For the better part of the past decade, enterprise data catalogs and governance tools occupied the periphery of corporate IT architecture. They were viewed primarily as utility players—essential tools for ticking compliance boxes, managing audits, and maintaining documentation, but rarely treated as central components of overarching business strategy. Today, the rapid acceleration of artificial intelligence has fundamentally altered that dynamic.
As organizations rush to deploy generative AI and autonomous agents, a harsh reality has come to light: an AI model or enterprise agent is only ever as trustworthy as the underlying data that feeds it. Consequently, the platforms responsible for cataloging, governing, and delivering that data have been pulled rapidly from the operational background to the very center of corporate strategy. Choosing the right data intelligence platform is no longer an operational afterthought; it has become a critical leadership decision that dictates whether enterprise AI initiatives will thrive or stall out.
This seismic shift in enterprise priorities is heavily reflected in recent industry research. According to IDC’s 2025 Office of the Chief Data Officer (CDO) Survey, a staggering 86% of organizations have reported a deliberate and substantial increase in their focus on data management specifically designed to support generative AI and agentic AI initiatives. Executive attention and corporate budgets are rapidly following this same strategic path, concentrating heavily on the architectural layers that assemble and deliver trusted data.
It is against this transformative backdrop that IDC has positioned Precisely in the Leaders category in the recently published 2026 IDC MarketScape: Worldwide Data Intelligence Platform Software Vendor Assessment. Authored by Marlanna Harrington, Senior Research Analyst for Data Platforms at IDC, and Stewart Bond, Vice President of Data Intelligence and Integration Software at IDC, the report evaluates how software vendors are adapting to a market where traditional data governance has merged entirely with AI readiness.
From Governance Tooling to AI Infrastructure
The integration of artificial intelligence into the core of data management has transformed what buyers expect from software vendors. The 2026 IDC MarketScape evaluates 15 different vendors across the landscape, and a notable degree of strategic alignment has emerged among them. Every single evaluated vendor reported a concrete plan to embed generative AI capabilities—and, increasingly, agentic AI workflows—directly into their cataloging, data quality, and governance tooling. Furthermore, nearly all of these platforms now expose governed metadata directly to AI agents utilizing the Model Context Protocol or equivalent open interfaces.
One of the clearest and most pronounced signals in the current market is the meteoric rise of the digital data steward. These are intelligent agents designed to take over the traditionally slow, labor-intensive manual work of documenting data assets, drafting rigorous quality rules, and proposing remediation steps for data errors, while surfacing their underlying reasoning for human review and approval. For most modern enterprises, the primary constraint on scaling data initiatives is not a shortage of advanced tooling, but a severe shortage of skilled personnel and available hours. Automation capable of executing complex data stewardship work at a massive scale is rapidly becoming baseline table stakes for software providers.
Concurrently, a parallel shift is reshaping how organizations approach openness and interoperability. Modern data products and data contracts are increasingly being built on open specifications, while data lineage tracking and storage infrastructure interoperate seamlessly through widely adopted industry standards such as OpenLineage and Apache Iceberg. This evolution leaves technology buyers materially less exposed to proprietary vendor lock-in than they were even two years ago. As a result, the real points of differentiation between competing software platforms have shifted toward architectural robustness and native depth.
Looking toward the immediate future, market demand is being pulled forward by two distinct directions. Together, these vectors mark a profound evolution: a transition from merely governing the legacy data organizations already understand and utilize, to governing the vast universe of information that advanced AI models can now reach, ingest, and operationalize.
Underpinning every single one of these market shifts is a singular, fundamental job to be done: supplying reliable context. IDC treats context not as a separate, isolated market segment, but rather as the primary use case that data intelligence exists to serve. An autonomous AI agent that does not inherently know what a specific dataset means, where it originated, or whether its contents can be trusted will inevitably produce confident yet ungoverned—and potentially disastrously incorrect—answers. Data intelligence is, at its core, the critical mechanism that transforms raw, unorganized metadata into actionable context and reliably delivers it to both human decision-makers and automated machines.
Why the Decision Now Drives AI ROI
The financial and operational stakes of getting this architectural choice right are entirely measurable, and the line connecting data intelligence to business value is remarkably direct. Cataloging, lineage tracking, data quality management, and comprehensive governance are the foundational elements that enable a technical team to transform a raw, siloed dataset into a polished data product featuring accountable ownership, easily discoverable access, and a clearly defined business purpose.
IDC’s extensive research directly links high levels of data product maturity to superior artificial intelligence outcomes. Organizations that achieve high data product maturity are reported to be 5.5 times more likely to have generative AI applications successfully running in production environments. Furthermore, they are 4.6 times more likely to have agentic AI deployed in production compared to their low-maturity industry peers. Beyond deployment rates, these mature organizations report roughly three times the overall financial and operational improvement from their technology investments.
This measurable divergence explains why selecting a sophisticated data intelligence platform is now recognized as a material driver of AI return on investment, rather than a routine IT procurement decision. Precisely earned its position as a Leader in the 2026 IDC MarketScape assessment based on a comprehensive evaluation of both its current software capabilities and its forward-looking strategic roadmap, measured precisely against the criteria that matter most as the enterprise software market pivots toward AI-ready data and trusted operational context.
As enterprises worldwide grapple with the challenges of scaling their AI initiatives while satisfying boardrooms and regulators alike, events and discussions centered around these shifts continue to gain momentum. For organizations seeking to explore these themes further, upcoming industry forums—such as the virtual event "Precisely Now: Where Data Meets AI," featuring insights from Precisely CEO Walid Abu-Hadba, Chief Product Officer Matt Waxman, IDC analyst Stewart Bond, and enterprise customers successfully navigating the journey toward trusted, AI-ready data—highlight the growing urgency of building a solid data intelligence foundation today.
