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90% Experience Barriers to IoT

One in four Global Fortune 2000 enterprises rank Internet of Things (IoT) deployment as the most important initiative in their organization, yet 90% experience barriers to effective implementation and expansion due to lack of IoT expertise and skills in-house, according to a new independent survey from VansonBourne sponsored by Software AG.

98% of respondents stated that they are already generating some level of return from their IoT investments yet 89% still believe that they must improve their approach to IoT to further improve ROI.

Most respondents report achieving specific business benefits from their early IoT implementations with 39% citing production capacity increases and higher customer satisfaction and 38% making better informed business decisions. Respondents with the highest rate of satisfaction with their IoT deployments used a hybrid implementation approach – namely buying an IoT platform from an external vendor and then building upon it with customization done in-house, giving them the flexibility, they require within the framework of a reliable platform.

According to the survey, the biggest barrier to effective IoT implementation is lack of internal expertise and skills according to 31% of respondents. Other barriers include the inability to manage and process large volumes of data (29%), integration issues (28%), too many legacy systems (28%), inability to scale the network to meet IoT demands (26%) and cybersecurity challenges (25%).

The majority of respondents (93%) employed some level of hybrid approach to IoT implementation — both buying an IoT platform from an external vendor and then adding customization internally. When evaluating IoT platform vendors, respondents stated that they are looking for an innovative partner (46%), with a solution that an be easily integrated across an entire organization (40%), with customization that suits their specific needs (39%).

“Organizations know they cannot deliver IoT with off-the-shelf packaged applications or with ground-up in-house builds,” said Raj Datta, President, Software AG North America. “They need to rapidly deploy IoT in way that is easy to customize, open enough to seamlessly integrate with their existing landscape while remaining proven, secure and robust. This points to the need for a platform with rich functionality out-of-the-box that still gives customers the ability to quickly create business solutions to suit their unique needs.”

Datta pointed out that 72% of survey respondents feel their IoT platform vendor could and should be doing more to help them. And, they want to realize better IoT platform functionality with 49% seeing high reliability and 48% seeking enhanced security as two key attributes they want from their external vendor IoT platforms. Furthermore, as more organizations seek carrier-grade IoT platforms, they will continue to depend on external vendors to achieve that level of stealth functionality and reliability.

98% of respondents stated that all business processes ... expect to be positively impacted by IoT

Most organizations are still in the early stages of their IoT implementations which explains why 89% of survey respondents believe that their organization needs to improve its plans for IoT. At the same time, there is tremendous optimism and confidence regarding how their IoT implementations can transform their organizations and make them more competitive in the future. 98% of respondents stated that all business processes and functions within their organizations have been or expect to be positively impacted by IoT. Those functions include customer service/relationships, product/service delivery, supply chain management, marketing/sales, product/service development, employee/HR processes, infrastructure management, contact center/support, accounting/finance and administration.

Regarding IoT deployment in 2019 and beyond, most organizations are looking to deploy IoT on the edge but are struggling to do so — 80% of respondents want to deploy IoT on the edge but only 8% are actually doing that today. By combining IoT and edge computing, organizations can shift the workload of processing IoT data closer to, or in some cases on the device itself. For example, instead of sending all the data from a wind turbine to the cloud and processing the data centrally, users can process data and analytics locally and then send the results to the cloud. This reduces network load, cloud processing and storage requirements while making IoT in areas without reliable networks possible.

According to market research firm IDC, the IT spend on edge infrastructure will reach up to 18% of the total spend on IoT infrastructure by 2020. That spend is driven by the deployment of converged IT and OT systems which reduces the time to value of data collected from their connected devices.

Methodology: The survey queried 800 senior IT and business decision makers at organizations with a global annual revenue of $500M and higher across 13 different countries in North America, Europe and Asia Pacific. The respondents came from organizations that were deploying IoT platforms and the respondents themselves had some level of involvement and responsibility for selecting and maintaining those solutions.

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90% Experience Barriers to IoT

One in four Global Fortune 2000 enterprises rank Internet of Things (IoT) deployment as the most important initiative in their organization, yet 90% experience barriers to effective implementation and expansion due to lack of IoT expertise and skills in-house, according to a new independent survey from VansonBourne sponsored by Software AG.

98% of respondents stated that they are already generating some level of return from their IoT investments yet 89% still believe that they must improve their approach to IoT to further improve ROI.

Most respondents report achieving specific business benefits from their early IoT implementations with 39% citing production capacity increases and higher customer satisfaction and 38% making better informed business decisions. Respondents with the highest rate of satisfaction with their IoT deployments used a hybrid implementation approach – namely buying an IoT platform from an external vendor and then building upon it with customization done in-house, giving them the flexibility, they require within the framework of a reliable platform.

According to the survey, the biggest barrier to effective IoT implementation is lack of internal expertise and skills according to 31% of respondents. Other barriers include the inability to manage and process large volumes of data (29%), integration issues (28%), too many legacy systems (28%), inability to scale the network to meet IoT demands (26%) and cybersecurity challenges (25%).

The majority of respondents (93%) employed some level of hybrid approach to IoT implementation — both buying an IoT platform from an external vendor and then adding customization internally. When evaluating IoT platform vendors, respondents stated that they are looking for an innovative partner (46%), with a solution that an be easily integrated across an entire organization (40%), with customization that suits their specific needs (39%).

“Organizations know they cannot deliver IoT with off-the-shelf packaged applications or with ground-up in-house builds,” said Raj Datta, President, Software AG North America. “They need to rapidly deploy IoT in way that is easy to customize, open enough to seamlessly integrate with their existing landscape while remaining proven, secure and robust. This points to the need for a platform with rich functionality out-of-the-box that still gives customers the ability to quickly create business solutions to suit their unique needs.”

Datta pointed out that 72% of survey respondents feel their IoT platform vendor could and should be doing more to help them. And, they want to realize better IoT platform functionality with 49% seeing high reliability and 48% seeking enhanced security as two key attributes they want from their external vendor IoT platforms. Furthermore, as more organizations seek carrier-grade IoT platforms, they will continue to depend on external vendors to achieve that level of stealth functionality and reliability.

98% of respondents stated that all business processes ... expect to be positively impacted by IoT

Most organizations are still in the early stages of their IoT implementations which explains why 89% of survey respondents believe that their organization needs to improve its plans for IoT. At the same time, there is tremendous optimism and confidence regarding how their IoT implementations can transform their organizations and make them more competitive in the future. 98% of respondents stated that all business processes and functions within their organizations have been or expect to be positively impacted by IoT. Those functions include customer service/relationships, product/service delivery, supply chain management, marketing/sales, product/service development, employee/HR processes, infrastructure management, contact center/support, accounting/finance and administration.

Regarding IoT deployment in 2019 and beyond, most organizations are looking to deploy IoT on the edge but are struggling to do so — 80% of respondents want to deploy IoT on the edge but only 8% are actually doing that today. By combining IoT and edge computing, organizations can shift the workload of processing IoT data closer to, or in some cases on the device itself. For example, instead of sending all the data from a wind turbine to the cloud and processing the data centrally, users can process data and analytics locally and then send the results to the cloud. This reduces network load, cloud processing and storage requirements while making IoT in areas without reliable networks possible.

According to market research firm IDC, the IT spend on edge infrastructure will reach up to 18% of the total spend on IoT infrastructure by 2020. That spend is driven by the deployment of converged IT and OT systems which reduces the time to value of data collected from their connected devices.

Methodology: The survey queried 800 senior IT and business decision makers at organizations with a global annual revenue of $500M and higher across 13 different countries in North America, Europe and Asia Pacific. The respondents came from organizations that were deploying IoT platforms and the respondents themselves had some level of involvement and responsibility for selecting and maintaining those solutions.

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 ...