Gartner predicts gen AI, the expenditure of $ 644 billion in 2025: What does IT leader mean for companies

Do not make a mistake, a lot of money will be spent on generative AI in 2025.

The analyst company Gartner has published a new report today that predicted that global gene AI expenses will reach $ 644 billion in 2025. This number corresponds to an increase of 76.4% compared to the AI ​​expenses in 2024 compared to the previous year.

Gartner’s report has been a choir of other industry analyzes in the past few months, all of which indicate increasing acceptance and expenses for gen AI. Expenses were grow by 130%According to studies carried out by AI in WhartonA research center at the Wharton School of the University of Pennsylvania. Deloitte reported that 74% of the companies have already fulfilled or exceeded AI initiatives.

Although it is not a surprise that the expenses for gene AI are growing, the Gartner report offers new clarity about where the money is going and where companies could get the greatest value from.

According to Gartner’s analysis, hardware will enjoy astonishing 80% of all genes Ai editions in 2025. The forecast shows:

• Devices make up 398.3 billion US dollars (99.5% growth)
• Server will reach $ 180.6 billion (33.1% growth)
• The software expenditure only follows 37.2 billion US dollars (93.9% growth)
• The services will be a total of 27.8 billion US dollars (162.6% growth)

“The device market was the biggest surprise, it is the market that is most powered by the offer side and not the demand side,” John Lovelock, Distinguished VP Analyst at Gartner, told Venturebeat. “Consumers and companies are not looking for AI -capable devices, but the manufacturers produce them and sell them. By 2027, it will be almost impossible to buy a PC that is not activated.”

The dominance of hardware will strengthen and will not reduce for Enterprise Ai

Since the hardware claims approximately 80% of the gene AAI expenditure of gene AI in 2025, many can assume that this ratio decreases that the market matures. Lovelock’s findings indicate the opposite.

“The situation is moving more in favor of hardware over time,” said Lovelock. “While more and more software has gene -enabled functions, less money is selected for the genei -Ai software. The AI ​​is embedded as part of the price of the software.”

This projection has profound effects on technological budget and infrastructure planning. Companies that expect to move hardware expenditure to the software over time may have to calibrate their financial models in order to take into account the ongoing hardware requirements.

In addition, the embedded nature of the AI ​​functionality of the future generation means that discrete AI projects can be spread less. Instead, AI functions are increasingly being found in existing software platforms, which makes deliberate adoption strategies and governance frameworks even more critical.

The POC cemetery: Why internal company -KI projects fail

Gartner’s report shows a sobering reality: Many projects for internal Gen-AI Proof-of-Concept (POC) have not provided the expected results. This has created what Lovelock describes as a “paradox”, in which the expectations decrease despite massive investments.

When Lovelock was asked to develop these challenges, he identified three specific obstacles that consistently derail genei initiatives.

“Companies with more experience with AI had higher success rates with gene AI, while companies with less experience suffered higher failure rates,” said Lovelock. “However, most companies have failed to one or more of the three best reasons: their data were of insufficient size or quality, their employees could not use the new technology to use the new process, or the new gene AAI would not have a sufficient ROI.”

These findings show that the main challenges of Gen AI are not technical restrictions, but the organizational standby factors:

1. Data junction: Many organizations lack sufficient high-quality data to effectively train or implement gene AI systems.
2. Change resistance: Users have difficulty using new tools or adapting workflows to include AI functions.
3. ROI deficits: Projects do not offer a measurable management value that justifies your implementation costs.

The strategic pivot point: from internal development to commercial solutions

The Gartner predicts an expected shift of ambitious internal projects in 2025 and beyond. Instead, the expectation that companies opt for commercial solutions from the Schelse that provide a more predictable implementation and business value.

This transition reflects the growing recognition that building AI solutions for relevant generations often represents more challenges than expected. Lovelock’s comments on the failure rates underline why many organizations will swing on commercial options, offer predictable implementation paths and a clearer ROI.

For technical managers, this suggests that prioritization of provider solutions that integrate genei functions into existing systems instead of building user -defined applications from scratch. As Lovelock stated, these functions are increasingly delivered as part of the standard software functionality as separate genei products.

What this means for the corporate strategy for companies

For companies that want to lead in the introduction of AI, Gartner’s forecast calls for several joint assumptions about the General AI market. The emphasis on hardware expenses, the supply side drivers and the embedded functionality indicates that an evolutionary approach can provide better results than revolutionary initiatives.

The technical decision-makers should concentrate on the integration of commercial gene AI functions into existing workflows instead of building customer-specific solutions. This approach corresponds to the observation of Lovelock that CIOs reduce the self -development efforts in favor of characteristics of existing software providers.

For organizations that plan a more conservative introduction, the inevitability of AI-enabled devices is challenging and opportunities. While these skills can get regular refresher cycles regardless of the strategic intention, organizations that prepare to use them effectively become competitive advantages.

Since the expenditure of Gen AI accelerates in 2025 of $ 644 billion, success is not determined solely by the expenditure of volume. Organizations that focus their investments on organizational willingness focus on overcoming the three key failure factors and develop strategies to use increasingly embedded gena -ai skills in order to use the greatest value from this rapidly developing technology landscape.