Gartner projected that by 2030, more than 10% of companies worldwide will transition to 'AI First' enterprises to secure a competitive advantage. AI agents, semantics, and integrated data and analytics (D&A) platforms were identified as key drivers that will lead the future D&A trend.

On the 17th, Gartner announced six key D&A trends that companies should consider when formulating strategies over the next two years. It stated that it expects companies that have transitioned to AI First to outpace their competitors in the adoption of AI agents, semantics, and integrated D&A platforms.

Carlie Idoine, VP Analyst at Gartner, explained, "Companies are rapidly transitioning to AI-first operating models that make AI a core standard for all business decisions, work processes, and investments," adding, "Without clear, company-wide commitment, it is difficult to continuously realize the potential of AI across the business."

Gartner analyzed that as AI emerges as a key element of economic power, countries are prioritizing control over their domestic AI capabilities.

Accordingly, the localization of D&A control was presented as a geopolitical factor to consider in a company's AI-first transformation roadmap. In terms of decision-making governance, the following trends were identified: accelerating sovereign AI, decision-making governance for mitigating AI agent risks, and governance platforms for responsible AI implementation.

Gartner predicted that adopting a decision intelligence platform would increase reliability fivefold and speed up processing by 80% by 2029 compared to the absence of governance.

In the field of data operations, agentic data streaming for real-time intelligence, agentic data management for operational efficiency, and GraphRAG for responding to complex use cases were presented.

Gartner predicted that the adoption rate of data streaming for agentic AI will increase from less than 15% in 2025 to over 60% in 2028.

In addition, it was explained that graph search augmentation combines knowledge graphs and large-scale language models (LLM) to reflect contextual meaning, and it was projected that by 2029, 40% of companies would utilize this technology to enhance the factual accuracy of responses and LLM inference capabilities.