While an overwhelming 86 percent of organizations have intensified their focus on data management to capitalize on the promise of artificial intelligence, the vast majority of AI initiatives are still struggling to make the critical leap into production. This central tension—the friction between rapid technological ambition and foundational data readiness—defined the core discourse at the recent "Precisely Now: Where Data Meets AI" event.
Bringing together industry analysts, enterprise executives, and software developers, the event painted a vivid picture of the modern corporate landscape. Through keynote discussions, strategic product announcements, and candid panels featuring prominent corporate customers, a clear consensus emerged: the success of artificial intelligence is entirely contingent upon the health, integrity, and trust associated with the underlying enterprise data.
AI Moved Into Production Faster Than Trusted Data Could Follow
When corporate leadership teams met in late November through Customer Advisory Boards, the prevailing mood across major industries—including financial services, insurance, and telecommunications—was one of calculated caution. Representing twenty of the largest enterprise organizations, these leaders shared a unified message: their infrastructure simply was not ready for the demands of generative and autonomous AI.
Yet, within the span of just six months, that cautious posture gave way to aggressive implementation. Enterprises rapidly pushed initiatives into flight, shifting their internal dialogue from the foundational question of whether they should adopt AI to a much more frustrating query: why isn’t this working yet?
According to enterprise leaders and industry research alike, the answer almost always circles back to data quality. Data from the International Data Corporation (IDC) underscores this reality. In a comprehensive survey of more than 1,000 global respondents, 44 percent of AI proofs of concept that failed to reach production cited poor data quality as the primary stumbling block. Another 33 percent noted that data issues played a significant, highly detrimental role. Combined, more than three-quarters of stalled or failed enterprise AI projects can be directly traced back to inadequate data preparation. The models themselves are rarely at fault; rather, they are being built and deployed on shifting, unreliable foundations.
Agents Are the New Data Consumers, and They Work at Machine Speed
Navigating this technological paradigm shift requires a fundamental reassessment of how enterprises view information infrastructure. Walid Abu-Hadba, CEO of Precisely, brings decades of executive experience from industry giants including Microsoft, Oracle, and Sage. Reflecting on his career during the event, Abu-Hadba noted that while previous technological eras struggled primarily with getting the software architecture and computing power right, the AI era introduces a distinct challenge: the technology is readily available, but it requires unprecedented levels of trusted data to operate safely.
The complication is that most enterprise data remains stubbornly fragmented, scattered across legacy mainframes, on-premises servers, complex multi-cloud environments, and diverse SaaS applications. Much of this data is stale, siloed, or inaccurate. Unleashing advanced AI algorithms on fragmented data inevitably produces flawed, untrustworthy outcomes. As Abu-Hadba succinctly summarized during his keynote, achieving trusted AI is entirely dependent on securing trusted data.
To unpack these dynamics further, Abu-Hadba was joined by Stewart Bond, author of the 2026 IDC MarketScape for Worldwide Data Intelligence Platform Software. Bond offered an unvarnished assessment of the shifting enterprise landscape, noting that the fundamental consumption model for data has fundamentally transformed.
Historically, corporate data was collected and curated to feed static dashboards and spreadsheets designed for human consumption. People had the luxury of time to interpret context, question anomalies, and reconcile discrepancies manually. Today, however, enterprise data increasingly feeds autonomous AI agents capable of making multi-step, high-stakes decisions at machine speed. These agents cannot afford to wait for data to be manually cleaned or verified; the information must be reliable the exact moment the machine requests it.
This shift dramatically alters the mandate for modern data intelligence platforms. Historically, data intelligence tools simply mapped where information resided and whether it met basic cleanliness thresholds. Moving forward, these systems must answer a much more demanding question: can an autonomous agent completely trust this data to execute a business transaction without human intervention? For corporate governance leaders, Bond offered a straightforward benchmark for success: the ability to instantly observe what an autonomous agent did, understand why it did it, and possess the capability to effortlessly reverse the action if necessary. When systems operate at machine speed, trust, explainability, and rigorous oversight must be architected into the system before the agent takes action.
Unified Data Management Replaces the Point-Solution Stack
For years, organizations managed their data infrastructure by cobbling together a disjointed array of specialized point solutions. Companies operated separate tools dedicated exclusively to data quality, governance, cataloging, and enrichment, relying on downstream analytics and human intervention to stitch the pieces together. That decentralized approach was sustainable until the introduction of enterprise AI.
Adding autonomous AI capabilities to every single point solution within a fragmented stack creates operational chaos. AI agents pulling from fragmented sources in varying states of quality—without a unified governance framework underneath—are bound to generate systemic errors.
Recognizing this architectural bottleneck, Precisely used the event to showcase the newly announced Precisely Platform, which is currently available in preview. Designed to eliminate the friction of the traditional point-solution stack, the platform converges data integration, data quality, governance, location intelligence, master data management, and customer communications into a single, cohesive user experience.
Addressing the architectural choices behind the platform, Abu-Hadba underscored the long-term dangers of making missteps in infrastructure design, noting that pivoting away from a poorly chosen architecture can be an excruciatingly difficult process for an enterprise. Expanding on this perspective, IDC’s Stewart Bond advised technology leaders to anchor their architecture decisions directly in comprehensive business requirements. When corporate data spans multi-cloud environments, legacy systems, and disparate business applications, flexible hybrid execution often outweighs the native depth of any single closed ecosystem. Notably, IDC recently named Precisely as a Leader among the fifteen vendors evaluated in its global data intelligence platform assessment.
By embracing unified data management, organizations can provide their AI applications, autonomous agents, and analytics engines with a single, synchronized, and trusted foundation.
Precisely AI Studio Gives Teams a Place to Start Building Faster
An enterprise-grade platform is only as effective as the applications organizations can build upon it. To bridge the gap between architectural infrastructure and practical application, Precisely introduced AI Studio during the event. Serving as the primary entry point for the broader Precisely Platform, AI Studio is designed to provide data teams, artificial intelligence engineers, and software developers with immediate access to ready-made applications, intelligent agents, and specialized skills.
Building a functional AI demonstration is relatively straightforward for modern engineering teams. However, building an application that can operate reliably and securely at scale within the complex environment of a large enterprise remains exceptionally difficult. AI Studio is engineered to lower that operational barrier, giving technical teams a collaborative space to experiment, discover new capabilities, and accelerate internal development. Over time, the environment is expected to evolve into a broader community-driven ecosystem where customers and partners can contribute their own tools and share best practices.
Furthermore, AI Studio is built to be Model Context Protocol (MCP) native, ensuring seamless integration with the model providers, development tools, and third-party enterprise applications that engineering teams are already utilizing. It operates in tandem with the existing Data Integrity Suite and the new Precisely Platform, ensuring that any solution engineered within the studio remains grounded in governed, enriched, and verified data. During the event, the company also provided an exclusive preview of a multi-domain master data management workflow built natively on the platform, with a select cohort of design partners slated to test the capabilities in the upcoming fall season.
The Hardest Work Is Shared Definitions, Not Technology
While advanced software architectures and modern platforms are critical, technology alone cannot solve deeply rooted organizational challenges. Stewart Bond emphasized that the underlying root cause of most corporate data failures can be traced directly to people and internal processes rather than software limitations.
This human element was brought into sharp focus during an engaging customer panel moderated by David Woods, Senior Vice President of Global Services at Precisely.
Kevin Guyot of Lake Michigan Credit Union (LMCU) shared that access to sophisticated technology was never the primary hurdle for his organization. Instead, the most significant obstacle arose when LMCU discovered that different internal business units had fundamentally conflicting definitions for basic enterprise terminology, such as "customer," "product," and "member," often operating without realizing the discrepancies. To resolve this, LMCU instituted a collaborative governance model and prioritized comprehensive data literacy training for front-line personnel. For Guyot, the most challenging aspects of data modernization were cultural and organizational rather than technical.
Echoing these sentiments, Jeannine Roberts of Charter Communications emphasized the vital importance of context in corporate data strategy. Analyzing operational reports without adequate context inevitably leads to flawed strategic decisions—a risk that becomes exponentially more dangerous when mistakes are executed at the speed and scale of artificial intelligence. Outlining Charter’s strategy for establishing a reliable data foundation, Roberts advocated for a methodical approach: testing data rigorously, enforcing strict governance, and continuously retesting in small, manageable segments that demonstrate clear, measurable business value.
For data leaders embarking on their own modernization journeys, Guyot offered a pragmatic piece of advice: always anchor initiatives in real business challenges rather than chasing technology trends for their own sake. Successful, sustainable artificial intelligence programs invariably begin with establishing shared organizational definitions, fostering data literacy, and addressing precise, well-defined operational problems.
Ultimately, the overarching narrative of the "Precisely Now" event centers on a permanent shift in corporate operations. For years, organizations could successfully work around fragmented, messy data. Human employees routinely filled in operational gaps, cross-functional teams manually reconciled inconsistencies, and subject matter experts supplied the vital context that legacy systems lacked.
Artificial intelligence systems, however, do not possess the capacity to compensate for missing human context. They operate strictly on the information they are given, processing transactions at a scale and velocity that precludes manual oversight. Whether examining insights from executive keynotes, absorbing the hard-won operational lessons shared by corporate customers, or reviewing the latest platform capabilities, one fundamental truth remains paramount: an enterprise artificial intelligence initiative is only as reliable as the data it consumes.
