Investment firms are continuing to spend on alternative and market data even as artificial intelligence changes how financial institutions evaluate, access and use information. A new Neudata survey found that 97% of data buyers expect alternative-data spending to either increase or remain stable over the next 12 months, the highest level recorded in the research firm’s three-year survey.
Alternative Data Buyers Signal Stronger Budgets as AI Changes Data Strategy
Confidence in alternative-data budgets has reached a three-year high, according to Neudata’s 2026 edition of The Future of Alternative and Market Data. The report is based on 191 responses from data providers and buyers globally, including quant, multi-strategy, discretionary and macro investment firms as well as AI research labs. Sixty percent of buyer respondents manage more than $1 billion in assets.
The headline finding is a relatively strong commitment to data spending. Ninety-seven percent of buyers surveyed expect their alternative-data budgets to increase or remain unchanged during the coming year, compared with 89% in the previous survey.
For data vendors, that points to continued demand for information that can generate investment signals. But the survey also suggests that simply adding more datasets is not enough. Buyers are becoming more focused on whether information produces a measurable advantage.
Signal strength was the leading reason a data trial failed to convert into a purchase. Thirty-nine percent of buyers cited the absence of a discernible signal, compared with 25% who identified price as the primary reason.
That distinction is important for an alternative-data market increasingly shaped by AI. Large language models and AI research tools can make it easier for investment teams to discover, process and analyze large volumes of unstructured information. They do not, however, automatically turn additional information into an investable signal.
The survey offers a similarly nuanced picture of AI’s financial impact. Fifty-six percent of buyers said their firms had measured a tangible return from AI deployment during the previous 12 months. Efficiency was the most frequently identified benefit, cited by 41% of respondents, while 17% attributed improved investment performance to AI.
The numbers suggest that AI’s near-term value in investment workflows may be easier to demonstrate through productivity than through returns. Automating research tasks, accelerating information processing and helping analysts work across large datasets are more immediately measurable than proving that an AI system directly generated superior investment performance.
Another emerging issue is how investment data will be accessed by AI systems. Neudata found that 44% of data providers surveyed offer access through Model Context Protocol (MCP), an open protocol designed to connect AI applications with external data and tools. More than half of those providers do not charge an additional fee for MCP access.
That could eventually influence how institutional investors evaluate data vendors. Instead of considering only a dataset’s format, coverage and licensing terms, buyers may increasingly assess how easily their AI systems can retrieve and work with that data.
Data quality remains a complication. One-third of buyers reported that the quality of their data sources had deteriorated over the previous one to two years. Respondents were divided over whether AI-generated content was responsible.
The issue goes beyond alternative-data vendors. As synthetic and AI-generated information becomes more widespread online, financial institutions face a growing challenge in distinguishing useful signals from duplicated, low-quality or machine-generated material.
Neudata’s findings therefore point to a market where demand remains strong but standards are becoming more demanding. Data providers need to demonstrate signal quality, while buyers are increasingly evaluating how datasets fit into AI-enabled research workflows.
For hedge funds, asset managers and financial institutions, the implication is less about buying more data and more about determining which information can withstand increasingly automated analysis. The next stage of alternative-data adoption will likely depend on that distinction.
Market Landscape
Alternative data has become an established component of institutional investment research, spanning areas such as satellite imagery, transaction data, web activity, geolocation, sentiment and other nontraditional information sources.
The growth of generative AI is adding another layer to the market. AI tools can reduce the cost and time involved in extracting information from unstructured sources, while emerging interfaces such as MCP are creating new mechanisms for connecting external datasets to AI applications.
Neudata’s findings indicate that buyers continue to allocate budgets to the category, but signal quality remains central to purchasing decisions. At the same time, concerns surrounding data quality and AI-generated content introduce additional diligence requirements.
For vendors, this creates a competitive environment where dataset uniqueness, measurable investment relevance, accessibility and data provenance can matter alongside pricing.
Top Insights
- Ninety-seven percent of surveyed alternative-data buyers expect spending to increase or remain stable, signaling sustained institutional demand for investment information.
- AI’s measured financial value currently appears stronger in efficiency than investment performance, with 41% citing efficiency gains versus 17% performance improvements.
- MCP adoption is emerging among data providers, potentially making AI accessibility an increasingly relevant consideration when investment firms evaluate datasets.
- Data quality is becoming a strategic concern as one-third of surveyed buyers reported deterioration across their data sources during recent years.
- Signal strength remains more important than price in failed data trials, highlighting the pressure on vendors to demonstrate actionable investment value.
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