtime can be costly for any business and particularly harmful for those in the healthcare industry.

As healthcare organizations roll out innovations at increasing velocity, they must build a long-term strategy for how they will maintain the uptime of their critical apps and services. And there's only one tool that can ensure this continuous availability in our modern IT ecosystems. Artificial Intelligence for IT Operations (AIOps) can help IT Operations teams ensure the uptime of critical apps and services.

How AIOps Helps Healthcare Systems

There's a common misconception: someone flips the technology "on" switch, and apps and services stay online and operate at peak performance. Unfortunately, this isn't the case.

Human IT Operations teams must operationalize healthcare technologies and then maintain them to provide reliable systems for patients, providers and other employees. After all, the systems, applications and networks behind the technologies that make our lives easier tend to be complex.

Maintaining uptime in these complex environments rides on monitoring and catching service-disrupting incidents before they impact the user. But humans can no longer perform this monitoring and detection alone because there is simply too much data for humans to process. Our modern systems prove too layered, interdependent and ephemeral. On top of this complexity, modern systems also spit out so much data that it's virtually impossible for human operators to handle, no matter how big the IT team.

The cadence of technology deployments and updates also makes uptime assurances more challenging. The very changes to the production environment that provide bigger and better technologies can also spark the service interruptions that lead to outages.

In short, the IT Operations teams charged with maintaining our modern IT ecosystems need help. And that's where AIOps comes in.

AIOps helps IT Operations teams simplify the management of ever complex and evolving IT systems. Through big data, automation and machine learning, a holistic AIOps solution detects potential service-disrupting incidents, identifies the problem and provides teams with the actionable insights they need to fix the problem — and keep it from happening again. If implemented correctly, these tools also automate the entire incident workflow to enable rapid responses that can catch issues early, before they impact the business.

Preventing outages is essential, but AIOps tools provide another advantage. With time added back into their days, engineering teams can focus on developing the kind of innovations that deliver tangible business value.

Go to: Safeguard Healthcare Innovations with AIOps - Part 2

Sean McDermott is the Founder of Windward Consulting Group and RedMonocle

Hot Topics

The Latest

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Safeguard Healthcare Innovations with AIOps - Part 1

Sean McDermott
Windward Consulting Group

You've likely heard it before: every business is a digital business. That saying is increasingly true; however, some businesses and sectors digitize more quickly than others. Healthcare has traditionally been on the slower side of digital transformation and technology adoption, but that's changing.

A vast majority (81%) of worldwide healthcare executives say digital transformation is accelerating within their organizations, according to Accenture's Digital Health Technology Vision report. And 93% say they're innovating with urgency.

Even as a regular consumer, healthcare's digital transformation isn't hard to see. Readily available telehealth platforms connect patients and providers for more convenient and accessible healthcare services. And some caregivers use innovations like wearable devices to track patient health from afar.

Technology is also working behind the scenes to modernize healthcare organizations. Smart technologies can help this data-heavy industry modernize its internal systems, providing fast, accurate information on things like the number of available beds, patient status and supply inventory. Artificial intelligence (AI) can also do the heavy lifting typically involved in mundane administrative processes like paying bills, maintaining patient databases and scheduling appointments.

Many digital-first healthcare organizations are finding a new challenge as they embrace technology: businesses must guarantee seamless digital experiences. In other words, they must keep their apps and services always on and performing well.

Too often, there is so much attention on buying or developing apps and services that sustaining these innovations is overlooked. But system downtime can be costly for any business and particularly harmful for those in the healthcare industry.

As healthcare organizations roll out innovations at increasing velocity, they must build a long-term strategy for how they will maintain the uptime of their critical apps and services. And there's only one tool that can ensure this continuous availability in our modern IT ecosystems. Artificial Intelligence for IT Operations (AIOps) can help IT Operations teams ensure the uptime of critical apps and services.

How AIOps Helps Healthcare Systems

There's a common misconception: someone flips the technology "on" switch, and apps and services stay online and operate at peak performance. Unfortunately, this isn't the case.

Human IT Operations teams must operationalize healthcare technologies and then maintain them to provide reliable systems for patients, providers and other employees. After all, the systems, applications and networks behind the technologies that make our lives easier tend to be complex.

Maintaining uptime in these complex environments rides on monitoring and catching service-disrupting incidents before they impact the user. But humans can no longer perform this monitoring and detection alone because there is simply too much data for humans to process. Our modern systems prove too layered, interdependent and ephemeral. On top of this complexity, modern systems also spit out so much data that it's virtually impossible for human operators to handle, no matter how big the IT team.

The cadence of technology deployments and updates also makes uptime assurances more challenging. The very changes to the production environment that provide bigger and better technologies can also spark the service interruptions that lead to outages.

In short, the IT Operations teams charged with maintaining our modern IT ecosystems need help. And that's where AIOps comes in.

AIOps helps IT Operations teams simplify the management of ever complex and evolving IT systems. Through big data, automation and machine learning, a holistic AIOps solution detects potential service-disrupting incidents, identifies the problem and provides teams with the actionable insights they need to fix the problem — and keep it from happening again. If implemented correctly, these tools also automate the entire incident workflow to enable rapid responses that can catch issues early, before they impact the business.

Preventing outages is essential, but AIOps tools provide another advantage. With time added back into their days, engineering teams can focus on developing the kind of innovations that deliver tangible business value.

Go to: Safeguard Healthcare Innovations with AIOps - Part 2

Sean McDermott is the Founder of Windward Consulting Group and RedMonocle

Hot Topics

The Latest

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...