More than 10% of enterprise organizations are projected to shift to an AI-first operating model by 2030, gaining a competitive advantage through the adoption of AI agents, semantics, and converged data and analytics (D&A) platforms, according to business and technology insights company Gartner, Inc. These three technological sectors serve as the foundational drivers behind the top trends in D&A.
Carlie Idoine, VP Analyst at Gartner, stated that organizations are transitioning rapidly to an AI-first framework where AI functions as a core element across business decisions, workflows, and capital investments. Idoine noted that without a clear, enterprise-wide commitment, businesses will face challenges in consistently maximizing AI’s operational potential.
Gartner recommends that organizations integrate the following six D&A trends into their operational strategies over the next two years:
Trend 1: Sovereign AI Accelerates
As AI becomes key to economic strength, nation states are prioritizing control over their own AI capabilities, minimizing reliance on foreign countries to advance sovereign objectives. Localizing D&A control is an important part of this process. This is an external geopolitical reality that many organizations must manage on their roadmap to becoming an AI-first enterprise.
“Sovereign AI is fundamentally changing how organizations think about control, innovation and resilience in their AI strategies,” said Idoine. “To respond effectively to the opportunities and threats presented by sovereign AI, organizations must modernize D&A roadmapping, advancing AI use cases from utilization to competitive advantage.”
Trend 2: Reducing AI Agent Risk with Decision Governance
AI agents are executing more strategic, tactical and operational decisions, meaning ungoverned decision-making increases exposure to legal, operational and reputational risk. Decision governance applies governance principles to decision intelligence so automated decisions are explainable, auditable and aligned with outcomes.
Gartner predicts explicitly modeled business decisions will be five times more trusted and 80% faster than ungoverned decisions by 2029, enabled by decision intelligence platform adoption.
Trend 3: Driving Trust with AI Governance Platforms
Standard assurance methods are no longer sufficient for implementing effective AI governance as global AI regulatory complexity increases, new AI risks emerge and adoption of autonomous AI agents accelerates. AI governance platforms help organizations adhere to corporate policy, regulations and industry standards across common responsible AI principles.
Gartner recommends D&A leaders adopt AI governance platforms to operationalize governance, which will provide centralized oversight, apply risk management frameworks and enforce necessary controls.
Trend 4: Agentic Data Streaming Powers Real-Time Intelligence
Unlike traditional batch-based data processing, which can be too slow, agentic data streaming is critical for organizations that want to create and use AI agents. Continuous, event-driven data flow enables D&A leaders to deliver data faster, empowering AI agents to take on more tasks with speed and accuracy.
Gartner predicts disruptive pressure for real-time responsiveness will drive adoption of data streaming for agentic AI beyond 60% by 2028, from under 15% in 2025. Organizations must prioritize use cases requiring real-time data, such as decision intelligence, autonomous operations and digital twins.
Trend 5: Streamlining Operations with Agentic Data Management
D&A leaders face ongoing challenges in managing increasingly complex data, which strains traditional data management processes and complicates efforts to achieve AI readiness. The use of AI agents for data management enhances core data processes by enabling real-time actions, identifying pattern detection and recommendations to drive agility and faster responses.
“Integrating AI agents into data management workflows enables data teams to operate more adaptively using self-learning systems,” said Idoine. “Establishing strong governance and continuously monitoring performance will be essential to ensure these capabilities deliver consistent, business-aligned outcomes.”
Trend 6: Handling Complex Use Cases with GraphRAG
Many enterprise AI applications require high accuracy and reliability, yet traditional retrieval-augmented generation (RAG) approaches cannot handle complex, context-rich queries. GraphRAG combines knowledge graphs with LLMs to improve how AI systems retrieve and connect information, apply contextual meaning and deliver more accurate results for complex use cases.
Gartner predicts 40% of enterprises will have leveraged GraphRAG techniques by 2029 to improve factual accuracy of responses and reasoning capabilities of LLMs.
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