Applying AI to IT asset management (ITAM) can significantly benefit IT teams. It primarily helps you save the time spent on repetitive ITAM tasks, to give you more time to focus on those critical high impact IT projects.
As organisations expand their AI initiatives, IT asset management is a natural area for integration. In this blog, we’ll explore some use cases we see for AI in ITAM and share how they can help you enhance your daily efficiency.
Benefits of AI for IT asset management
IT asset management is labour intensive. AI can automate tedious, repetitive tasks, freeing up time for strategic priorities like financial planning, risk mitigation, and IT improvement projects.
Additionally, IT landscapes—especially cloud and software assets—change rapidly, making it difficult for us humans to keep up. But AI can process vast amounts of data in real time, ensuring nothing falls through the cracks.
Transformative AI use cases in ITAM
These AI use cases offer greater potential ROI but are harder to implement and depend on having a solid asset database as a foundation. These use cases will get better over time as the AI learns your assets and has more historical data to access.
Providing insights and optimisation
AI can analyse historical data and suggest optimisations. For example, you can ask questions like “How can I optimise my Zoom licenses?” or “Which laptop models provide the best ROI for our team?” AI can also spot gaps in asset utilisation, assist with financial planning, and predict future asset needs based on trends like employee headcount growth.
Outsourcing the data analysis and/or identification of optimisation areas helps you spend time actually making the suggested improvements.
Accelerating compliance reporting
AI can generate compliance reports, reducing your manual workload. It can analyse contracts and regulations, compare them with actual IT asset usage, and flag discrepancies. This proactive approach ensures compliance while minimising your effort.
Simplifying risk management
AI can assess security risks by monitoring the latest threats, identifying vulnerabilities in your asset environment, and recommending mitigations. It can also automate task creation for IT teams, promptly addressing security issues. While it may not spot everything, and non-AI risk management work is still needed, it can significantly help you keep your business safe.
Improving asset discovery
AI can be used to supplement asset discovery tools to help identify all the assets you have at your organisation and ensure you’re not missing something. It can also help you identify shadow IT that you can then address appropriately.
Predictive maintenance
This tends to be more common and has much greater ROI when looking at industrial assets, but it’s still relevant to IT assets. AI can predict how assets will behave in future based on the history of similar assets you own. This helps you plan for replacements or upgrades well in advance instead of being surprised and reactive when something breaks. This means you can minimise service disruptions and ensure resources are allocated in anticipation.
Efficiency gains: quick-win AI applications for ITAM
These AI use cases, while not revolutionary, offer a faster ROI and have simpler requirements for your asset data. They are ideal places to start with AI.
Automating data entry
Entering asset data manually is tedious and prone to errors. AI’s OCR (optical character recognition) capabilities can extract information like serial numbers, contract deadlines, warranty lengths, and compliance data from documents and automatically enter them into your ITAM database. This improves data accuracy and saves your team precious time.
Identifying missing information
Your asset management is only as good as your asset data, and it is challenging to keep data updated. AI can flag incomplete or incorrect data by detecting anomalies or data that breaks patterns. It can also learn your data governance formats and processes and spot instances where they are not followed. This helps maintain high data quality with minimal effort, which is especially crucial for further AI-driven decision-making.
Spotting duplicate assets
Duplicate asset entries can inflate costs and create confusion. AI can detect duplicate records more effectively than traditional de-duplication features, ensuring a cleaner and more accurate asset database.
The state of AI in ITAM today
Currently, almost zero ITAM vendors offer AI capabilities. However, we expect this to change as ITAM vendors start to incorporate AI into their tools. Organisations looking to implement AI for ITAM today must either develop custom AI solutions themselves or wait for vendors to release AI features.
We already see cases where businesses use AI APIs for tasks like data extraction from pdf files and entering the extracted data into their ITAM or ITSM systems. While self-built solutions offer flexibility, they require far more effort and expertise.
Whichever route you choose, you may face the same challenges, which leads us nicely to the next section.
Challenges of AI in ITAM
Despite its potential, AI implementation in ITAM comes with challenges. Gartner predicts that by 2027, 50% of AI projects for IT service desks will be cancelled due to cost, complexity, or lack of ROI. Here’s our recommendations on how to address common hurdles and ensure success.
Data quality
AI relies on high-quality data. If your ITAM database is incomplete or outdated, prioritise fixing it before implementing AI. This could be solved by introducing better processes, really focusing on getting all your data into your database, or even switching ITAM tool if your current solution is not working for your team.
Cost considerations
AI is often more expensive than anticipated, with costs tied to processing power and API usage. For example, Gartner estimates that a company of 3000 with around 100 IT staff members can be charged anywhere from $5000-$150,000 for AI usage alone. It doesn't even account for implementation costs! Thorough due diligence is essential before committing to AI investments. Carefully weigh your expected benefits against the financial investment. And do not be afraid to recommend not using AI to solve a particular challenge if the ROI is not there.
Data privacy
AI processing often involves sensitive data, especially if you start to include employee information with your asset data. Organisations must ensure compliance with data security policies and be aware of where AI models are processing data. We’ve heard of businesses who cannot use a market-leading ITSM vendor’s AI agent because they are unsure of where their data is being processed.
Implementation complexity
Deploying AI is challenging, especially for first-time adopters. Having a dedicated AI expert on your team or within your company can help streamline the process and avoid common pitfalls. Having an AI expert also helps align AI initiatives across the company and avoid different teams solving the same problems independently.
Summary & next steps
To take full advantage of AI in ITAM, first ensure your asset data is as accurate as you can make it. Next align AI initiatives with broader company goals and secure stakeholder buy-in for your AI plan. We propose starting with small, quick-win use cases as they provide valuable learning experiences while driving meaningful results.
At Starhive, we’re exploring AI-powered ITAM capabilities. Our solution’s data-centric approach makes Starhive ideal for AI-driven ITAM and ITSM. If you’re interested in learning more or working with us to explore an AI use case, get in touch.
