As artificial intelligence moves rapidly from experimental testing into core enterprise operations, organizations worldwide are discovering a stubborn bottleneck: their underlying data is simply not ready. On October 6, data integrity specialist Precisely launched a sweeping new strategy designed to address this challenge head-on. The company introduced the Precisely Platform, Precisely AI Studio, and a series of new Model Context Protocol (MCP) servers, marking a major turning point in how enterprises prepare their data ecosystems for autonomous systems.
This strategic shift is built around a singular, urgent purpose: helping organizations make their data AI-ready so they can deploy artificial intelligence with absolute confidence. For decades, companies have accumulated a vast array of specialized data management tools, stitching them together over time to handle integration, governance, quality, and location intelligence. However, as enterprises race to implement advanced AI and agentic systems, this fragmented approach has begun to fail catastrophically.
The fundamental complication lies in the nature of AI itself. When organizations adopt fragmented tools, those individual applications often fail to share common elements such as data pipelines, metadata, or unified security frameworks. Traditionally, enterprises have relied on human analysts to identify and resolve the resulting discrepancies and errors. But when AI is introduced into each isolated tool, this model breaks down completely. Because artificial intelligence introduces uncertainty and operates at speeds that bypass traditional human review, neither human workers nor automated systems can easily track down and fix the data anomalies that emerge.
Addressing these deep-seated gaps requires a unified data management platform that can act as a comprehensive control plane across an entire corporate data ecosystem. By establishing shared metadata and consistent semantics, a unified platform allows a single business definition or data quality rule to be set once and applied uniformly across the enterprise. Furthermore, modern enterprises require a system that manages data directly where it lives—whether on legacy mainframes, SAP systems, on-premises servers, or multi-cloud environments—without forcing disruptive data migrations. It must also remain completely open to whichever analytical tools and artificial intelligence models a team chooses to deploy.
The newly introduced Precisely Platform directly addresses these enterprise requirements by bringing together the company’s core capabilities onto a single, shared metadata and semantic foundation. Data integration, data quality, data governance, location intelligence, master data management, and customer communications are now integrated into one cohesive environment. Additionally, the platform features built-in data enrichment, supplying the rich, real-world context that advanced analytics and AI models require to function accurately.
This evolution is a natural continuation of Precisely’s long-standing role in the enterprise software market. For decades, the company has specialized in solving complex data challenges, establishing robust data quality standards for heavily regulated industries, enforcing rigorous data governance, and managing the intricate mainframe and SAP systems that power many of the world’s most critical financial and operational transactions. Today, this extensive foundation supports more than 12,000 customers across more than 100 countries, including an impressive 92 percent of the Fortune 500.
What has fundamentally changed in the current technological landscape is that artificial intelligence has transformed data integrity from a back-office necessity into the absolute center of gravity for modern business strategy. The market response to this strategic pivot has been swift and substantial. Nearly 2,000 data leaders, technical builders, corporate customers, and industry partners registered for the company’s recent Precisely Now virtual event, exploring what it takes to transition from theoretical AI exploration to deploying production-ready systems backed by trusted data. Industry discussions have clearly shifted away from debating whether artificial intelligence requires trusted data, focusing instead on the practical roadmap required to achieve it.
Despite the excitement surrounding generative and agentic AI, significant hurdles remain across the broader enterprise landscape. Industry analysts have pointed to sobering projections regarding enterprise technology investments. Notably, research from Gartner has predicted that over 40 percent of agentic AI projects will be canceled by the end of 2027, driven largely by escalating operational costs, unclear business value, and inadequate risk controls. A closer examination of these failed initiatives reveals that many of the underlying problems trace directly back to data infrastructure. When AI agents operate on information that is hidden, unreliable, inconsistently governed, or missing vital real-world context, projects quickly stall or fail to deliver sustainable value.
To help enterprises evaluate their readiness, Precisely defines AI-ready data through four essential characteristics that must all be present before information is handed over to autonomous systems. First, data must be visible to the AI solutions that require it. Second, it must be demonstrably reliable in terms of quality. Third, it must be fully accountable through rigorous governance and lineage tracking. Finally, it must be complete, enriched with the granular real-world context—such as verified property details, business attributes, or associated demographics—that gives raw figures genuine meaning.
For organizations looking to take their next operational step, industry experts recommend auditing a single stalled artificial intelligence project through the lens of these four criteria. By examining whether the project’s underlying data is truly visible, reliable, accountable, and complete, technical leaders can quickly pinpoint the exact structural gaps that need attention first.
Launch day represents merely the starting point for this new phase of enterprise data management. The Precisely Platform is scheduled to transition from its current preview phase to general availability in early 2027. Meanwhile, the specialized assets available within Precisely AI Studio will continue to expand, and broader Model Context Protocol support will be rolled out across additional Precisely product offerings, ensuring that enterprises have the continuous support they need to put artificial intelligence to work securely and effectively.
