The race to integrate artificial intelligence into the enterprise has crossed a critical threshold, but a formidable barrier threatens to halt progress in its tracks. While roughly 86 percent of organizations have intensified their focus on data management specifically because of AI, the vast majority of artificial intelligence initiatives continue to struggle to reach full production status. This underlying tension set the tone for industry leaders, data executives, and enterprise decision-makers gathering recently at the virtual event Precisely Now: Where Data Meets AI.
Hosted by data integrity leader Precisely, the event brought together candid conversations with top industry analysts, executive leadership, and major enterprise customers. Discussions ranged from the missteps buyers frequently make when approaching data architectures to major product announcements and real-world implementation stories. Out of these multifaceted discussions, a clear consensus emerged regarding what is truly required to prepare enterprise data for the demands of modern artificial intelligence. Five key takeaways highlighted the reality of bridging the gap between AI ambition and operational execution.
AI Moved Into Production Faster Than Trusted Data Could Follow
When the Customer Advisory Board of Precisely met just months prior, the prevailing sentiment among the group of twenty enterprise leaders from sectors such as financial services, insurance, and telecommunications was cautious. Their collective message was straightforward: organizations simply were not ready. Yet, within a remarkably short window, those same customers found themselves with active artificial intelligence initiatives in flight. The core question shifted dramatically from whether companies should adopt AI to why these tools were not delivering expected results in production environments.
In almost every case, the root cause traced back to the underlying data. Supporting this observation, research from the International Data Corporation (IDC) paints a stark picture. In a survey spanning more than 1,000 respondents, 44 percent of AI proofs of concept that failed to reach production cited data quality as the primary culprit. Another 33 percent indicated that data quality played a significant, contributing role. Combined, more than three-quarters of stalled AI initiatives can be attributed directly to shortcomings in data integrity. The bottleneck keeping enterprise AI from scaling is rarely a flaw in the underlying models; rather, it is the poor state of the data feeding them.
Agents Are the New Data Consumers, and They Work at Machine Speed
Reflecting on decades of technology shifts experienced throughout his career at major organizations including Microsoft, Oracle, Sage, and now Precisely, CEO Walid Abu-Hadba emphasized that the current wave of artificial intelligence is fundamentally different from previous technological evolutions. In earlier eras, getting the technology itself to function properly was typically the hardest challenge. Today, however, deploying advanced technology is only half the battle; organizations must also ensure that the data fueling those systems is entirely trustworthy.
Enterprise data is notoriously fragmented, scattered across legacy mainframes, on-premises systems, modern cloud environments, and diverse SaaS applications, with substantial portions remaining stale or inaccurate. Abu-Hadba noted that unleashing AI onto such a fractured data landscape inevitably yields flawed, untrustworthy outcomes. As he succinctly put it during the event, achieving trusted AI is impossible without first securing trusted data.
Joining Abu-Hadba for a candid discussion was Stewart Bond, author of the 2026 IDC MarketScape for Worldwide Data Intelligence Platform Software. Bond offered an unvarnished assessment of how enterprise data consumption is changing. Historically, data was compiled to feed human-centric dashboards and static spreadsheets that people could review at their leisure. Today, data increasingly feeds autonomous agents capable of making complex, multi-step decisions at machine speed. These agents cannot afford to wait for data to be manually cleaned or validated; it must be ready precisely when the machine demands it.
This fundamental shift redefines the mandate of data intelligence platforms. In the past, data intelligence focused primarily on locating data and verifying whether it was clean. Now, systems must answer a much more rigorous question: can an autonomous agent safely trust this data enough to execute actions without human intervention? For governance leaders, Bond offered a straightforward evaluation test: can administrators see exactly what the agent did, and is there a mechanism to undo the action if necessary? Because agents consume information at machine speed, establishing trust, explainability, and rigorous oversight before an agent acts has become an absolute operational necessity.
Unified Data Management Replaces the Point-Solution Stack
For years, organizations managed their data landscapes by cobbling together disparate point solutions for data quality, governance, cataloging, and enrichment, relying on separate analytics layers to stitch everything together. That piecemeal approach sufficed until the advent of enterprise AI. Introducing artificial intelligence into every corner of a fragmented software stack quickly transforms a complicated setup into complete operational chaos, with automated agents pulling from siloed repositories of varying quality devoid of shared governance.
To address this structural vulnerability, Precisely showcased the newly announced Precisely Platform, currently available in preview. The platform is designed to unify data integration, data quality, governance, location intelligence, master data management, and customer communications into a single, cohesive user experience. Abu-Hadba underscored the strategic importance of architectural choices during the event, warning that reversing a poor architectural decision once implemented is exceptionally difficult.
Echoing this sentiment, IDC’s Stewart Bond advised organizations to anchor their technology strategies firmly in business requirements. When enterprise data spans hybrid environments encompassing multi-cloud configurations, legacy mainframes, and modern SaaS applications, a unified execution model often outweighs the native depth of any single ecosystem vendor. Notably, IDC recently recognized Precisely as a Leader among the fifteen vendors evaluated in its global data intelligence platform assessment. By consolidating fragmented tools, unified data management provides AI applications, autonomous agents, and traditional analytics with a single, reliable foundation.
Precisely AI Studio Gives Teams a Place to Start Building Faster
An enterprise platform is only as powerful as the practical applications built upon it. To bridge this gap, Precisely introduced AI Studio alongside its new platform architecture, providing an accessible entry point for data teams, AI engineers, and developers. The studio offers a centralized environment where technical staff can access pre-built applications, intelligent agents, and specialized skills to explore new capabilities and accelerate their internal development cycles.
While building a simple proof-of-concept demonstration is relatively straightforward, engineering a reliable application that operates securely at scale within a complex enterprise remains a formidable challenge. AI Studio is designed to lower the barrier to entry for development teams. Over time, contributions from customers and partners are expected to expand the environment into a collaborative community where builders can share functional tools. Furthermore, AI Studio is Model Context Protocol (MCP) native, ensuring seamless integration with the model providers, tools, and developer applications already favored by enterprise teams. Operating in tandem with the existing Data Integrity Suite and the new Precisely Platform, AI Studio ensures that any application deployed by development teams remains grounded in verified, trustworthy data.
The Hardest Work Is Shared Definitions, Not Technology
During a customer panel moderated by David Woods, Senior Vice President of Global Services at Precisely, industry practitioners emphasized that the most stubborn obstacles in data transformation are rooted in people and processes rather than raw technology. Kevin Guyot of Lake Michigan Credit Union (LMCU) noted that access to advanced technology was never the primary hurdle for his organization. Instead, the real challenge lay in the discovery that different internal departments defined fundamental business terms like "customer," "product," and "member" in conflicting ways without realizing it.
To overcome this, LMCU established a shared governance framework and invested in training front-line personnel in foundational data literacy. For Guyot, navigating human alignment proved far more challenging than managing the technical infrastructure. Similarly, Jeannine Roberts of Charter Communications highlighted the critical importance of contextual awareness in data management. Examining business reports devoid of proper context inevitably leads to flawed decision-making—a risk magnified exponentially when operating at the speed and scale of artificial intelligence. Roberts advocated for a disciplined approach to building an AI-ready data foundation, urging organizations to test, govern, and continuously retest data streams in manageable segments that demonstrate clear business value.
For data leaders embarking on similar transformation journeys, Guyot advised starting with tangible business challenges rather than letting technology dictate the strategy. Successful AI implementations invariably begin with establishing shared enterprise definitions, educating teams on data governance, and addressing specific operational problems.
For organizations navigating the intersection of analytics, data management, and artificial intelligence, the overarching message from the event remains unambiguous. Historically, businesses could tolerate fragmented data because human workers routinely stepped in to bridge gaps, reconcile inconsistencies, and supply missing context. Modern AI systems lack that intuitive adaptability; they execute actions based strictly on the data provided, operating at a scale and velocity that precludes manual intervention. As enterprise leaders continue to refine their strategies, the fundamental truth underscored throughout the event stands: an organization’s artificial intelligence capabilities will only ever be as reliable as the data foundation supporting them.
