A new era for strategic SAM

Modern software asset management strategy is becoming critical for business intelligence

  • Traditional SAM tools leave CIOs increasingly vulnerable to software over-spending and greater compliance risk
  • Advanced SAM programs can reduce up to 30% of wasted software spend in the first year of adoption
  • Machine learning capabilities are adding additional layers of SAM intelligence

For CIOs, managing hundreds of software assets across an enterprise can be like opening Pandora’s box. The problem may seem manageable from the outside, but once you look beneath the surface and into layers of licensing, security, and cost management, order can quickly turn to chaos.

Historically, most IT organizations had a limited number of software applications to worry about—productivity suites, financial and travel apps, Web browsers. Today, with the mass adoption of mobile devices, cloud services, and IoT, software asset management (SAM) is a different beast.

Tracking assets across rapidly expanding corporate public and private clouds can be a costly, risky adventure. Companies waste an estimated $167 billion annually on unnecessary software costs, according to Gartner.

The challenge is catching up with many CIOs. As recently as 2018, according to Deloitte, 74% of IT leaders said their organizations didn’t have a formal SAM function, and even more (83%) did not consider it a strategic priority.

Those views are changing quickly. Companies that deploy modern SAM programs can reduce up to 30% of their software “waste” in the first year of adoption, according to Gartner. The annual marketplace for SAM solutions, estimated at $1.4 billion in 2018, is expected to reach $3.9 billion by 2026.

The consumption meter for cloud is always running.

Next-generation SAM is helping CIOs understand what software their organizations own, how much or little is being used, whether the company is overpaying or not, and if their companies are effectively managing their licensing compliance risk.

“It’s all about knowing and managing the software your organization is using so you don’t go beyond what you’re entitled to consume,” says Paul Baguley, a partner at KPMG. The new generation of digital SAM tools can help CIOs cover the basics such as maintaining accurate software inventories, and “understanding analytics around software demand management, usage, and other capabilities of these tools as they continue to evolve.”

From old-school SAM to new

For years, many companies used SAM primarily to gather data about how their organizations acquired, used, and disposed of their software assets. The idea was to track and manage software usage in order to avoid buying unneeded software and exceeding software license limits, which can trigger costly vendor audits. Most IT leaders viewed SAM as a clerical task that IT staff could handle with spreadsheets and other basic tools.

That’s no longer a feasible approach because of how much software is now being consumed and shared across the modern enterprise. Large companies manage an average of 129 software applications, according to the 2019 Businesses @ Work report from Okta.

The explosion of software as a service (SaaS) in recent years adds another layer of complexity in that SaaS makes it far easier for employees to sign up and download a multitude of apps to help them complete their work.

Cloud-based business apps still require careful oversight. As a result of these trends, modern SAM tools are expanding beyond their traditional focus on software license management.

“Instead of serving as enhanced spreadsheets, the better SAM tools are evolving into business intelligence tools,” says AJ Witt, ITAM industry analyst at the ITAM Review, a trade publication. “They are moving away from just capturing vast amounts of inventory data and more toward making sense of that data.”

A software asset management strategy can support different functions across a company, including procurement, HR onboarding, IT services, legal, and security. Each department expects to have seamless visibility into IT assets. Accomplishing this can be difficult without SAM tools and the ability to know what assets the organization owns.

Take software licensing, for example. The SAM toolset of the future starts “with a strong software library where you have all of your licensing rules,” says Sandi Conrad, practice lead with Info-Tech Research Group.
Most companies with successful SAM programs have grown those libraries over long periods of time, she adds. Some contain hundreds of thousands of different software titles. “It’s a lot of work to keep all of that up-to-date, build out the algorithms, and match the licensing to the data. You need modern SAM tools to handle that, and it’s vital to choose the right ones for your business.”

Consider Burns & McDonnell, a construction engineering firm with more than 7,000 employees. After the company offloaded manual tasks to a SAM platform with automation capabilities, it realized $5 million in cost savings in its first six months, and now saves at least 1,000 worker days per year.

Smarter SAM with AI

Going forward, experts predict many companies will roll SAM into broader IT asset management (ITAM) platforms that can centrally manage all their hardware, software, and cloud assets.

“Whether it’s SaaS, platform as a service, or IoT devices, none of us want to go to multiple consoles to manage complex IT assets,” says Ryan Wood-Taylor, director of ITAM product marketing for ServiceNow.

“The consumption meter for cloud is always running. You need to consider the business services the cloud resources support and be able to take corrective action from the same platform.”

That’s one reason why machine-learning capabilities are finding their way into ITAM and SAM tools to help automate repetitive, manual tasks.

ServiceNow and other vendors are developing SAM tools that promise proactive benefits. For example, machine learning (ML) tools can analyze ledger data to identify different types of software spending, giving IT managers new visibility into their distributed software and SaaS purchases.

“We’re still in early days, but there’s definitely potential with SAM and artificial intelligence that hasn’t yet been tapped into,” says KPMG’s Baguley.

Here are some of the ways in which AI and machine learning could further enhance SAM tools in coming years:

  • Tracking entitlements. AI applications, Baguley says, could provide far faster and more accurate loading of entitlements when companies purchase software licenses
  • Full automation of license management. “Companies frequently employ SAM professionals to understand various software licensing rules,” says Baguley. “AI could help automate all of that, allowing people to focus on more meaningful tasks.”
  • Real-time legal and compliance analysis. Machine learning algorithms can sift through murky legal language in software agreements to flag potential opportunities or problems and provide actionable recommendations on how to handle them.
  • Public cloud load-balancing. ML-based automation tools can help track and spot consumption issues that need addressing across the various public clouds. While each public cloud has its own metering capabilities, most organizations today store apps and data in multiple clouds when they could be optimizing hybrid cloud consumption centrally.

As more CIOs start to see major upside in AI and ML, vendors are racing to figure out what capabilities to add in the next few years. “AI and ML are on their roadmaps,” Conrad says. “They will become more ingrained in these tools as tracking software assets becomes more complex.”