LONDON, UK / AGILITYPR.NEWS / July 08, 2026 / CUBIG, the company building Syntitan, an AI-ready data operating layer for enterprise AI, today announced it has been recognised in two recent Gartner® research publications focused on the technologies helping organisations accelerate the adoption of Agentic AI.

CUBIG was recognised as a Sample Provider in the 2026 Gartner Emerging Tech: AI Vendor Race: Most Prominent Use Cases in Agentic AI by Industry and identified as a Tech Innovator in the 2026 Gartner Emerging Tech: AI Vendor Race: Tech Innovators in Agentic AI - Solution Accelerators. 

For CUBIG, these insights highlight the growing industry focus on addressing one of the most persistent barriers to enterprise AI adoption: the state of enterprise data itself. 

For many organisations, the challenge is not a lack of data. It is an abundance of enterprise data that remains difficult to use safely, reliably and consistently within AI systems. 

While AI capabilities continue to advance rapidly, many enterprises still struggle to move AI initiatives beyond proof-of-concept and into production. Their data is often fragmented, restricted by regulation or policy, uneven in quality, or disconnected from the workflows where AI is expected to operate. As a result, organisations face significant challenges deploying AI and agentic systems in production environments.

CUBIG believes the latest Gartner insights reflects this shift in enterprise priorities. The Tech Innovators research highlights technologies that provide enterprise context, semantic data layers, governance and operational orchestration as becoming increasingly important for production-scale Agentic AI. According to Gartner, “Vendors that integrate enterprise context deliver greater value than those that only focus on model optimization”. Gartner also predicts: “By 2027, 40% of enterprises will demote or decommission autonomous AI agents due to governance gaps identified only after production incidents occur”.

CUBIG believes these findings reinforce the growing need for what it describes as the missing operational data layer for enterprise AI. 

Through Syntitan, CUBIG enables organisations to prepare, validate and connect enterprise data states for training, evaluation, execution and governance across AI workflows. The platform brings together capabilities including DTS, which transforms restricted, scarce or low-quality enterprise data into AI-ready states while preserving its statistical structure, and LLM capsule, a context-preserving data layer that lets AI work on sensitive operational data that cannot move in raw form, reconstructing usable results inside the organisation’s own environment.

“Enterprise AI does not fail only because models are incapable. It often fails because the data state behind an AI run was never designed to be reused, traced or reproduced,” said Bae Ho, Founder and CEO of CUBIG.

“Organisations have invested heavily in data platforms, governance frameworks and AI infrastructure. Yet there remains a critical gap between managed data and operational AI. We believe this recognition from Gartner reinforces the need for an AI-ready data operating layer that makes enterprise data usable, traceable and ready for AI workflows.”

The Solution Accelerators research also highlights a deployment of CUBIG's technology within a leading South Korean life insurer. According to Gartner, “The solution increased classification accuracy by 85.9% to 90%, a 200% improvement over rule-based systems’ 50% to 60% accuracy”, enabling the retention of high-utility behavioural insights beyond the six-month legal limit.

“Agentic AI may change how organisations operate, but autonomous systems are only as reliable as the data states they run on,” added Ho. 

“As enterprises move from experimentation to production, data readiness, traceability and operational trust become foundational requirements. AI-ready data is not just a preparation step. It is part of the operating layer for scalable enterprise AI.”

CUBIG has recently expanded its international market presence as demand grows for technologies that help enterprises bridge the gap between data infrastructure and real-world AI deployment. The company works with organisations operating in regulated and data-intensive sectors including financial services, healthcare, telecommunications, manufacturing and the public sector.