Gartner says AI-first enterprises will depend on agents, governance and data platforms

Gartner expects more than one in 10 enterprises to become AI-first by 2030, with stronger performers moving faster in the adoption of AI agents, semantics and converged data and analytics platforms.

The research firm said its top data and analytics trends for 2026 are shaped by three areas: agentic data and analytics, semantics, and platform convergence. Together, they point to a more demanding phase of enterprise AI adoption, where organisations need stronger data foundations, clearer governance and fewer fragmented tools before AI agents can operate reliably across business functions.

Gartner defines an AI-first enterprise as an organisation with an enterprisewide commitment to considering AI alongside other options and using it where it makes sense for core decisions and investments. The firm said common blockers include organisational fragmentation, complex IT systems, disconnected data silos and analytics technologies that limit enterprise-wide AI adoption.

“Organisations are moving rapidly toward 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 its full potential across the business.”

AI agents increase the need for decision control

Gartner expects AI agents to become a bigger part of data and analytics operations, but the firm also warns that automated decisions need stronger oversight. As AI agents begin to support or execute strategic, tactical and operational decisions, weak governance can increase legal, operational and reputational risk.

The research firm predicts that by 2029, 20% of data and analytics leaders will have adopted agentic data and analytics, with AI agents automating data management, data streaming and decision governance for safety. The approach uses AI agents to accelerate the flow from data to business impact, including real-time data processing, adaptive data management and more responsive operations.

Decision governance is a core part of that shift. Gartner said it applies governance principles to decision intelligence, so automated decisions can be explained, audited and aligned with intended outcomes. The firm predicts explicitly modelled business decisions will be five times more trusted and 80% faster than ungoverned decisions by 2029, supported by decision intelligence platform adoption.

AI governance platforms are also expected to gain importance as organisations deal with more complex AI regulations, new AI risks and the wider use of autonomous AI agents. Gartner said these platforms can help organisations apply centralised oversight, risk management frameworks and controls across responsible AI principles.

Real-time data needs must be judged carefully

Gartner expects agentic data streaming to become more widely adopted as organisations build AI agents that depend on live information. The firm predicts adoption of data streaming for agentic AI will rise beyond 60% by 2028, up from under 15% in 2025.

The trend is tied to use cases where batch-based data processing is too slow, such as decision intelligence, autonomous operations and digital twins. However, Gartner’s report also cautions organisations against treating every AI use case as a real-time problem. It recommends starting with a business-driven latency assessment and using micro-batching where near-real-time data movement is sufficient.

Agentic data management is another area Gartner expects data and analytics leaders to consider over the next two years. The approach uses AI agents to improve data processes by enabling real-time actions, identifying patterns and recommending changes that can help data teams respond faster.

“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.”

Semantics and GraphRAG target reliability gaps

Gartner places semantics at the centre of AI-ready data and analytics architecture. In practical terms, that means organisations need consistent definitions, business logic and context across platforms before AI systems can produce reliable answers for users and AI agents.

The report identifies composite semantic layers and GraphRAG as two approaches that can improve reliability. Composite semantic layers coordinate data products, knowledge graphs and business intelligence models across an organisation’s data and analytics architecture. GraphRAG combines knowledge graphs with large language models to help AI systems retrieve information, apply context and answer complex enterprise questions more accurately.

Gartner predicts that organisations using composite semantic strategies with GraphRAG to reduce inference costs will improve response and reliability by 50% by 2028. It also predicts that 40% of enterprises will have used GraphRAG techniques by 2029 to improve factual accuracy and the reasoning capabilities of LLMs.

The report cited Microchip Technology as an example of GraphRAG being used to support customer service. According to Gartner, the company built a GraphRAG-powered chatbot and private LLM to help its customer service team access order and production data without relying on engineering and operations teams for routine queries.

Platform convergence meets sovereign AI pressure

Gartner expects more enterprises to replace fragmented data and analytics tools with converged platforms that bring together data management, analytics, governance and agentic capabilities. The firm predicts that by 2030, more than 50% of enterprises will have used a single platform combining data, analytics, governance and agentic features to support an AI-first strategy.

The pressure to consolidate is partly operational. Gartner said organisations have deployed an average of a dozen data management solutions and often struggle to realise AI goals at scale. It also said half of chief data and analytics officers consider optimisation of the technology landscape to be their primary responsibility.

The report cited Toyota Motor Europe as an example of a composite semantic layer approach, with business domains able to create their own models within governance and certification controls. Gartner also cited organisations such as BDO, Toyota and WPP as examples of companies using data management platforms to make data accessible as products, support natural language queries, apply governance and enable GenAI applications.

Sovereign AI adds another constraint to data and analytics planning. Gartner said nation states are prioritising control over AI capabilities as AI becomes tied to economic strength, which may require organisations to localise data and analytics controls while managing different sovereign AI strategies.

“Sovereign AI is fundamentally changing how organisations think about control, innovation and resilience in their AI strategies,” said 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.”