Stanford’s AI Index: 5 critical insights reshaping enterprise tech strategy

Stanford University’s Artificial Intelligence-Centered Institute (ocean) released its 2025 AI Index report, providing data-driven analysis of the global development of AI. HAI has been developing reports on AI for the past few years and The first benchmark By 2022. Needless to say, a lot has changed.

The 2025 report is equipped with statistics. Among some of the most important findings:

• The United States has produced 40 well-known AI models in 2024, which are significant in China (15) and Europe (3).
• Training training on AI models is doubled approximately every five months and the dataset size is every eight months.
• AI model inferred costs have dropped sharply – a 280-fold decrease from 2022 to 2024.
• Global private AI investment reached USD 252.3 billion in 2024, an increase of 26%.
• 78% of organizations report using AI (55% in 2023).

For IT leaders in enterprises mapped AI strategies, the report provides key insights into model performance, investment trends, implementation challenges and competitive dynamics to reshape the technology landscape.
These are five key points for enterprise IT leaders in the AI ​​index.

1. The democratization of artificial intelligence is accelerating

Perhaps the most striking discovery is that high-quality AI becomes more affordable and easy to use. The cost barrier that once restricted AI to technology giants is collapsing. Discovering in sharp contrast to what 2024 Stanford Report Established.

“The AI ​​model has become cheaper, more open and accessible over the past year,” Nestor Maslej, research manager at HAI AI Index, told VentureBeat. “Although the cost of training is still high, we now see a world where, although not a border, the cost of model is falling.”

The report significantly magnifies this shift: the inference cost of AI models executed at the GPT-3.5 level dropped from per million tokens in November 2022 to $0.07 per million tokens from October 2024 to October 2024, a 280-fold decrease in 18 months.

It is also important to perform performance convergence between closed and open weight models. The gap between top-tier closed models such as GPT-4 and leading open models such as llamas narrowed from 8.0% in January 2024 to 1.7% in February 2025.

IT Leader Action Project: Reevaluate your AI procurement strategy. Organizations that previously priced from state-of-the-art AI capabilities can now offer viable options through open models or cheaper commercial APIs.

2. The gap between AI adoption and value realization remains large

The report shows that 78% of organizations now use AI in at least one business function (up from 55% in 2023), but the actual business impact behind adoption lags.

When asked about meaningful ROI, Maslej admitted: “Our data is limited, and that data will be separated from AI from organizations that do not conduct. This is a key area of ​​analytics that we intend to explore further.”

The report shows that most organizations that use AI reports to generate moderate financial improvements. For example, 47% of businesses increase in strategic and corporate financial reporting revenue, but usually below 5%.

IT Leader Action Project: Focus on measurable use cases with obvious ROI potential rather than wide implementation. Consider developing a stronger AI governance and measurement framework to better track value creation.

3. Specific business functions show that AI has higher financial returns

The report provides granular insights into which business functions are seeing the most significant financial impact from AI implementation.

“In terms of cost, AI seems to maximize the functionality of supply chain and service operations,” Maslej notes. “See Maximum Benefits in terms of revenue, strategy, corporate financing and supply chain functions.”

Specifically, 61% of organizations save costs in supply chain and inventory management reports, while 70% of organizations increase in strategic and corporate financing reports revenue. Service operations and marketing/sales also show strong value creation potential.

IT Leader Action Project: Prioritize the functionality of AI investment to display the most important financial gains in the report. Supply chain optimization, service operations and strategic planning are high-potential areas for initial or extended AI deployment.

4. AI shows strong potential for balanced workforce performance

One of the most interesting findings is the impact of AI on labor productivity across skills levels. Several studies cited in the report show that AI tools disproportionately benefit low-skilled workers.

In a customer support environment, low-skilled workers experience a 34% productivity increase with AI assistance, while high-skilled workers have little progress. Similar patterns emerged in consulting (43% versus 16.5% growth) and software engineering (21-40% versus 7-16% growth).

“Generally, these studies show that AI has a strong positive impact on productivity and often benefits low-skilled workers from highly skilled workers, though not always,” Maslej explained.

IT Leader Action Project: Consider AI deployment as a workforce development strategy. Artificial intelligence assistants can help improve the competitive environment between junior and senior employees and have the potential to address skills gaps while improving overall team performance.

5. Responsible AI implementation is still a desire, not a reality

Despite growing awareness of AI risks, the report reveals significant gaps between risk identification and mitigation measures. Although 66% of organizations believe cybersecurity is a risk associated with AI, only 55% will actively mitigate it. Similar gaps exist in regulatory compliance (63% vs. 38%) and intellectual property infringement (57% vs. 38%).

These findings are amid the rise of AI events, which rose by 56.4% to 233 cases in 2024. Organizations face real consequences for failing to implement responsible AI practices.

IT Leader Action Project: Don’t delay the implementation of strong person-in-charge AI governance. Despite rapid improvement in technical capabilities, the report shows that most organizations still lack effective risk mitigation strategies. Now, developing these frameworks may be a competitive advantage rather than a compliance burden.

Looking to the future

The Stanford AI Index report lists images that rapidly mature AI technologies have become easier to use and capable, and organizations still have difficulty leveraging their potential.

For IT leaders, the strategic requirement is clear: focus on target implementation with measurable ROI, emphasize responsible governance and leverage AI to enhance workforce capacity.

“This shift points to greater accessibility, and I think it shows that a wave of adoption of a wider AI may be coming,” Maslej said.