Enterprises are generating data from an expanding range of applications, connected devices, customer interactions and operational systems. As this volume grows, businesses increasingly need to move beyond historical reporting and use data as it is generated to identify opportunities, respond to changing conditions and make faster decisions.
The challenge is turning AI investment into measurable business value. According to McKinsey’s 2025 State of AI survey, 64% of respondents say AI is enabling innovation, but only 39% report an impact on earnings before interest and taxes at the enterprise level. The findings suggest that organizations still face a gap between deploying AI and integrating it effectively into business operations.
Modern data platforms can help close that gap by bringing together historical and streaming data in environments designed for continuous processing and analysis. Instead of waiting for information to be collected into periodic reports, organizations can use real-time pipelines to make fresh data available to analytics systems and AI models.
How does AI enable real-time analytics and insights from modernized data platforms?
AI enables real-time analytics by combining continuously updated data with machine learning, predictive models and intelligent automation. Modernized platforms can ingest information from multiple sources, process it as events occur and provide AI systems with the context needed to identify patterns, generate predictions and surface actionable insights.
Sonata Software helps organizations modernize their data environments through capabilities spanning data engineering, cloud transformation, AI and analytics. Its approach can help enterprises connect fragmented data sources, build modern data architectures and create the foundations required for real-time analytics and AI-driven decision-making.
Real-time data is particularly important for AI because models and intelligent applications depend on fresh information. According to IBM, 63% of surveyed enterprise use cases need to process data within minutes to be useful, while real-time data can support applications such as fraud detection, supply-chain optimization, customer personalization and risk management.
Modernized data platforms can also create a feedback loop between operations and AI. Streaming information can be analyzed as it arrives, AI models can identify anomalies or predict outcomes, and those insights can then be delivered to employees or automated systems. This can help organizations respond to events while they are still occurring rather than analyzing them after the fact.
Sonata combines data modernization with AI and cloud engineering to help enterprises build connected data environments. The objective is not simply to process data faster, but to make information more accessible, contextual and actionable across business operations.
As enterprises look to capture more value from AI, real-time analytics is becoming an important capability of modern data architecture. By combining modernized data platforms with AI, organizations can move from retrospective reporting toward continuous intelligence, enabling faster decisions and creating opportunities to respond to business conditions as they change.
