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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 The organization asked my company Windward Consulting to implement an AIOps strategy that would help streamline its outdated and unwieldy IT system management.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

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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

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

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 ...