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Are Your Software Dollars Gathering Dust? It's Time to Eliminate the Shelfware and Cloud Waste!

Rex McMillan

Software spend in 2021 and beyond will be a hot button as organizations redirect priorities and spending as a result of the pandemic. Spend that can be linked to clear results — more productivity, more ROI, and better integration of the remote workforce — will be looked upon as worthy. However, wasted spend — particularly software assets that have devolved into "shelfware" or cloud waste — will be a ripe opportunity for CIOs and IT management to direct a laser beam on optimizing software asset usage and its potential drain on budget.

With IT teams now supporting workers who are predominantly in remote environments and the attendant security challenges, a fair question is, "Should worrying about shelfware and uncontrolled cloud usage be added to the list of top concerns?" According to Gartner, "At any point in time IT operations may be running with 25% plus of software going unused." A benchmark study a few years back estimated U.S. wasted software spend to be $30 billion, or an average $259 per desktop. If your organization has 20,000 desktops, for example, that equals $5.2 million in investment bringing in zero return.

So, the answer is yes, tightening control over software asset and cloud spend and use should be on the radar. Inevitably, the C-suite, looking to 2021, will be asking tough questions about any new requested spending. And importantly, IT will be expected to deliver a thorough, cogent report on "spend intelligence" related to software use and whether these assets are contributing to desired business outcomes or are simply a money drain.

Spend intelligence is, among other attributes, a means of getting control of shelfware and cloud consumption. It captures data on all software asset spend and cloud application usage and assesses actual use. It then gives rise to the ability to better manage and retire software and cloud assets, or repurpose them, throughout their usable lifecycle. It is a noble goal. However, gathering data on all software and cloud applications has become far more difficult as IT teams now must look at the universe of those assets residing on-prem, in the cloud, or at the edge where remote workers are using devices and applications to enter the network.

The solution is to incorporate automation, machine learning and data analytics into software spend inquiries. This will accelerate insights into how well an organization is using its current software asset environment, and to put a laser light on all assets that have become shelfware. A few practices to consider include the following:

Eliminate Time-Wasting Tasks

A survey of IT professionals revealed 45% use inventory tools as one of their resources for asset tracking, 43% are still using spreadsheets and 50% are using an endpoint management solution. Introducing automated processes into spend intelligence gathering will eliminate time-consuming manual tasks. Data can be collected and maintained in a single, easily navigated repository, reducing the risk of error.

Automate Data Intelligence

Capturing software asset data across on-prem, cloud and edge environments requires tools that can employ automation to collect data from these diverse environments, then automatically analyze and organize the data into relevant categories like licenses or subscriptions.

By moving this data to a central repository, IT teams can quickly find information they need on a particular license, for example, by just using a search mechanism on the dedicated dashboard.

Speed Up Visibility

Stopping the shelfware and cloud waste budget drain involves not only knowing what unused software assets already exist but also preventing more of those assets from becoming dormant and unused. That takes constant diligence in tracking usage, license types, purchases, subscriptions, renewals and instances, contract expirations and ongoing spend.

Automated processes give IT clear insights into precisely where software spend waste is occurring.

IT also provides an up-to-the-minute picture of which applications are consistently being used, detail that will eventually need to be factored into budget strategy reports to the C-suite.

Dust Off the Shelf

If a software asset is not being used in a reasonable timeframe, it needs to be eliminated, or redeployed where the license cost is valued. That usually means making changes to subscriptions, licenses and contracts, notably those with built-in renewal clauses.

Ivanti's survey of IT professionals found 28% devote hours each week supporting out-of-warranty/out-of-support policy assets, and 20% of them indicate they don't have insights into which assets are out of date. This combination of unused software and those licenses past their expiration date is a weak link in IT's and CIO's charters to spend carefully and strategically post-pandemic. Integrating automated tools that can deliver the needed due diligence in managing vendor relationships has to be a top priority.

Reclaim Dollars

The payoff for incorporating automation into software asset management is clearer insights into asset spend, usage and contractual agreements — both on-prem and in the cloud. IT teams now can reclaim dollars that no longer need to be spent on software assets that have become shelfware or cloud waste, are underutilized or are now out-of-date.

Going into 2021, software asset spending will be a source of more scrutiny as IT executives and CIOs fine tune spending to further stabilize productivity in the remote environment. Spending will occur, but IT departments who have excessive amounts of shelfware, or cloud application licenses that have long stopped contributing to ROI, will be in a weakened position to make a case for new investments. By incorporating automation into data collection and software asset management due diligence, IT can gain the power of knowledge to make a case for new strategic investments, all with an eye to better business outcomes.

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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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Are Your Software Dollars Gathering Dust? It's Time to Eliminate the Shelfware and Cloud Waste!

Rex McMillan

Software spend in 2021 and beyond will be a hot button as organizations redirect priorities and spending as a result of the pandemic. Spend that can be linked to clear results — more productivity, more ROI, and better integration of the remote workforce — will be looked upon as worthy. However, wasted spend — particularly software assets that have devolved into "shelfware" or cloud waste — will be a ripe opportunity for CIOs and IT management to direct a laser beam on optimizing software asset usage and its potential drain on budget.

With IT teams now supporting workers who are predominantly in remote environments and the attendant security challenges, a fair question is, "Should worrying about shelfware and uncontrolled cloud usage be added to the list of top concerns?" According to Gartner, "At any point in time IT operations may be running with 25% plus of software going unused." A benchmark study a few years back estimated U.S. wasted software spend to be $30 billion, or an average $259 per desktop. If your organization has 20,000 desktops, for example, that equals $5.2 million in investment bringing in zero return.

So, the answer is yes, tightening control over software asset and cloud spend and use should be on the radar. Inevitably, the C-suite, looking to 2021, will be asking tough questions about any new requested spending. And importantly, IT will be expected to deliver a thorough, cogent report on "spend intelligence" related to software use and whether these assets are contributing to desired business outcomes or are simply a money drain.

Spend intelligence is, among other attributes, a means of getting control of shelfware and cloud consumption. It captures data on all software asset spend and cloud application usage and assesses actual use. It then gives rise to the ability to better manage and retire software and cloud assets, or repurpose them, throughout their usable lifecycle. It is a noble goal. However, gathering data on all software and cloud applications has become far more difficult as IT teams now must look at the universe of those assets residing on-prem, in the cloud, or at the edge where remote workers are using devices and applications to enter the network.

The solution is to incorporate automation, machine learning and data analytics into software spend inquiries. This will accelerate insights into how well an organization is using its current software asset environment, and to put a laser light on all assets that have become shelfware. A few practices to consider include the following:

Eliminate Time-Wasting Tasks

A survey of IT professionals revealed 45% use inventory tools as one of their resources for asset tracking, 43% are still using spreadsheets and 50% are using an endpoint management solution. Introducing automated processes into spend intelligence gathering will eliminate time-consuming manual tasks. Data can be collected and maintained in a single, easily navigated repository, reducing the risk of error.

Automate Data Intelligence

Capturing software asset data across on-prem, cloud and edge environments requires tools that can employ automation to collect data from these diverse environments, then automatically analyze and organize the data into relevant categories like licenses or subscriptions.

By moving this data to a central repository, IT teams can quickly find information they need on a particular license, for example, by just using a search mechanism on the dedicated dashboard.

Speed Up Visibility

Stopping the shelfware and cloud waste budget drain involves not only knowing what unused software assets already exist but also preventing more of those assets from becoming dormant and unused. That takes constant diligence in tracking usage, license types, purchases, subscriptions, renewals and instances, contract expirations and ongoing spend.

Automated processes give IT clear insights into precisely where software spend waste is occurring.

IT also provides an up-to-the-minute picture of which applications are consistently being used, detail that will eventually need to be factored into budget strategy reports to the C-suite.

Dust Off the Shelf

If a software asset is not being used in a reasonable timeframe, it needs to be eliminated, or redeployed where the license cost is valued. That usually means making changes to subscriptions, licenses and contracts, notably those with built-in renewal clauses.

Ivanti's survey of IT professionals found 28% devote hours each week supporting out-of-warranty/out-of-support policy assets, and 20% of them indicate they don't have insights into which assets are out of date. This combination of unused software and those licenses past their expiration date is a weak link in IT's and CIO's charters to spend carefully and strategically post-pandemic. Integrating automated tools that can deliver the needed due diligence in managing vendor relationships has to be a top priority.

Reclaim Dollars

The payoff for incorporating automation into software asset management is clearer insights into asset spend, usage and contractual agreements — both on-prem and in the cloud. IT teams now can reclaim dollars that no longer need to be spent on software assets that have become shelfware or cloud waste, are underutilized or are now out-of-date.

Going into 2021, software asset spending will be a source of more scrutiny as IT executives and CIOs fine tune spending to further stabilize productivity in the remote environment. Spending will occur, but IT departments who have excessive amounts of shelfware, or cloud application licenses that have long stopped contributing to ROI, will be in a weakened position to make a case for new investments. By incorporating automation into data collection and software asset management due diligence, IT can gain the power of knowledge to make a case for new strategic investments, all with an eye to better business outcomes.

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.