Software asset management (SAM) has evolved from simple back-office record-keeping to more complex licence and contract management that integrates innovative technologies such as artificial intelligence (AI), machine learning (ML) and generative AI (GenAI).
Successful SAM implementations save on licence costs, improve compliance and reduce true-ups. They also improve software utilisation, enabling firms to derive more value from existing investments; remove unused, unwanted, or architecturally unfit applications; and improve supplier relationships through better contract management. Most SAM practices and tools have been around for a long time, and SAM continues to play an important role throughout the software acquisition cycle.
Firms now use SAM processes not only to support one-time events, software audits, or contract renewals, but also to achieve effective cost management and tech optimisation. Tracking cloud licences, recording contracts, managing and linking assets, and understanding entitlements remain areas for SAM suppliers to focus on.
In Forrester’s 2023 modern technology operations survey, only 37% of all digital and IT professionals (43% at enterprises) agreed that their organisation’s software is well controlled and managed by a SAM team. IT has many challenges, including developing new systems and resolving high-priority incidents. But in terms of a process that should be consistent and repeatable, SAM is complex and challenging, and few firms feel they have fully mastered it.
The seemingly simple matter of whether a given software title is installed on a device is full of complexities and pitfalls. Reconciling this hard-to-establish technical fact with the contractual entitlement to use the software is difficult.
A major value proposition of SAM is that it lowers operational risk by reducing the likelihood of unfavourable software audits and unexpected massive true-ups. This is a weaker value proposition than revenue generation or cost reduction, making SAM an attractive target for cost-cutting.
SAM complexity
Subject matter experts aren’t easy to find and often must be trained from within – they’re valuable and thus subject to poaching.
As an example, a large enterprise with more than 5,000 employees tasked a SAM team of just three people with generating a weekly department-wide compliance report and sending it to department leaders. The company’s dilemma: on one hand, it needed more resources to be able to ensure an accurate accounting of every application, its actual usage, and compliance; on the other hand, cost optimisation concerns led to plans to either outsource the SAM function or involve part-timers for SAM reporting.
Another area of complexity is that the proliferation of software-as-a-service (SaaS) solutions accessible via web browsers initially caused some disruption for SAM suppliers. Cloud licensing is less a licensing and compliance issue and more an optimisation and usage challenge.
Most SAM suppliers use standard single sign-on cloud access security brokers or browser plug-ins to record and manage SaaS contracts and usage. SAM suppliers may also have standard application programming interfaces (APIs) that integrate with most major SaaS providers to pull in licence and contract data.
It is also worth noting that free and open source software has its own set of challenges related to redistribution and commercialisation. This makes it critical for companies to record any open source software, especially software that comes with a GNU Affero General Public License.
Change in SAM strategy
The focus of IT asset management (ITAM) and SAM have shifted away from software audits and licence compliance. It is now more focused on usage management; security and contract risk management; and cost, spending and business value optimisation. With the right process rigour and tool utilisation, Forrester believes firms can get more business value from SAM tools.
One of their uses is in supplier management. SAM tools can provide supplier management teams with information on asset usage, location and performance data to negotiate deals with software, hardware and service suppliers. They also help fine-tune agreements and negotiate better deals by identifying areas to optimise, expand or eliminate.
SAM tools also drive software usage cost optimisation and mitigate the risk of paying true-up costs. While software reclamation often drives SAM implementations, properly utilising software assets and licences emanating from SAM mitigates other risks. These include non-compliance risks from unauthorised software installations, exceeded licence limits, or the use of software without proper licences; financial risks from non-compliance penalties or paying for unnecessary subscriptions or software licences; and security risks from using unsupported software, which can lead to cyber attacks or data breaches.
The role of AI in SAM
As environments become more complex – with cloud, edge and quantum computing creating “asset confusion” and licence structures continuously changing – it becomes even more important to have an integrated asset management approach and deploy innovative technologies like GenAI and automation to improve operational efficiency and reduce manual load, risk and cost. While suppliers are yet to release new functionality, they are actively developing several ML and GenAI capabilities.
Forrester believes GenAI will facilitate contract lifecycle management by automating contract management steps such as drafting, negotiation, approval, execution and monitoring. Asset managers will be able to use GenAI to quickly create and modify contracts and meet their specific needs while complying with organisational policies and regulatory requirements.
Machine learning can also be deployed for compliance monitoring and risk assessment. ML tools can scrutinise contracts to identify compliance risks, predict licence violations, detect unauthorised software usage and optimise licence procurement decisions. By automatically correlating software usage data with entitlements and licence terms, ML algorithms can help organisations prepare for audits, identify potential compliance gaps and provide evidence to support licence reconciliation efforts. ML-powered anomaly detection algorithms can also identify unauthorised software installation, usage or access attempts.
All of the major enterprise service management platforms that support ITAM – ServiceNow, BMC Helix, Ivanti – offer chatbots. Looking ahead, Forrester predicts GenAI-powered chatbots will provide ever more personalised recommendations for asset management decisions by analysing user preferences, historical interactions and contextual data, and will be able to tailor responses to meet the individual needs of users, whether that’s recommending software licences based on usage patterns or suggesting investment strategies based on risk profiles.
This article is based on the Forrester report Why you must rethink your software asset management practices by Biswajeet Mahapatra and Charles Betz, with contributions from Carlos Casanova, Janet Worthington, Julie Mohr, Glenn O’Donnell, Arjun Kalra and Bill Nagel. Biswajeet Mahapatra is a principal analyst at Forrester focusing on initiatives that help CIOs ramp up their digital transformation journey.