Generative AI is proving to be a powerful, flexible tool in the hands of motivated enterprises, with data and predictive analytics as the top AI workload.
Overall, 62% of respondents cited data analytics among their top AI workloads. Generative AI was a close second at 61%, and even surpassed data analytics in industries including healthcare and life sciences and telecommunications. In addition, generative AI was the top workload among North American and EMEA respondents.
Companies are seeing significant ROI when deploying and scaling highly specific applications that target a distinct business opportunity.
The key to building highly specific and profitable AI applications is using open source and open weight models and software, which allows organizations to bring the right tools to solve specific problems and fine-tune models with their own data for deployment in generative and agentic applications.
Overall, 85% of respondents said open source is moderately to extremely important to their organization’s AI strategy. That includes nearly half (48%) who said open source is very to extremely important.
Small companies, which are often resource-constrained and prefer to build solutions rather than pay for commercial off-the-shelf products, were especially keen on open source, with 58% saying open source is very to extremely important. More than half of executives (51%) throughout the surveys also cited the high importance of open source.
Nearly all the respondents in this year’s surveys said their AI budgets will increase or at least stay the same in 2026.
Overall, 86% of respondents said their AI budget will increase this year. Another 12% said budgets will stay the same. And nearly 40% of respondents said budgets will increase by 10% or more. North American organizations are especially keen on increasing their AI budgets, with 48% stating their budgets would increase by 10% or more, as well as 45% of executive-level respondents.
The surveys revealed that the financial services, retail and CPG, and healthcare and life sciences industries showed the strongest adoption and ROI results.
The spending will go toward optimizing current AI solutions and finding more use cases across the enterprise. Overall, 42% of respondents said optimizing AI workflows and production cycles was the top spending priority in 2026, followed by 31% who said they’d spend on finding additional use cases. Another 31% said spending would go toward building and providing access to AI infrastructure, such as on-premises data centers, or to the cloud.
AI has strong momentum in the enterprise, but it’s still fairly early in the adoption cycle. Nearly a third of respondents in the surveys are still in the pilot and assessment stage. Challenges persist in workflows and operations, as well as getting the right expertise to scale impactful solutions.
Organizations are also still grappling with their data. Building specialized AI applications requires enterprises to have a handle on their data to fine-tune models for their needs. Having sufficient data and other data-related issues were cited as the top challenge in the surveys, according to 48% of respondents.
Lack of AI experts and data scientists to implement that data and scale AI projects from pilot to production was the next most prominent challenge, according to 38% of respondents.
The benefits of AI can also be difficult to quantify. For example, improved productivity can be a subjective measurement for the everyday office worker. As such, 30% of respondents cited lack of clarity on AI’s ROI as one of their top challenges.
Respondents of NVIDIA’s “State of AI” surveys comprise people who’ve opted in to receive communications from NVIDIA and have invested in or are curious about AI for their business.
Fielded from August to December 2025, the “State of AI” surveys garnered data from over 3,200 respondents across financial services, retail, healthcare, telecommunications and manufacturing. Respondents included a variety of roles, such as C-suite and vice presidents (27%), directors and managers (33%) and AI practitioners (40%).
Respondents represented organizations of varying scale, with 39% from large enterprises employing more than 1,000 people, 27% from mid-sized companies with 100-1,000 employees and 34% from smaller organizations with fewer than 100 employees.
Geographic distribution spanned four major regions: APAC (32%), North America (26%), EMEA (21%) and the rest of the world (20%).
The online surveys were sourced from NVIDIA’s distribution lists and through social media globally, and in China and Japan through a third-party agency.
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Key considerations
- Investor positioning can change fast
- Volatility remains possible near catalysts
- Macro rates and liquidity can dominate flows
Reference reading
- https://blogs.nvidia.com/blog/state-of-ai-report-2026/#primary
- https://blogs.nvidia.com/blog/author/drowinski/
- https://blogs.nvidia.com/blog/state-of-ai-report-2026/#disqus_thread
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Informational only. No financial advice. Do your own research.