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CIOs Fear IoT Performance Will Become a Major Burden

Nearly three-quarters (74%) of IT leaders are concerned that Internet of Things (IoT) performance problems could directly impact business operations and significantly damage revenues, according to a new report, entitled Overcoming the Complexity of Web-Scale IoT Applications: The Top 5 Challenges, commissioned by Dynatrace

This is mostly because 78% of CIOs said there is a risk that their organization will roll-out IoT strategies without having a plan or solution in place to manage the performance of the complex cloud ecosystems that underpin IoT rollouts.

In fact, 69% of CIOs predicted that IoT will become a major performance management burden as they struggle to overcome the escalating complexity of their modern enterprise cloud environments.

“Businesses across every industry are eagerly exploring IoT’s potential to engage new markets, drive new revenue and create stronger competitive advantage,” said Dave Anderson, Digital Performance Expert at Dynatrace. “However, IoT ecosystems and delivery chains are intricate and boundless, which creates unprecedented frequency of change, size and complexity in the cloud environments on which they are built. Enterprises are already struggling to master cloud complexity and now IoT substantially magnifies this challenge.”

The report looks at the challenges organizations face in maintaining seamless software experiences as they continue to roll-out IoT ecosystems. Key findings include:

The scale of IoT is too big to manage in a traditional way

■ 73% of CIOs worry that the number of third-parties and internal resources involved in IoT service delivery chains will make it incredibly difficult to identify who is responsible when performance problems arise.

■ 52% of CIOs say understanding the impact that IoT platform providers and network operators have on performance is a key challenge to managing user experience in IoT.

■ 75% of CIOs are concerned that problems within the platform or network layer that impact the performance of their applications could be hidden from them by an IoT service provider.

It is impossible to master IoT complexity manually

■ 84% of CIOs believe that AI capabilities and the ability to automate most of the processes that support IoT deployments will play a crucial role in the success of their IoT strategies.

IoT is losing its ability to meet user expectations

■ 70% of CIOs worry that consumer and user expectations for faster, fault free experiences could soon increase beyond what IT teams are able to deliver.

■ 69% of CIOs fear losing control over the user experience as the IoT delivery chain continues to become more convoluted.

■ 64% of CIOs are worried that the spiraling numbers of wearables could soon make it impossible to manage mobile performance for such devices.

IoT creates new user experience headaches

■ Ensuring that IoT device firmware updates and security patches don’t have a negative performance impact (62%).

■ Having the ability to track application behaviour on IoT devices as they interact with cloud services (60%).

■ Understanding the impact of IoT device performance on the user-experience (53%).

■ Mapping the rapidly growing IoT ecosystem as it expands (38%).

“If IoT is to deliver on its promise, organizations can’t afford to be overwhelmed by the complexity issues it presents – which is exactly what happens if an enterprise is using a traditional monitoring approach,” adds Anderson. “Platform-specific tools and do-it-yourself solutions aren’t built for web-scale, highly dynamic, complex cloud environments – they leave you cobbling together a mix of solutions which will never add up to a sophisticated platform that gives you a complete view of your environment and automated way of making sense of everything real time.

“Organizations need a new approach that works at scale and simplifies IoT cloud complexity; a software intelligence platform that uses AI and automation to provide full operational insights into vast ecosystems of IoT sensors, devices, gateways, applications, and underlying infrastructure. With answers at their fingertips, rather than just more data on glass, organizations will be poised to enjoy the benefit from all that IoT technologies have to offer.”

Survey Methodology: The report is based on a global survey of 800 CIOs in large enterprises with over 1,000 employees, conducted by Vanson Bourne and commissioned by Dynatrace. The sample included 200 respondents in the US, 100 in the UK, France, Germany, and China, and 50 in Australia, Singapore, Brazil and Mexico respectively.

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CIOs Fear IoT Performance Will Become a Major Burden

Nearly three-quarters (74%) of IT leaders are concerned that Internet of Things (IoT) performance problems could directly impact business operations and significantly damage revenues, according to a new report, entitled Overcoming the Complexity of Web-Scale IoT Applications: The Top 5 Challenges, commissioned by Dynatrace

This is mostly because 78% of CIOs said there is a risk that their organization will roll-out IoT strategies without having a plan or solution in place to manage the performance of the complex cloud ecosystems that underpin IoT rollouts.

In fact, 69% of CIOs predicted that IoT will become a major performance management burden as they struggle to overcome the escalating complexity of their modern enterprise cloud environments.

“Businesses across every industry are eagerly exploring IoT’s potential to engage new markets, drive new revenue and create stronger competitive advantage,” said Dave Anderson, Digital Performance Expert at Dynatrace. “However, IoT ecosystems and delivery chains are intricate and boundless, which creates unprecedented frequency of change, size and complexity in the cloud environments on which they are built. Enterprises are already struggling to master cloud complexity and now IoT substantially magnifies this challenge.”

The report looks at the challenges organizations face in maintaining seamless software experiences as they continue to roll-out IoT ecosystems. Key findings include:

The scale of IoT is too big to manage in a traditional way

■ 73% of CIOs worry that the number of third-parties and internal resources involved in IoT service delivery chains will make it incredibly difficult to identify who is responsible when performance problems arise.

■ 52% of CIOs say understanding the impact that IoT platform providers and network operators have on performance is a key challenge to managing user experience in IoT.

■ 75% of CIOs are concerned that problems within the platform or network layer that impact the performance of their applications could be hidden from them by an IoT service provider.

It is impossible to master IoT complexity manually

■ 84% of CIOs believe that AI capabilities and the ability to automate most of the processes that support IoT deployments will play a crucial role in the success of their IoT strategies.

IoT is losing its ability to meet user expectations

■ 70% of CIOs worry that consumer and user expectations for faster, fault free experiences could soon increase beyond what IT teams are able to deliver.

■ 69% of CIOs fear losing control over the user experience as the IoT delivery chain continues to become more convoluted.

■ 64% of CIOs are worried that the spiraling numbers of wearables could soon make it impossible to manage mobile performance for such devices.

IoT creates new user experience headaches

■ Ensuring that IoT device firmware updates and security patches don’t have a negative performance impact (62%).

■ Having the ability to track application behaviour on IoT devices as they interact with cloud services (60%).

■ Understanding the impact of IoT device performance on the user-experience (53%).

■ Mapping the rapidly growing IoT ecosystem as it expands (38%).

“If IoT is to deliver on its promise, organizations can’t afford to be overwhelmed by the complexity issues it presents – which is exactly what happens if an enterprise is using a traditional monitoring approach,” adds Anderson. “Platform-specific tools and do-it-yourself solutions aren’t built for web-scale, highly dynamic, complex cloud environments – they leave you cobbling together a mix of solutions which will never add up to a sophisticated platform that gives you a complete view of your environment and automated way of making sense of everything real time.

“Organizations need a new approach that works at scale and simplifies IoT cloud complexity; a software intelligence platform that uses AI and automation to provide full operational insights into vast ecosystems of IoT sensors, devices, gateways, applications, and underlying infrastructure. With answers at their fingertips, rather than just more data on glass, organizations will be poised to enjoy the benefit from all that IoT technologies have to offer.”

Survey Methodology: The report is based on a global survey of 800 CIOs in large enterprises with over 1,000 employees, conducted by Vanson Bourne and commissioned by Dynatrace. The sample included 200 respondents in the US, 100 in the UK, France, Germany, and China, and 50 in Australia, Singapore, Brazil and Mexico respectively.

Hot Topics

The Latest

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

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 ...