NEW YORK — In a major strategic shift aimed at solving one of the most persistent bottlenecks in enterprise technology, data integrity company Precisely introduced a suite of new capabilities on October 6 designed to prepare corporate data for autonomous artificial intelligence systems. The announcements include the new Precisely Platform, Precisely AI Studio, and a series of Model Context Protocol (MCP) servers. Together, these offerings mark a new chapter for the company, uniting its core data management functionalities under a single purpose: helping organizations ensure their data is fully AI-ready so they can deploy artificial intelligence tools with absolute confidence.
The launch arrives at a critical juncture for enterprise technology. While artificial intelligence has dominated corporate investment strategies over the last few years, many businesses have discovered that their underlying information architectures are simply not prepared to support advanced autonomous systems. Gartner has predicted that a significant percentage of agentic AI projects will face cancellation due to escalating costs, unclear business value, and inadequate risk controls. According to industry experts, many of these challenges trace back to foundational data issues—information that is either trapped out of reach, compromised by poor quality, inconsistently governed, or missing crucial real-world context. Unlike traditional analytics, where human oversight can catch and correct errors on the fly, autonomous AI agents act directly on information without the safety net of human review, making data integrity more vital than ever.
What Organizations Need to Move Forward: Four Characteristics of AI-Ready Data
To help enterprises navigate this transition, Precisely has established a definitive framework outlining what it means for data to be truly AI-ready. Before organizations hand their sensitive information over to autonomous systems, industry leaders argue that data must possess four essential characteristics.
First, data must be visible to the AI solutions that require it, breaking down isolated silos across the enterprise. Second, it must be reliable in quality, free from corruption, duplication, or systemic errors. Third, it must be accountable through rigorous governance, clear lineage, and transparent tracking. Finally, it must be complete with comprehensive real-world context, such as the physical properties, business networks, or demographic trends associated with a verified address.
For years, enterprises have attempted to achieve these standards by assembling a patchwork of individual tools, stitching them together one capability at a time. However, this fragmented approach has historically created massive operational hurdles. When capabilities are added incrementally without shared elements like common data pipelines, unified metadata, or centralized security frameworks, they inevitably fail to communicate effectively. Historically, organizations relied on human analysts to manually identify and resolve the resulting discrepancies.
When artificial intelligence is introduced into this fragmented environment, the traditional approach completely breaks down. Because AI introduces layers of uncertainty and speed at multiple operational points, it becomes virtually impossible for either humans or automated systems to pinpoint and rectify data issues before they impact business outcomes.
Closing these critical operational gaps requires a unified data management platform capable of acting as a centralized control plane across an organization’s entire data ecosystem. By utilizing shared metadata and semantics, a unified platform allows a single quality rule or business definition to be established once and applied uniformly across the enterprise. Furthermore, a modern approach means managing information precisely where it lives—whether in modern cloud warehouses, legacy mainframes, or SAP environments—without forcing disruptive and costly data migrations, while remaining completely open to whichever models and tools an engineering team prefers.
The newly introduced Precisely Platform directly addresses these enterprise needs by bringing together a wide array of established data capabilities. Customers can now access data integration, data quality, data governance, location intelligence, master data management, and customer communications management on a single, cohesive foundation of shared metadata and semantics. Furthermore, the platform features built-in data enrichment capabilities designed to supply the rich, real-world context that advanced analytics and AI models demand to generate accurate outputs.
Why This Is the Moment for Precisely
The strategic pivot toward unified data management for artificial intelligence builds upon decades of deep industry experience. Over the years, Precisely has built its reputation by helping global enterprises solve their most intricate and demanding data challenges. The company has invested heavily in ensuring data quality for heavily regulated industries, providing robust data governance frameworks that other software providers historically treated as an afterthought, and securing the mission-critical mainframe and SAP systems that process many of the world’s most consequential financial and operational transactions.
Today, this foundational work supports more than 12,000 enterprise customers across more than 100 countries, including an overwhelming 92% of the Fortune 500. What has fundamentally changed in the current market environment is that the advent of artificial intelligence has transformed this foundational data work from a background administrative necessity into the main event of corporate IT strategy.
The market response to this evolution has been swift and enthusiastic. Nearly 2,000 data leaders, engineers, enterprise customers, and technology partners recently registered for the Precisely Now virtual event, exploring the intersection of enterprise data and artificial intelligence, and examining the rigorous steps required to put AI to work using trusted information. Industry observers note that corporate leadership has largely moved past the initial phase of asking whether artificial intelligence requires trusted data, and is now actively searching for practical roadmaps on how to achieve it.
What Comes Next for AI-Ready Data
While the October 6 product launch represented a major milestone, company executives emphasize that it is merely a starting point for the broader market. The Precisely Platform is scheduled to transition from its current preview phase to full general availability in early 2027. Meanwhile, the collection of specialized assets within Precisely AI Studio is slated to expand continually, and support for the Model Context Protocol will be extended to a wider range of Precisely products in the near future.
For organizations seeking to jumpstart their own modernization initiatives, industry advisors suggest beginning with a practical exercise. Taking a single artificial intelligence project that has stalled or underperformed, and evaluating its underlying data against the criteria of visibility, reliability, accountability, and completeness, often reveals precisely where hidden structural gaps exist. Addressing those specific vulnerabilities provides IT leaders with a clear, actionable starting point for building a secure, reliable foundation for enterprise artificial intelligence.
