Skip to main content

Safeguard Healthcare Innovations with AIOps - Part 2

Sean McDermott
Windward Consulting Group

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.

Start with: Safeguard Healthcare Innovations with AIOps - Part 1

Case Study: Healthcare Modernization with AIOps

Recently, a regional healthcare organization employing 24,000 people and serving 7 million patients across 350 locations wanted to retire its legacy monitoring tools and adopt AIOps. The organization asked my company Windward Consulting to implement an AIOps strategy that would help streamline its outdated and unwieldy IT system management.

Our team's AIOps implementation process helped this client and can help others in the industry too. Here's what my team did:

1. Articulate the vision.

AIOps use cases are wide-ranging, and the technology's benefits can affect an entire healthcare organization. Cross-departmental stakeholders should collaborate with the IT function (or a third-party consultant) to detail a holistic vision for the technology.

2. Connect the vision to a strategy

Too often, IT teams get inspired by tools and purchase a solution before building a strategy. To maximize the potential of AIOps, project stakeholders should examine their vision and build a comprehensive strategy around how they will realize that vision.

3. Select the tools

Once project stakeholders define the vision and strategy, they can start making tactical technology decisions. The group can examine the organization's existing tool stack, determining if these solutions meet project needs or if new technology is required.

4. Execute the strategy

With the foundational elements in place, AIOps project stakeholders from across the organization can execute the strategy's measurable steps. Of course, the steps will be different for every person involved. Executives have largely strategic deliverables like making technology investments, determining priorities and recruiting talent. IT teams, on the other hand, may take incremental steps toward deploying the AIOps tool or work in conjunction with an AIOps consultant.

5. Reinforce AIOps adoption

Organizations sometimes prematurely declare AIOps victory, neglecting the change management and hands-on training necessary to realize optimal results. Stakeholders should develop a comprehensive training plan for end-users at all skill levels, while executives should work on a change management strategy that encourages long-term technology adoption.

6. Continue engagement and adoption efforts

Project stakeholders should continuously check in with users to uncover pain points, find skills gaps worthy of additional training and measure progress. And broadcasting the measurable outcomes and lessons learned can continue enthusiasm around the capability for artificial intelligence (AI)- and machine learning (ML)-enabled innovations. Consistently communicating with the organization's workforce can shore up support for technology expansion and further investment.

These 6 steps to AIOps implementation helped our healthcare client streamline the monitoring of its complicated IT system, enabling more uptime. And replacing disparate legacy tools with one holistic AIOps strategy helped the client save money and allowed its workforce to focus its efforts on innovation.

Technological innovations have the power to transform healthcare and improve patient outcomes; however, these innovations require constant monitoring to ensure continuous availability and peak performance. And humans can't do it alone. Healthcare organizations that want to delight users with innovative technology must provide IT teams with the AIOps tools needed to keep these innovations running smoothly.

Sean McDermott is the Founder of Windward Consulting Group and RedMonocle

Hot Topics

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Safeguard Healthcare Innovations with AIOps - Part 2

Sean McDermott
Windward Consulting Group

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.

Start with: Safeguard Healthcare Innovations with AIOps - Part 1

Case Study: Healthcare Modernization with AIOps

Recently, a regional healthcare organization employing 24,000 people and serving 7 million patients across 350 locations wanted to retire its legacy monitoring tools and adopt AIOps. The organization asked my company Windward Consulting to implement an AIOps strategy that would help streamline its outdated and unwieldy IT system management.

Our team's AIOps implementation process helped this client and can help others in the industry too. Here's what my team did:

1. Articulate the vision.

AIOps use cases are wide-ranging, and the technology's benefits can affect an entire healthcare organization. Cross-departmental stakeholders should collaborate with the IT function (or a third-party consultant) to detail a holistic vision for the technology.

2. Connect the vision to a strategy

Too often, IT teams get inspired by tools and purchase a solution before building a strategy. To maximize the potential of AIOps, project stakeholders should examine their vision and build a comprehensive strategy around how they will realize that vision.

3. Select the tools

Once project stakeholders define the vision and strategy, they can start making tactical technology decisions. The group can examine the organization's existing tool stack, determining if these solutions meet project needs or if new technology is required.

4. Execute the strategy

With the foundational elements in place, AIOps project stakeholders from across the organization can execute the strategy's measurable steps. Of course, the steps will be different for every person involved. Executives have largely strategic deliverables like making technology investments, determining priorities and recruiting talent. IT teams, on the other hand, may take incremental steps toward deploying the AIOps tool or work in conjunction with an AIOps consultant.

5. Reinforce AIOps adoption

Organizations sometimes prematurely declare AIOps victory, neglecting the change management and hands-on training necessary to realize optimal results. Stakeholders should develop a comprehensive training plan for end-users at all skill levels, while executives should work on a change management strategy that encourages long-term technology adoption.

6. Continue engagement and adoption efforts

Project stakeholders should continuously check in with users to uncover pain points, find skills gaps worthy of additional training and measure progress. And broadcasting the measurable outcomes and lessons learned can continue enthusiasm around the capability for artificial intelligence (AI)- and machine learning (ML)-enabled innovations. Consistently communicating with the organization's workforce can shore up support for technology expansion and further investment.

These 6 steps to AIOps implementation helped our healthcare client streamline the monitoring of its complicated IT system, enabling more uptime. And replacing disparate legacy tools with one holistic AIOps strategy helped the client save money and allowed its workforce to focus its efforts on innovation.

Technological innovations have the power to transform healthcare and improve patient outcomes; however, these innovations require constant monitoring to ensure continuous availability and peak performance. And humans can't do it alone. Healthcare organizations that want to delight users with innovative technology must provide IT teams with the AIOps tools needed to keep these innovations running smoothly.

Sean McDermott is the Founder of Windward Consulting Group and RedMonocle

Hot Topics

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...