As artificial intelligence continues to dominate boardroom strategies, a striking disconnect has emerged between enterprise ambition and operational reality. Recent industry data reveals that while 86% of organizations have aggressively increased their focus on data management in response to the AI boom, the vast majority of AI initiatives are still stalling before they ever reach production.

This growing operational tension took center stage at the recent virtual and hybrid industry event, Precisely Now: Where Data Meets AI. Hosted by industry leaders to address the friction between rapid AI deployment and underlying infrastructure, the event brought together top executives, analysts, and enterprise customers. Through candid executive dialogues, major product unveilings, and practical customer panels, a consensus quickly emerged: the primary bottleneck holding back enterprise AI is not the sophistication of the models, but the state of the data fueling them.

Five core themes defined the discussions at the event, shedding light on the critical steps organizations must take if they hope to scale their AI investments successfully.

AI Moved Into Production Faster Than Trusted Data Could Follow

When enterprise customer advisory boards met just months prior to the event, the prevailing sentiment among top executives—representing twenty of the largest global organizations across financial services, insurance, and telecommunications—was marked by cautious hesitation. Their message at the time was straightforward: enterprises simply were not ready for enterprise-grade AI.

Fast forward a matter of months, and the operational landscape shifted dramatically. Those same organizations found themselves with active AI initiatives already in flight. Yet, the conversation had pivoted away from the initial question of whether companies should adopt AI, morphing instead into a much more urgent frustration: why isn’t this working yet?

The root cause of this widespread friction almost always traces back to underlying data issues. Research from IDC underscores this reality. In a comprehensive survey of more than 1,000 global respondents, 44% of organizations whose AI proofs of concept failed to reach production cited poor data quality as the primary culprit. Another 33% reported that data quality played a significant, contributing role. Combined, these figures indicate that over three-quarters of stalled AI projects ultimately fail because the data supporting them is inadequate, not because the underlying machine learning models are flawed.

Agents Are the New Data Consumers, and They Work at Machine Speed

Navigating technology shifts is nothing new for enterprise leaders who have weathered decades of digital transformation. Walid Abu-Hadba, CEO of Precisely, has guided organizations through multiple major technological waves throughout a career spanning leadership roles at tech giants including Microsoft, Oracle, and Sage. However, Abu-Hadba emphasized that the current artificial intelligence wave differs fundamentally from past paradigm shifts.

In previous technological eras, getting the technology itself right was often the hardest part of the equation. With modern AI, organizations face a dual challenge: they must not only deploy advanced technology, but they also require a foundation of trusted data for that technology to operate upon. The persistent challenge for enterprises is that critical data remains trapped and scattered across legacy mainframes, on-premises data centers, modern cloud environments, and disparate SaaS applications. Much of this data is frequently stale, siloed, or inaccurate. Unleashing automated AI tools on fractured and unverified data inevitably yields flawed outcomes. As Abu-Hadba succinctly summarized during the event, trusted AI requires 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 provided an unvarnished assessment of the changing data landscape, highlighting what corporate buyers are getting right, where they continue to stumble, and where the most viable enterprise opportunities lie.

Bond pointed out a fundamental paradigm shift in how data is consumed within the enterprise. Historically, enterprise data was collected, cleaned, and aggregated to feed static dashboards and spreadsheets that were ultimately reviewed by human eyes. Today, enterprise data increasingly feeds autonomous agents designed to make complex, multi-step decisions at machine speed. These automated agents cannot pause to wait for data teams to manually clean or verify information; the data must be rigorously prepared and ready precisely when the machine demands it.

This operational reality fundamentally transforms the scope and purpose of data intelligence. Previously, data intelligence tools primarily served to locate enterprise data and verify whether it met basic cleanliness standards. In the era of autonomous agents, data intelligence must answer a far more demanding question: can an autonomous machine agent implicitly trust this data enough to execute business processes without requiring a human to review the action first? Bond suggested a straightforward metric for modern governance leaders: the ability to immediately audit what an agent did and possess the capability to effortlessly undo it. Because autonomous agents consume data at unprecedented machine speeds, organizations must establish trust upstream through rigorous explainability and built-in human oversight.

Unified Data Management Replaces the Point-Solution Stack

For years, organizations managed their data infrastructure by cobbling together a patchwork of separate, specialized point solutions. Companies typically ran entirely distinct tools for data quality, data governance, enterprise cataloging, and data enrichment, relying on downstream analytics platforms to stitch the fragmented ecosystem together.

That siloed approach was manageable until the advent of enterprise AI. Introducing AI capabilities into every single point of a fragmented toolchain creates operational chaos. Autonomous agents are left pulling data from disparate sources existing in various states of quality, completely lacking a unified, shared governance framework underneath.

Recognizing this architectural bottleneck, Precisely used the event to showcase the newly announced Precisely Platform, currently available in preview. The platform is designed to consolidate data integration, data quality, data governance, location intelligence, master data management, and customer communications into a single, cohesive user experience.

Abu-Hadba underscored the critical importance of architectural choice, warning attendees of the severe long-term difficulties organizations face if they commit to the wrong foundational architecture early on. Offering complementary guidance, IDC’s Stewart Bond advised technology buyers to anchor their strategies firmly in business requirements. When enterprise data spans hybrid environments encompassing multi-cloud setups, legacy systems, and modern SaaS applications, seamless hybrid execution often outweighs the native depth of any single isolated ecosystem. Notably, IDC recently named Precisely as a Leader among the fifteen enterprise software vendors evaluated in its global data intelligence market assessment.

By moving toward unified data management, enterprises can finally provide their AI applications, autonomous agents, and analytics engines with a single, dependable foundation from which to operate.

Precisely AI Studio Gives Teams a Place to Start Building Faster

An enterprise-grade platform is only as valuable as the applications and solutions developers can successfully build upon it. To bridge this gap, the company announced the launch of Precisely AI Studio, designed to serve as the primary entry point for the broader Precisely Platform.

The studio provides a collaborative environment where data science teams, AI engineers, and software developers can access pre-built applications, autonomous agents, and specialized functional skills. By providing these ready-made components, the studio allows teams to quickly evaluate possibilities and accelerate their internal development cycles.

While building a simple proof-of-concept demo has become relatively straightforward, developing robust AI applications that can operate securely and reliably at scale within a complex enterprise remains a formidable challenge. AI Studio is intended to significantly lower the barrier to entry for development teams. Over time, customers and partners are expected to contribute their own custom tools, transforming the studio into a collaborative community of builders sharing practical innovations.

Furthermore, the studio is Model Context Protocol (MCP) native, ensuring seamless integration with the major model providers, developer tools, and enterprise applications that engineering teams already utilize in their daily workflows. It integrates directly with the existing Precisely Data Integrity Suite and the new Precisely Platform, ensuring that whatever solutions developers build remain grounded in trusted, governed data. During the event, executives also provided an early preview of a multi-domain master data management workflow built natively on the platform, with a select cohort of design partners scheduled to test the capabilities in the upcoming season.

Ultimately, AI Studio functions as an innovation hub where enterprise teams can explore advanced use cases and accelerate AI development, backed by the assurance that their projects are anchored in data that has been thoroughly governed, enriched, and validated by the underlying platform.

The Hardest Work Is Shared Definitions, Not Technology

While technology platforms and AI tooling receive significant attention, industry analysts and enterprise leaders emphasize that the root causes of persistent data challenges are fundamentally human and procedural rather than purely technical.

This reality was underscored during a customer panel moderated by David Woods, Senior Vice President of Global Services at Precisely. Panelist Kevin Guyot of Lake Michigan Credit Union (LMCU) noted that access to advanced technology was never the primary obstacle for his organization. Instead, the most significant hurdle emerged during internal discovery, when LMCU realized that different business units across the company had subtly defined fundamental terms like “customer,” “product,” and “member” in entirely different ways without realizing it.

To overcome this fragmentation, LMCU established a collaborative cross-functional governance model and invested in comprehensive data literacy training for front-line personnel. Guyot highlighted that navigating these human-centric organizational hurdles required far more effort than implementing the underlying technical infrastructure.

Jeannine Roberts of Charter Communications emphasized the vital importance of contextual understanding in enterprise data. Analyzing reports without proper context routinely leads to poor decision-making—a risk that is severely magnified when decisions are executed at AI speed and scale. Roberts outlined her organization’s strategy for building a dependable data foundation for AI initiatives through an iterative process of testing, governing, and retesting within small, manageable segments that demonstrate clear, measurable business value.

For data leaders embarking on similar transformation journeys, Guyot advised starting with tangible business challenges rather than beginning with the technology stack itself. Truly successful artificial intelligence programs invariably begin with establishing shared linguistic definitions, investing in governance education, and focusing intensely on solving a specific business problem.

Ultimately, the overarching lesson resonating across all the discussions at Precisely Now is that organizations can no longer rely on human workarounds to compensate for fragmented and poor-quality data. In the past, human workers could informally fill in information gaps, reconcile inconsistencies between systems, and supply the missing context that static software lacked. Autonomous AI systems, however, cannot perform these manual reconciliations; they act directly on the data they are given, operating at scale and at speeds far too fast for human intervention to catch errors mid-stream.

As enterprise leaders map out their technological roadmaps moving forward, the message remains clear: an organization’s artificial intelligence capabilities can only ever be as reliable as the underlying data foundation that supports them.