AI is opening new revenue opportunities and changing how businesses handle everyday work. It is also putting fresh pressure on IT budgets.
New applications may require additional computing power, faster storage and stronger network connections. Yet the systems supporting current operations still need attention. Funding an AI project does not remove the cost of keeping the rest of the business running.
For IT leaders, the challenge is deciding where new investment is necessary and where existing hardware can continue delivering value.
Budget Beyond the Initial Purchase
The cost of AI extends beyond software licenses and new servers. Integration, electricity, cooling, training and ongoing support all affect what a project ultimately costs.
The physical demands are significant. The International Energy Agency projects that global data center electricity consumption will rise from 485 TWh in 2025 to approximately 950 TWh by 2030, with AI-focused facilities growing particularly quickly. These figures cover all data centers, not AI alone. Source: IEA.
Businesses need to budget for the new workload alongside the infrastructure already in service. Leaving maintenance out of that calculation can create support gaps just as teams take on additional responsibilities.
Connect AI Spending to Business Results
An AI project should have a clear purpose, whether that is handling customer requests faster, improving forecasting or supporting a new paid service.
Revenue growth and cost savings are different measures. Faster processing may reduce the effort needed for a task, but it does not automatically generate additional sales. Likewise, new revenue needs to be weighed against the cost of delivering the service.
Track the results that matter: completed work, service quality, operating costs and revenue attributable to the project. Those measures provide a stronger basis for further investment than purchasing capacity ahead of a demonstrated need.
Address the Daily Hurdles
The practical challenges often appear after deployment. Teams must connect applications to existing data, manage access, check outputs and keep services available.
Infrastructure can introduce its own constraints. Storage may struggle to deliver data quickly enough, network connections may become congested, or equipment failures may interrupt dependent services.
Each problem needs the right response. Maintenance addresses hardware faults and support needs. Performance bottlenecks may require configuration changes or targeted upgrades. Buying more computing power will not necessarily fix a limitation elsewhere.
Review Working Hardware Before Replacing It
An AI initiative does not make every existing server, storage system or network device unsuitable.
Equipment supporting everyday business applications may still meet its requirements. Before including it in a refresh, review its performance, reliability, support coverage and running costs.
For example, a business introducing an AI customer-service application may need new capacity for that workload while retaining suitable hardware used for internal systems. This is a planning scenario, rather than an ETS customer case study, but it illustrates why replacement decisions should be made by workload.
The goal is to identify what needs changing and keep useful equipment supported.
Make Maintenance Part of the Investment Plan
An expensive OEM renewal is a reason to review support options. It does not, by itself, mean the hardware needs replacing.
Third-party maintenance can offer another route for eligible equipment, helping businesses manage support costs and plan replacement around operational needs.
Extended Technical Solutions (ETS) focuses on maintaining existing enterprise servers, storage and network hardware. As businesses direct funding toward AI, that maintenance can help them preserve useful infrastructure and avoid unnecessary refresh spending.
Keeping equipment longer still requires judgment. Parts availability, failure history, energy use and software security requirements all matter. Hardware maintenance should also be assessed separately from access to software and firmware updates.
Support Today’s Operations While Investing in Tomorrow
A practical AI strategy accounts for both new capabilities and the systems the business already relies on.
Review maintenance coverage before renewals approach. Identify genuine capacity gaps. Replace equipment where there is a clear operational reason, and consider continued support where existing hardware remains suitable.