Eighty-six percent of organizations have actively increased their focus on data management as a direct consequence of the artificial intelligence boom. Yet, despite mounting investments and a pervasive sense of urgency, the vast majority of enterprise AI initiatives continue to struggle to reach production.
This core tension—the friction between rapid technological deployment and foundational data readiness—permeated every conversation at the recent "Precisely Now: Where Data Meets AI" event. Hosted to explore the evolving landscape of enterprise technology, the event brought together industry analysts, enterprise executives, and software leaders to dissect what is working, what is failing, and what it truly takes to make enterprise data AI-ready.
From candid executive conversations regarding what buyers are getting right and wrong, to major product announcements and engaging customer panels, a clear picture emerged of the hurdles facing modern enterprises. Five primary takeaways encapsulated the state of the industry at this critical inflection point.
AI Moved into Production Faster Than Trusted Data Could Follow
When the Customer Advisory Board met previously, the prevailing mood across industries—ranging from financial services and insurance to telecommunications—was one of caution. Comprising leaders from twenty of the largest organizations, their message was straightforward: enterprises simply were not ready for the operational demands of generative and analytical artificial intelligence.
Just six months later, however, the landscape shifted dramatically. Those same organizations found themselves with live AI initiatives already in flight, and the fundamental question had evolved from "Should we use AI?" to "Why isn’t this working yet?"
The answer, almost uniformly, traces back to the underlying data. Research from IDC reinforces this reality. In a comprehensive survey of more than 1,000 respondents, 44 percent of AI proofs of concept that failed to reach production cited data quality as the primary reason. Another 33 percent noted that poor data quality played a significant role in their project’s failure. Combined, over three-quarters of stalled or failed AI projects can be directly attributed to shortcomings in data infrastructure and hygiene.
Ultimately, AI projects are stalling in production not because the underlying machine learning models are fundamentally flawed, but because the data feeding those models is fragmented, inaccurate, or unprepared for prime time.
Agents Are the New Data Consumers, and They Work at Machine Speed
Navigating technological shifts is hardly new for industry veterans. Walid Abu-Hadba, CEO of Precisely, has led organizations through multiple generational technology waves over his career at companies spanning Microsoft, Oracle, Sage, and now Precisely. However, he explains that the current artificial intelligence shift fundamentally differs from anything that came before it.
In previous technological eras, getting the software infrastructure right was universally considered the hardest part of the equation. With the advent of advanced AI, however, organizations face a dual challenge: they need the technology, but they also require impeccably trusted data for that technology to operate upon safely and accurately.
The primary complication is that most enterprise data remains scattered across a sprawling mosaic of legacy mainframes, on-premises data centers, modern cloud environments, and disparate SaaS applications. Much of this data is inevitably stale, siloed, or inaccurate. Unleashing automated artificial intelligence models onto unvetted, messy data inevitably leads to flawed outcomes. As Abu-Hadba succinctly summarized during the event, "If you want trusted AI, you need trusted data."
Stewart Bond, author of the IDC MarketScape for Worldwide Data Intelligence Platform Software, offered an equally candid assessment of the changing market dynamics, buyer missteps, and emerging opportunities. Bond highlighted a profound architectural shift in how data is utilized. Historically, enterprise data was gathered to feed static dashboards and spreadsheets designed for human consumption, allowing people time to interpret, contextualize, and spot errors.
Today, data increasingly feeds autonomous agents capable of making multi-step, complex decisions at machine speed. These digital agents cannot wait for data to be manually cleansed or verified; the information must be inherently ready the exact moment the machine demands it.
This operational reality fundamentally transforms the role of data intelligence within the enterprise. Traditionally, data intelligence tools were tasked with locating data repositories and verifying whether files were clean. Now, enterprise systems must be able to answer whether an autonomous agent can trust that data enough to execute business actions without requiring a human to check the work first. For corporate governance leaders, Bond offered a simple litmus test: can you see what the AI agent did, and can you easily undo it if necessary?
Because modern agents consume data at unprecedented speeds, trust must be established systematically before the agent acts, embedding robust explainability and continuous human oversight directly into the architecture.
Unified Data Management Replaces the Point-Solution Stack
For years, organizations managed their data ecosystems by stitching together separate, specialized point solutions for data quality, governance, cataloging, and enrichment, relying on downstream analytics to hold the patchwork together. That decentralized approach worked adequately well for traditional business intelligence, but it has proven entirely inadequate in the age of AI.
Introducing artificial intelligence capabilities into every individual point of a fragmented stack creates systemic chaos. Autonomous agents end up pulling from disparate data sources residing in varying states of quality, completely lacking a unified, shared governance layer underneath.
Recognizing this architectural bottleneck, Precisely used the event to showcase the newly announced Precisely Platform, designed to bring data integration, data quality, governance, location intelligence, master data management, and customer communications into a single, cohesive user experience.
Industry leaders emphasize that architectural choices made today will have long-term consequences. Abu-Hadba underscored the permanence of these decisions, noting how difficult it is for an enterprise to reverse course after selecting the wrong underlying architecture.
In alignment with these insights, IDC named Precisely a Leader among the fifteen vendors evaluated in its worldwide data intelligence platform research. Analysts advise that organizations must anchor their data strategies directly in operational requirements, emphasizing that hybrid execution across clouds, legacy systems, and modern applications is often more critical than relying on the native depth of any single software ecosystem.
Ultimately, unified data management provides AI applications, autonomous agents, and enterprise analytics with a single, reliable foundation from which to operate securely.
Precisely AI Studio Gives Teams a Place to Start Building Faster
An enterprise software 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.
AI Studio provides data teams, artificial intelligence engineers, and application developers with access to ready-made applications, intelligent agents, and specialized skills. By offering these pre-built components, the studio allows technical teams to immediately explore organizational possibilities and dramatically accelerate their own internal development cycles.
While building a simple proof-of-concept demonstration is relatively straightforward, engineering a solution that can be trusted at scale within a complex enterprise environment remains notoriously difficult. AI Studio aims to lower the barrier to entry for production-grade development. Over time, customers and partners are expected to contribute their own tools and workflows, transforming AI Studio into a collaborative community where builders share practical utilities with one another.
Furthermore, the studio is model control protocol (MCP) native, ensuring seamless integration with the model providers, development tools, and enterprise applications that engineering teams are already utilizing. Operating in tandem with the existing Data Integrity Suite and the new Precisely Platform, AI Studio ensures that whatever applications teams construct remain firmly grounded in trustworthy data.
During the event, organizers also provided an early preview of an advanced multi-domain master data management workflow built natively on the platform, with plans to collaborate with a select cohort of design partners to refine the capability.
The Hardest Work Is Shared Definitions, Not Technology
While technology stacks and platform integrations dominate headlines, industry veterans emphasize that the deepest obstacles to successful enterprise AI deployment are fundamentally human and procedural rather than purely technical.
During a customer panel moderated by David Woods, Senior Vice President of Global Services at Precisely, corporate leaders shared the operational roadblocks they encountered while preparing their data for advanced analytics.
Kevin Guyot of Lake Michigan Credit Union (LMCU) noted that access to advanced technology was never the primary obstacle for his organization. Instead, the greatest hurdle was discovering that various internal departments had unconsciously adopted conflicting definitions for foundational business terms such as "customer," "product," and "member."
To resolve these discrepancies, LMCU constructed a shared governance framework and invested heavily in training front-line teams in data literacy. Guyot’s experience highlights a recurring theme across the industry: the most challenging hurdles in an enterprise AI transformation are rarely solved by code alone.
Similarly, Jeannine Roberts of Charter Communications emphasized the critical importance of operational context, warning that business reports reviewed without proper context inevitably lead to flawed decisions—a risk compounded exponentially when operating at AI speed and scale. Outlining her strategy for building a robust data foundation, Roberts advocated for a disciplined approach: "Test it, govern it, retest it" across manageable business segments that clearly demonstrate tangible value.
Guyot’s primary advice for corporate data leaders reflects these lessons: start by addressing real, pressing business challenges rather than blindly chasing artificial intelligence trends for their own sake. Truly successful AI programs invariably begin with establishing shared linguistic definitions, prioritizing governance education, and focusing intensely on a specific business problem.
What Data and AI Leaders Must Take Away
For years, organizations could successfully compensate for fragmented, messy data through manual intervention. Human workers routinely filled in data gaps, cross-functional teams reconciled analytical inconsistencies, and subject matter experts supplied the vital context that legacy systems lacked.
Artificial intelligence systems do not possess that institutional intuition. They act directly upon the data they are fed, operating at scale and at speeds that outpace human ability to step in and correct errors on the fly.
That fundamental reality runs as a consistent thread through every discussion, from executive keynotes to hard-won customer lessons: an enterprise artificial intelligence deployment is only ever as reliable as the underlying data that feeds it.
Establishing a solid, unified data foundation and maintaining a rapid, scalable path for building new solutions must be treated as two halves of the exact same strategic plan. Enterprise leaders looking to revisit these insights can access on-demand sessions from the event to examine the new platform capabilities, developer studios, and industry discussions firsthand.
