Retired IT assets could fund AI spending, report says

Iron Mountain has published research suggesting retired IT assets could help fund corporate AI spending. The study says many organisations have yet to consider reclaimed IT as a budget source.

It found that 83% of firms have not used reclaimed IT spending to support AI investment, while 17% already do. By comparison, 27% use new capital allocation and 24% draw on departmental funds.

Businesses can recover 10% to 30% of initial hardware costs when IT asset disposition is managed as a formal budget recovery programme, according to the report. For an organisation with more than 5,000 employees, the research models potential recovered value of USD $1.2 million, rising to USD $12 million for one with 50,000 employees.

The report comes as companies face pressure to expand computing infrastructure, data storage and related technology for AI projects while containing costs. It cites separate findings showing that almost 90% of organisations are struggling to keep AI costs within budget.

Budget pressure

Iron Mountain argues that decommissioned technology is often treated as an end-of-life disposal issue rather than a source of capital recovery. The report presents asset lifecycle management as a way to combine asset tracking, secure data destruction, resale and recycling in a single process.

That approach becomes more important as AI shortens hardware refresh cycles. More frequent replacement of servers, storage equipment and devices can leave companies with larger volumes of retired assets, increasing the pool of equipment that may still hold resale or recycling value.

Erica Hirsch, Senior Vice President, Asset Lifecycle Management, Iron Mountain, said: "The AI investment conversation tends to focus on what businesses need to buy next, but there is another side to the equation. Organisations should also understand what value is sitting in the technology they already own and how that can be recovered as their IT estate evolves."

The report also links retired IT management to environmental targets. It says 71% of organisations are concerned that AI's energy demand will conflict with ESG or sustainability milestones, which may strengthen the case for recovering, reusing and recycling technology as infrastructure changes.

Sustainability link

For finance and technology leaders, the case is that better control over outgoing hardware could address several issues at once: budget discipline, data security and waste reduction. The model suggests firms with larger employee bases have more scope to recover meaningful sums, particularly where refresh cycles are speeding up.

That may be relevant for large enterprises making repeated hardware investments to support AI workloads. New spending on processing, storage and networking can strain capital budgets, especially when business units are competing for resources.

By contrast, the report suggests retired assets can provide an internal source of funds outside traditional requests for new budget. While the sums involved would not cover the full cost of large AI programmes, they could offset part of the expense of infrastructure upgrades.

Iron Mountain positions the issue as part of a broader shift in how companies think about technology ownership. Instead of focusing only on procurement, businesses should also pay closer attention to the value that remains in equipment at the point of retirement, the report argues.

Effective asset lifecycle management gives organisations greater visibility and control over how retired assets are handled securely and responsibly, according to Iron Mountain. That is likely to matter in sectors with strict data handling rules, where disposal of old equipment can create operational and compliance risks.

Hirsch said: "AI is changing the economics of the IT lifecycle. The opportunity is to connect technology refresh with capital recovery, security and sustainability, turning the end of one asset's life into part of the investment case for the next."