More Than 10% of Enterprises Projected to Go AI-First by 2030, Gartner Reports

Brickinfo News Agency – More than one in 10 enterprises are projected to adopt an AI-first operating model by 2030, outperforming their competitors through the integration of AI agents, semantics, and converged data and analytics (D&A) platforms. According to tech insights firm Gartner, Inc., this rapid shift is pushing organizations to treat artificial intelligence as a core element in every business decision, workflow, and investment. To navigate this transition, experts emphasize that companies must commit to enterprise-wide strategies to fully realize the technology’s potential across their operations.

“Organizations 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, organizations will struggle to consistently realize its full potential across the business.”

Geopolitical shifts are heavily influencing these strategies, particularly through the rise of sovereign AI. As artificial intelligence becomes a marker of economic strength, nation states are increasingly prioritizing control over their own AI capabilities to minimize reliance on foreign countries. This external reality requires organizations to localize their D&A control and modernize their roadmaps. Idoine noted that sovereign AI is fundamentally changing how organizations think about control, innovation, and resilience, adding that companies must advance their AI use cases from basic utilization to competitive advantage.

The rising autonomy of AI agents in executing strategic, tactical, and operational decisions has also introduced significant legal, operational, and reputational risks. To counter this, Gartner highlights the necessity of decision governance, which applies governance principles to decision intelligence to ensure automated actions remain explainable and auditable. Driven by the adoption of decision intelligence platforms, the firm predicts that explicitly modeled business decisions will be five times more trusted and 80% faster than ungoverned decisions by 2029.

Furthermore, increasing global regulatory complexity and autonomous agent adoption are rendering standard assurance methods obsolete. Gartner recommends that D&A leaders deploy AI governance platforms to centralize oversight, enforce necessary controls, and apply risk management frameworks that align with corporate policies and responsible AI principles.

Data processing methods are similarly shifting to support these autonomous systems through agentic data streaming. Unlike traditional batch-based processing, continuous, event-driven data flows allow AI agents to execute tasks with higher speed and accuracy. Driven by the pressure for real-time responsiveness, Gartner predicts that the adoption of data streaming for agentic AI will surge beyond 60% by 2028, up from less than 15% in 2025. Organizations are advised to prioritize real-time data use cases such as digital twins, autonomous operations, and decision intelligence.

Managing increasingly complex data environments remains a major challenge for modern teams trying to achieve AI readiness. By embedding AI agents directly into core data management workflows, data teams can utilize self-learning systems to enable real-time actions, pattern detection, and recommendations. “Integrating AI agents into data management workflows enables data teams to operate more adaptively using self-learning systems,” Idoine stated. “Establishing strong governance and continuously monitoring performance will be essential to ensure these capabilities deliver consistent, business-aligned outcomes.”

Finally, standard retrieval-augmented generation (RAG) approaches are proving insufficient for enterprises requiring high accuracy for complex, context-rich queries. To address this, organizations are turning to GraphRAG, a technique that combines knowledge graphs with large language models (LLMs) to enhance how systems retrieve information and apply contextual meaning. Gartner projects that 40% of enterprises will leverage GraphRAG techniques by 2029 to improve the factual accuracy and reasoning capabilities of their AI models.