Gartner forecasts gen AI spending to hit $644B in 2025: What it means for enterprise IT leaders

There is no doubt that a lot of money is spent on generating AI in 2025.

Analysts Gartner released a new report today predicting that global generation of AI spending will reach $644 billion in 2025. This figure represents a 76.4% year-on-year increase in AI spending in 2024 compared to Gen Gen Gen Gen Gen.

Gartner’s report has joined the chorus of other industry analytics in recent months, which all indicate increased adoption and spending in AI generations. Expenditure is already Grow 130%,according to AI in Waltonthe Center for Research at the University of Pennsylvania Wharton. Deloitte Report The 74% of businesses have reached or exceeded the AI ​​generation program.

While it’s no surprise that spending in the AI ​​generation is growing, the Gartner report provides new clarity on where money goes and how businesses may get the most value.

According to Gartner’s analysis, hardware will account for an astonishing 80% of all AI generation spending in 2025. Prediction shows:

• Equipment will account for $398.3 billion (99.5% increase)
• Servers will reach $180.6 billion (33.1% increase)
• Software spending was only $37.2 billion (up 93.9%)
• Services totaled $27.8 billion (up 162.6%)

“The device market is the biggest surprise, a market driven by the supply rather than the demand side,” John Lovelock, Gartner’s distinguished vice president analyst, told VentureBeat. “Consumers and businesses are not seeking AI-enabled devices, but are producing them and selling them. By 2027, it’s nearly impossible to buy a PC without AI-enabled.”

Hardware advantages will intensify without reducing enterprise AI

As hardware accounts for about 80% of AI Gen Gen spending in 2025, many may think that the ratio will gradually shift to software and services as the market matures. Lovelock’s insights show the opposite.

“Over time, the ratio of hardware is more favorable,” Lovelock said. “While more and more software will have AI-enabled capabilities, less money will be spent on AI Gen Software – GEN AI will embed features as part of the software price.”

The forecast has profound implications for technical budgets and infrastructure programs. Organizations that expect to transfer spending from hardware to software over time may need to recalibrate their financial models to address ongoing hardware requirements.

Furthermore, the embedded nature of future AI capabilities means that discrete AI projects may become less common. Instead, AI capabilities will increasingly arrive as features in existing software platforms, making the intentional adoption of strategies and governance frameworks even more critical.

POC Cemetery: Why internal enterprise AI projects fail

Gartner’s report highlights a sober reality: many internal AI proof-of-concept (POC) projects fail to deliver expected results. This creates what Lovelock calls a “paradox” that expectations are still declining despite large investments.

When asked to elaborate on these challenges, Lovelock identified three specific barriers that consistently matched the AI ​​program.

“Companies with more AI experience have higher success rates with AI generations, while those with lower experience have higher failure rates,” Lovelock explained. “But most businesses fail for one or more of three reasons: their data is insufficient in size or quality, their employees are unable to use new technologies or change to use new processes, or the new AI will not have enough ROI.”

These insights suggest that the main challenge of the AI ​​generation is not technological limitations, but organizational preparation factors:

1. Insufficient data: Many organizations lack sufficient high-quality data to effectively train or implement AI systems.
2. Change the resistance: It is difficult for users to adopt new tools or adjust workflows to incorporate AI capabilities.
3. ROI shortage: The project cannot provide measurable business value to justify its implementation cost.

Strategic Hub: From In-house Development to Business Solutions

Gartner forecasts point to an expected transformation from ambitious internal projects in 2025 and beyond. Instead, it is expected that enterprises will choose commercial off-the-shelf solutions that provide more predictable implementation and business value.

This transition reflects an increasing awareness that building custom AI solutions often presents more challenges than expected. Lovelock’s comment on failure rates highlights why many organizations are involved in business choices, providing predictable implementation paths and clearer ROIs.

For technology leaders, this demonstrates the priority of embedding AI Gen II capabilities into existing systems rather than building custom applications from scratch. As Lovelock notes, these features will increasingly be delivered as part of standard software features, rather than as separate generations.

What does this mean for enterprise AI strategies

For businesses looking to lead AI adoption, Gartner’s predictions challenge several common assumptions about the AI ​​generation market. The emphasis on hardware spending, supplier drivers, and embedded capabilities suggests that more evolved approaches may yield better results than revolutionary programs.

Technology decision makers should focus on integrating commercial AI capabilities into existing workflows, rather than building custom solutions. This approach is consistent with Lovelock’s observation that CIOs are reducing self-development efforts to support the capabilities of existing software providers.

For more conservative organizations, the inevitability of AI-enabled devices presents challenges and opportunities. Although these capabilities may be reached through conventional refresh cycles, organizations prepared to utilize them effectively will gain a competitive advantage regardless of strategic intentions.

As AI Gen spending accelerates to $644 billion in 2025, success cannot be determined by the amount of spending alone. Organizations that align investments with organizational preparation, focus on overcoming three key failure factors, and develop strategies that leverage the growing number of AI Gen II capabilities will extract the greatest value from this rapidly evolving technological landscape.