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8 Action Points for IT Leaders to Deliver Innovation in a Sustainable Way

Gregg Ostrowski
AppDynamics

Across all industries, the speed of innovation continues to soar, as deployment of no code and low platforms enables IT teams to accelerate application release velocity. In the latest research from Cisco AppDynamics, The Age of Application Observability, the majority of technologists predict that increased adoption of these cloud native technologies will deliver applications at least four times over the coming years.

Cloud native technologies are already delivering game-changing benefits to many organizations, providing the agility and resilience to respond effectively to constantly evolving customer needs and enabling hybrid work. And they create a platform for brands to deliver ever more intuitive and personalized digital experiences to consumers, building loyalty and engagement and opening up new revenue streams.

However, with IT teams all over the world concerned because of the intense pressure they are now facing, there is now a growing realization that many organizations simply can't sustain the current pace of innovation; not unless they adopt new approaches and working practices within their IT departments and implement new tools to help technologists manage an increasingly complex and fragmented IT estate.

Currently, IT teams don't have the tools and visibility they need to manage availability and performance across hybrid environments, with no clear line of sight for applications where components are running across both cloud native and on-premises technologies. This is making it impossible for teams to rapidly troubleshoot issues and significantly raising the potential for disruption and downtime to customer-facing applications.

For all the effort and investment that organizations are directing towards digital transformation and cloud migration, they're now at risk of not being able to maximize their returns, because technologists aren't able to deliver the seamless digital experiences that customers now demand at all times.

In the study, technologists acknowledge the need for urgent change within the IT department to better manage application performance, and they point to eight key action points for all organizations to establish a more sustainable approach to innovation:

1. Application observability across hybrid environments

78% of technologists state that the increased volume of data from multi-cloud and hybrid environments is making manual monitoring impossible. And this is why, more than anything else, technologists point to application observability across hybrid environments as important for their organization to deliver accelerated and sustainable innovation.

Rather than using separate monitoring tools across on-premises and cloud native technologies, application observability provides IT teams with unified visibility across their entire IT estate. This means they can easily detect issues, understand root causes and remediate issues in a timely way, bringing down metrics such as Mean Time To Resolution (MTTR). As many as 97% of technologists now see a critical need for their organization to move from a monitoring approach to an application observability solution to manage their hybrid environment.

2. Strategic technology partners

64% of technologists admit that they find it difficult to differentiate between application observability and monitoring solutions. And this lack of clarity in the market is slowing down buying decisions and implementation timelines. Therefore, technologists recognize the need to lean on the expertise and consultancy of trusted partners to ensure they select the right application observability solution for their business. Critically, this means finding a tool which serves its purpose now but also supports and eases their journey towards cloud native technologies over the coming years.

3. Unified teams

Within many organizations, the introduction of cloud native technologies (and the associated new teams) has created major siloes between people, processes, and data within the IT department. New teams such as Site Reliability Engineer (SRE) and CloudOps are working in isolation from legacy teams looking after on-premises technologies, and security teams aren't being integrated into the application development lifecycle until the very last minute.

Technologists recognize the need for much closer collaboration between teams to manage hybrid environments. With application observability in place, all IT teams can come together around a single source of truth and adopt new approaches such as DevSecOps, where security has input into development from day one.

4. Shared vision and execution plans

Linked to the above, IT leaders need to develop and communicate a shared vision for all technologists, as opposed to each team or discipline working towards their own individual goals. They need to inspire technologists around their innovation plans at an organization-wide level and incentivize the entire IT department to achieve common goals. Encouragingly, 88% of technologists state that they are open to sharing KPIs with other teams.

5. Fully skilled team

Recruiting and developing the right IT skills to deliver digital transformation remains a major challenge for all organizations, and this is being accentuated by rapid adoption of cloud native technologies which require very specialist skills.

IT leaders need to develop creative and far-reaching strategies to engage with new talent pools, as well as focusing on upskilling programs for existing employees. Crucially, IT leaders need to ensure that all technologists are widening their skill sets, and expanding their knowledge and appreciation of other disciplines in order to operate within cross-functional teams.

6. Leadership buy-in for new approaches and tools to manage performance

Unfortunately, 71% of technologists state that leaders within their organization do not fully understand that modern applications need modern approaches and tools to manage availability, performance and security. That needs to change in order for IT teams to get the budget and senior sponsorship required to implement new tools and affect cultural change.

Technologists need to build robust business cases for application observability approaches and solutions, highlighting the role that application observability needs to play in enabling brands to meet customer expectations for seamless digital experiences. Crucially, technologists need to demonstrate how application observability must be the foundation for sustainable innovation and for organizations to compete and differentiate in the market.

7. Modern metrics to link IT performance to business transactions

Most IT departments don't have the insights they need to measure the impact of IT on the business. They're still deploying separate monitoring tools across their IT estate so can't get a complete picture to link IT data with business metrics.

Technologists are therefore looking for application observability solutions which allow IT teams to correlate IT data with business metrics. The ability to access business transaction insights in real-time, and then analyze them in business-level dashboards, is vital for teams to prioritize issues based on severity and potential impact on customers and the business. Technologists can focus their efforts in the right places to maximize their impact.

8. Ability to validate investment in cloud native technologies

Within all sectors, IT leaders report that they're coming under increasing pressure to demonstrate the value that their innovation initiatives — and cloud investment in particular — are bringing to the business. This pressure is likely to intensify as economic conditions continue to be challenging and organizations look to streamline costs.

Application observability enables technologists to track, measure and report on the impact that their innovation programs are generating. IT leaders can make insight-driven decisions on where to focus investments based on what will have the biggest benefit for customers, employees, and ultimately, the business. In this way, application observability is essential for organizations to reap the full benefits of their accelerated innovation programs, and to build on their current momentum.

Gregg Ostrowski is CTO Advisor at Cisco AppDynamics

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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 ...

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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 ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

8 Action Points for IT Leaders to Deliver Innovation in a Sustainable Way

Gregg Ostrowski
AppDynamics

Across all industries, the speed of innovation continues to soar, as deployment of no code and low platforms enables IT teams to accelerate application release velocity. In the latest research from Cisco AppDynamics, The Age of Application Observability, the majority of technologists predict that increased adoption of these cloud native technologies will deliver applications at least four times over the coming years.

Cloud native technologies are already delivering game-changing benefits to many organizations, providing the agility and resilience to respond effectively to constantly evolving customer needs and enabling hybrid work. And they create a platform for brands to deliver ever more intuitive and personalized digital experiences to consumers, building loyalty and engagement and opening up new revenue streams.

However, with IT teams all over the world concerned because of the intense pressure they are now facing, there is now a growing realization that many organizations simply can't sustain the current pace of innovation; not unless they adopt new approaches and working practices within their IT departments and implement new tools to help technologists manage an increasingly complex and fragmented IT estate.

Currently, IT teams don't have the tools and visibility they need to manage availability and performance across hybrid environments, with no clear line of sight for applications where components are running across both cloud native and on-premises technologies. This is making it impossible for teams to rapidly troubleshoot issues and significantly raising the potential for disruption and downtime to customer-facing applications.

For all the effort and investment that organizations are directing towards digital transformation and cloud migration, they're now at risk of not being able to maximize their returns, because technologists aren't able to deliver the seamless digital experiences that customers now demand at all times.

In the study, technologists acknowledge the need for urgent change within the IT department to better manage application performance, and they point to eight key action points for all organizations to establish a more sustainable approach to innovation:

1. Application observability across hybrid environments

78% of technologists state that the increased volume of data from multi-cloud and hybrid environments is making manual monitoring impossible. And this is why, more than anything else, technologists point to application observability across hybrid environments as important for their organization to deliver accelerated and sustainable innovation.

Rather than using separate monitoring tools across on-premises and cloud native technologies, application observability provides IT teams with unified visibility across their entire IT estate. This means they can easily detect issues, understand root causes and remediate issues in a timely way, bringing down metrics such as Mean Time To Resolution (MTTR). As many as 97% of technologists now see a critical need for their organization to move from a monitoring approach to an application observability solution to manage their hybrid environment.

2. Strategic technology partners

64% of technologists admit that they find it difficult to differentiate between application observability and monitoring solutions. And this lack of clarity in the market is slowing down buying decisions and implementation timelines. Therefore, technologists recognize the need to lean on the expertise and consultancy of trusted partners to ensure they select the right application observability solution for their business. Critically, this means finding a tool which serves its purpose now but also supports and eases their journey towards cloud native technologies over the coming years.

3. Unified teams

Within many organizations, the introduction of cloud native technologies (and the associated new teams) has created major siloes between people, processes, and data within the IT department. New teams such as Site Reliability Engineer (SRE) and CloudOps are working in isolation from legacy teams looking after on-premises technologies, and security teams aren't being integrated into the application development lifecycle until the very last minute.

Technologists recognize the need for much closer collaboration between teams to manage hybrid environments. With application observability in place, all IT teams can come together around a single source of truth and adopt new approaches such as DevSecOps, where security has input into development from day one.

4. Shared vision and execution plans

Linked to the above, IT leaders need to develop and communicate a shared vision for all technologists, as opposed to each team or discipline working towards their own individual goals. They need to inspire technologists around their innovation plans at an organization-wide level and incentivize the entire IT department to achieve common goals. Encouragingly, 88% of technologists state that they are open to sharing KPIs with other teams.

5. Fully skilled team

Recruiting and developing the right IT skills to deliver digital transformation remains a major challenge for all organizations, and this is being accentuated by rapid adoption of cloud native technologies which require very specialist skills.

IT leaders need to develop creative and far-reaching strategies to engage with new talent pools, as well as focusing on upskilling programs for existing employees. Crucially, IT leaders need to ensure that all technologists are widening their skill sets, and expanding their knowledge and appreciation of other disciplines in order to operate within cross-functional teams.

6. Leadership buy-in for new approaches and tools to manage performance

Unfortunately, 71% of technologists state that leaders within their organization do not fully understand that modern applications need modern approaches and tools to manage availability, performance and security. That needs to change in order for IT teams to get the budget and senior sponsorship required to implement new tools and affect cultural change.

Technologists need to build robust business cases for application observability approaches and solutions, highlighting the role that application observability needs to play in enabling brands to meet customer expectations for seamless digital experiences. Crucially, technologists need to demonstrate how application observability must be the foundation for sustainable innovation and for organizations to compete and differentiate in the market.

7. Modern metrics to link IT performance to business transactions

Most IT departments don't have the insights they need to measure the impact of IT on the business. They're still deploying separate monitoring tools across their IT estate so can't get a complete picture to link IT data with business metrics.

Technologists are therefore looking for application observability solutions which allow IT teams to correlate IT data with business metrics. The ability to access business transaction insights in real-time, and then analyze them in business-level dashboards, is vital for teams to prioritize issues based on severity and potential impact on customers and the business. Technologists can focus their efforts in the right places to maximize their impact.

8. Ability to validate investment in cloud native technologies

Within all sectors, IT leaders report that they're coming under increasing pressure to demonstrate the value that their innovation initiatives — and cloud investment in particular — are bringing to the business. This pressure is likely to intensify as economic conditions continue to be challenging and organizations look to streamline costs.

Application observability enables technologists to track, measure and report on the impact that their innovation programs are generating. IT leaders can make insight-driven decisions on where to focus investments based on what will have the biggest benefit for customers, employees, and ultimately, the business. In this way, application observability is essential for organizations to reap the full benefits of their accelerated innovation programs, and to build on their current momentum.

Gregg Ostrowski is CTO Advisor at Cisco AppDynamics

Hot Topics

The Latest

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 ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.