AI数据互联互通状况报告_2026年展望_39页_1mb

Summary of the State of AI Data Connectivity Report: 2026 Outlook

Core Content

The State of AI Data Connectivity Report: 2026 Outlook highlights the critical role of data infrastructure in the success of AI initiatives. It emphasizes that while generative AI (GenAI) and autonomous AI agents are increasingly adopted, the main challenge for enterprises is not the AI models themselves, but the data infrastructure and context delivery that enable these models to function effectively.

The report is based on a survey of 200+ data and AI leaders from enterprises and software providers, and it outlines the current landscape of AI adoption, the challenges faced, and the investment priorities that define the next phase of AI maturity.

Main Findings

AI Maturity and Data Infrastructure

• AI maturity is defined by the extent to which an organization has operationalized AI, moving beyond experimentation to measurable business impact.
• The strongest predictor of AI success in 2026 is the maturity of the underlying data infrastructure.
• 60% of companies with the highest AI maturity also have the most mature data infrastructure.
• 53% of companies with immature AI have immature data systems.
• The report outlines a five-stage AI maturity framework:
  • Experimenting
  • Implementing
  • Scaling
  • Optimizing
  • Leading

Data Integration Challenges

• 71% of AI teams spend over a quarter of their implementation time on data integration.
• 46% of organizations require real-time access to six or more data sources for an average AI use case.
• AI-native software providers are 3x more likely to require more than 26 external data integrations compared to traditional providers.
• 100% of organizations say real-time data is necessary for AI agents and customer service automation, but only 80% have begun implementing real-time integration, and most are still in early stages.

Investment Priorities

• 83% of organizations are implementing or planning a centralized, semantically consistent data access layer, far surpassing investment in AI models (9%).
• Structured data sources are the primary focus for AI features (58%), while unstructured data is used less frequently (11%).

AI Use Cases

• Employee or agent co-pilot (79%) and customer support automation (70%) are the most prevalent applications of enterprise AI.
• Code generation (60%) and marketing/content generation (55%) are also widely adopted.
• AI is being used to augment human workflows rather than replace them, with a focus on low-risk, high-impact use cases.

AI Tool Sprawl and Integration Complexity

• 76% of organizations use enterprise LLM platforms, but the AI technology stack is not centralized.
• AI tool sprawl leads to context fragmentation and integration complexity.
• 83% of organizations have built or plan to build centralized, semantically consistent data access, indicating a growing recognition of the need for unified semantics.

Real-Time Integration as a Maturity Marker

• 66% of enterprises require real-time or near-real-time data for over a quarter of their AI use cases.
• 80% of respondents support real-time or hybrid integration.
• Real-time integration is a strong indicator of AI maturity, with 100% of "leading" enterprises supporting it, while only 40% of low-maturity enterprises do.

Governance and Security Concerns

• Data quality and security are the top challenges for enterprise AI adoption.
• 73% cite data quality as a top blocker, and 66% cite security and privacy as a top challenge.
• 29% list data connectivity gaps as the #1 blocker to enterprise AI success.

Implications

• Data infrastructure is the real bottleneck for AI adoption.
• Investing in data integration is critical to improving AI success rates and time-to-value.
• Centralized, semantically consistent data access is becoming a non-negotiable requirement for scalable AI.
• Real-time data integration is a key differentiator in AI maturity and success.

Key Initiatives and Investments

• Enterprises are increasingly adopting centralized data access layers.
• AI-native software providers are better positioned to meet the growing demand for data integration.
• Governance and security are top priorities in AI strategy.
• Real-time integration is a clear indicator of AI maturity and success.

Conclusion

The report underscores that data connectivity and infrastructure maturity are now the primary enablers of AI success in enterprises. It suggests that organizations must prioritize data integration, semantic consistency, and real-time data access to fully leverage AI capabilities and achieve measurable ROI. For software providers, aligning with these enterprise priorities is essential to remain competitive and meet the evolving needs of the market.