More than one in 10 enterprises will be AI-first by 2030, outperforming competitors in the adoption of AI agents, semantics and converged data and analytics (D&A) platforms, according to the business and technology insights company Gartner Inc. These three areas are the driving forces behind the top trends for D&A.
"Organisations are moving rapidly towards an AI-first operating model, where AI is now a core consideration in every business decision, workflow and investment," said Carlie Idoine, VP analyst at Gartner.
"Without a clear, enterprise-wide commitment, organisations will struggle to consistently realise their full potential across the business."
Gartner recommends that organisations factor the following D&A trends into their strategies over the next two years.
Trend 1: Sovereign AI accelerates: As AI becomes a key to economic strength, nation states are prioritising control over their own AI capabilities, minimising reliance on foreign countries to advance sovereign objectives. Localising D&A control is an important part of this process. This is an external geopolitical reality that many organisations must manage on their roadmap to becoming an AI-first enterprise.
"Sovereign AI is fundamentally changing how organisations think about control, innovation and resilience in their AI strategies," said Ms Idoine. "To respond effectively to the opportunities and threats presented by sovereign AI, organisations must modernise D&A roadmapping, advancing AI use cases from utilisation 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 modelled 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 organisations adhere to corporate policy, regulations and industry standards across common responsible AI principles.
Gartner recommends D&A leaders adopt AI governance platforms, which will provide centralised 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 organisations 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. Organisations must prioritise 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 Ms 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 large language models (LLMs) to improve how AI systems retrieve and connect information, apply contextual meaning and deliver more accurate results for complex use cases.
