Gartner says more than 10% of firms will be AI-first

More than one in 10 enterprises will be AI-first by 2030, according to Gartner. The research group linked that shift to data and analytics trends focused on AI agents, semantics and converged platforms.

Carlie Idoine, VP Analyst at Gartner, presented the forecast as part of a broader set of data and analytics trends that organisations should factor into strategy over the next two years.

Gartner defines AI-first as an operating model in which artificial intelligence is a core consideration in business decisions, workflows and investment choices. It argues that companies adopting that model will move faster than rivals in deploying AI agents and connecting data, analytics and semantic technologies.

"Organisations are moving rapidly toward an AI-first operating model, where AI is now a core consideration in every business decision, workflow and investment. Without a clear, enterprise-wide commitment, organisations will struggle to consistently realise its full potential across the business," said Idoine, VP Analyst at Gartner.

Sovereign AI

One of Gartner's main themes is sovereign AI, reflecting growing concern among governments and businesses about dependence on foreign technology and infrastructure. Local control over data and analytics is becoming more important as countries seek to strengthen economic resilience and retain oversight of AI development.

That pressure is spilling into corporate planning. For multinationals and heavily regulated sectors in particular, decisions about where data sits, which models can be used and who controls supporting systems are becoming strategic rather than purely technical questions.

"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."

Governance pressure

Another key area is the risk of AI agents making decisions with limited oversight. As automated systems take on more strategic, tactical and operational decisions, the legal, operational and reputational stakes rise where governance is weak.

Gartner pointed to decision governance as a way to make automated decisions more explainable and auditable. It predicts explicitly modelled business decisions will be five times more trusted and 80% faster than ungoverned decisions by 2029, with decision intelligence platforms playing a central role.

The firm also highlighted a broader need for AI governance platforms as regulatory demands increase and autonomous agents become more common. Traditional assurance processes are no longer enough to manage emerging AI risks or maintain alignment with corporate policy and industry rules.

In practice, these platforms are designed to give organisations central oversight of AI use, apply risk management frameworks and enforce controls across systems and teams. Governance, in Gartner's view, is moving from policy documents into software and operational processes.

Real-time data

The rise of AI agents is also changing expectations for how quickly data must move through organisations. Gartner identified agentic data streaming as a major trend, arguing that event-driven data flows are becoming more important than batch processing for companies that want systems to respond in real time.

It forecasts that demand for faster responses will push data streaming adoption for agentic AI to more than 60% by 2028, up from less than 15% in 2025. The shift is likely to matter most in areas such as decision intelligence, autonomous operations and digital twins, where delays in data handling can limit the usefulness of AI systems.

Alongside streaming, organisations are starting to apply AI agents to data management itself. The aim is to automate routine tasks, spot patterns and support faster operational responses as datasets grow larger and more complex.

"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."

Accuracy focus

The final trend Gartner highlighted is GraphRAG, which combines knowledge graphs with large language models to improve how systems retrieve and connect information. The approach is aimed at enterprise use cases where standard retrieval-augmented generation methods can struggle with context-heavy or complex questions.

Gartner said GraphRAG could improve factual accuracy and reasoning in AI outputs, and it predicts 40% of enterprises will have used the technique by 2029. That points to a broader shift from basic generative AI deployment toward making outputs more reliable in business settings where mistakes carry financial or regulatory consequences.

Taken together, the trends suggest that the next phase of AI adoption in large organisations will depend less on experimentation and more on control, trust, data flow and accuracy. Gartner's central argument is that companies that align those elements early are most likely to operate as AI-first enterprises by the end of the decade.