The race to operationalize artificial intelligence has taken a sharp and humbling turn in enterprise boardrooms worldwide. While recent industry data indicates that approximately 86 percent of organizations have intensified their focus on data management in response to the generative AI boom, a stark paradox continues to define the market: the vast majority of AI initiatives are still stalling before they ever reach full production.

This tension between ambition and execution served as the central theme at the recent virtual event "Precisely Now: Where Data Meets AI." Hosted by industry leaders to address the growing friction in enterprise data pipelines, the event brought together executive insights, analyst perspectives from the International Data Corporation (IDC), and real-world case studies from major financial and telecommunications institutions. Through candid discussions about what corporate buyers are getting right—and where they are going fundamentally wrong—a clear picture emerged regarding the rigorous demands of AI-ready data infrastructure.

1. AI Moved Into Production Faster Than Trusted Data Could Follow

When corporate customer advisory boards convened late last year, the prevailing sentiment across major industries—including financial services, insurance, and telecommunications—was one of profound caution. Comprising executives from roughly twenty of the largest enterprise organizations, these advisory groups shared a singular, unambiguous message: they were simply not ready for the operational demands of enterprise-grade artificial intelligence.

Yet, within a span of just six months, the operational landscape shifted dramatically. Those same organizations quickly transitioned from exploratory evaluations to active AI initiatives in flight. Consequently, the primary question being asked in executive suites shifted from "Should we use AI?" to a far more frustrated inquiry: "Why isn’t this working yet?"

According to industry analysts, the answer almost invariably points back to the state of enterprise data. Research from IDC underscores this reality. In a comprehensive survey of more than 1,000 global respondents, 44 percent of organizations whose AI proofs of concept failed to reach production cited poor data quality as the primary root cause. Another 33 percent noted that data quality issues played a significant contributing role. Combined, more than three-quarters of stalled or failed AI implementations can be traced directly back to inadequate underlying data.

In essence, enterprise AI projects are rarely failing because the underlying machine learning models are flawed. Instead, they stall because the data feeding those models lacks the consistency, accuracy, and hygiene required for enterprise-scale deployment.

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

Navigating technological shifts is hardly new for seasoned industry executives. Walid Abu-Hadba, CEO of data integrity leader Precisely, has guided organizations through multiple major technology transformations throughout his career, holding leadership roles at global powerhouses including Microsoft, Oracle, and Sage. However, Abu-Hadba explains that the current artificial intelligence wave presents a fundamentally unique challenge compared to previous technological epochs.

Historically, the primary hurdle for enterprise technology teams was getting the software architecture and computing power right. With AI, however, getting the technology functioning is only half the battle; organizations must also ensure they have thoroughly trusted data for those sophisticated systems to operate upon. The persistent challenge for large enterprises is that their data remains heavily fragmented, scattered across legacy mainframes, on-premises data centers, modern cloud environments, and diverse Software-as-a-Service (SaaS) applications. Much of this enterprise data remains stale, siloed, or inaccurate. Unleashing advanced AI models on unvetted, fragmented data inevitably yields flawed outcomes, reinforcing Abu-Hadba’s core maxim: if an organization wants trusted AI, it must first establish trusted data.

To better understand this dynamic, 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 enterprise requirements, highlighting a profound evolution in how data is utilized across modern organizations.

Historically, enterprise data was primarily compiled to feed static dashboards, operational spreadsheets, and human analysts who possessed the contextual awareness to spot anomalies or data discrepancies. Today, however, enterprise data increasingly feeds autonomous AI agents designed to execute multi-step business decisions at machine speed. These autonomous agents cannot afford to wait for data to be manually cleaned, reconciled, or vetted; the data must be fully prepared and reliable the precise moment the machine requires it.

This fundamental shift radically alters the traditional role of data intelligence. Previously, data intelligence solutions simply helped organizations discover where their data resided and whether it met basic cleanliness standards. Now, modern data intelligence must definitively answer a much higher-stakes question: can an autonomous agent safely trust this data enough to execute a business transaction without requiring a human to manually review it first?

For corporate governance leaders, Bond proposes a straightforward litmus test for operational trust: can the organization clearly audit what the autonomous agent did, and can it easily reverse the action if necessary? Because modern AI agents consume and process data at machine speed, trust must be established proactively before the agent acts, embedding explainability and structured human oversight directly into the architecture.

3. Unified Data Management Replaces the Fragmented Point-Solution Stack

For years, enterprise IT departments relied on a patchwork of disconnected point solutions to manage their information assets. Organizations typically ran entirely separate tools for data quality management, data governance, cataloging, and data enrichment, relying on downstream business analytics applications to stitch the fragmented ecosystem together.

While this decentralized approach functioned adequately for traditional business reporting, it has proven entirely unsustainable in the age of artificial intelligence. Introducing AI capabilities across each of these isolated operational points transforms an already complex IT setup into outright organizational chaos. Autonomous agents suddenly find themselves pulling information from fragmented data stores existing in various states of quality, completely devoid of a cohesive, shared governance framework underneath.

To address this structural vulnerability, industry providers are racing to deliver integrated architectures. The newly announced Precisely Platform, unveiled in preview, represents a concerted effort to eliminate the traditional point-solution stack by bringing together data integration, data quality, governance, location intelligence, master data management, and customer communications into a single, cohesive user experience.

Industry analysts emphasize that architectural choices made during this foundational phase carry long-term consequences. Abu-Hadba underscored this reality during discussions at the event, noting how exceptionally difficult it is for an enterprise to reverse course after selecting the wrong architectural foundation. Bond similarly advised data leaders to anchor their decisions strictly in operational requirements, noting that for organizations whose data spans complex multi-cloud environments, legacy mainframes, and modern applications, flexible hybrid execution often outweighs the native depth of any single closed ecosystem. Notably, IDC recently recognized Precisely as a Leader among the 15 vendors evaluated in its global market assessment.

By consolidating disparate tools into a unified framework, modern data management platforms provide AI applications, autonomous agents, and analytics engines with a single, reliable foundation from which to operate.

4. Precisely AI Studio Gives Teams a Place to Start Building Faster

An enterprise-grade platform is ultimately only as valuable as the applications and solutions developers can successfully build upon it. To bridge the gap between architectural infrastructure and practical application, Precisely introduced AI Studio as the primary entry point for the broader Precisely Platform.

Designed specifically for data teams, AI engineers, and software developers, AI Studio provides immediate access to pre-built applications, intelligent agents, and modular skills. This environment is intended to help technical teams quickly evaluate potential use cases and accelerate their internal development cycles. While building a proof-of-concept demonstration is relatively straightforward for modern technical teams, building a secure, scalable solution that functions reliably within a complex enterprise environment remains a formidable challenge. AI Studio is designed to lower that operational barrier to entry, enabling customers and partners to contribute their own tools over time and fostering a collaborative community of enterprise builders.

Furthermore, the environment is Model Context Protocol (MCP)-native, ensuring seamless integration with the third-party model providers, developer tools, and enterprise applications that engineering teams are already utilizing. It operates directly alongside the established Data Integrity Suite and the newly launched Precisely Platform, anchoring every generated output in verified, high-integrity data.

During the event, technical teams also provided an early preview of an advanced multi-domain master data management workflow built natively on the platform, with plans to collaborate alongside a select group of design partners to refine the capability. Ultimately, AI Studio provides a sandbox where teams can explore new technological possibilities and accelerate development, backed entirely by data that the broader platform has systematically governed, enriched, and validated.

5. The Hardest Work Is Shared Definitions, Not Technology

Despite the rapid evolution of software tools and machine learning models, industry experts and enterprise practitioners repeatedly emphasize that the most stubborn obstacles to successful AI implementation are rooted in people and organizational processes rather than technology itself.

During a customer panel moderated by David Woods, Senior Vice President of Global Services at Precisely, enterprise leaders shared candid insights regarding their internal transformation journeys. Kevin Guyot of Lake Michigan Credit Union (LMCU) noted that access to advanced technology was never the primary obstacle for his institution. Instead, the most difficult hurdle was discovering that different internal business units defined foundational terms—such as "customer," "product," and "member"—in entirely different ways without realizing it.

To overcome this fragmentation, LMCU implemented a shared governance model and invested heavily in training front-line operational teams in data literacy. Guyot’s experience highlights a broader truth for corporate leadership: the most challenging hurdles in an AI transformation are rarely technical.

Jeannine Roberts of Charter Communications echoed these sentiments, emphasizing the critical importance of operational context. Business reports reviewed without proper context frequently lead to flawed decision-making—a risk that is magnified exponentially when decisions are executed at AI speed and scale. Outlining her approach to establishing a dependable data foundation for AI, Roberts advocated for a disciplined methodology: testing data rigorously, enforcing governance, and continuously retesting in small, manageable segments that demonstrate clear, measurable business value.

For data leaders embarking on similar transformation journeys, Guyot’s primary recommendation is to anchor initiatives in real business challenges rather than technology trends for their own sake. Truly successful artificial intelligence programs invariably begin with establishing shared organizational definitions, fostering data literacy through targeted governance education, and addressing specific, well-defined business problems.

Navigating the Road Ahead for Data and AI Leaders

For years, organizations could successfully compensate for fragmented or imperfect data through human intervention. Employees routinely filled in operational gaps, departmental teams reconciled data inconsistencies manually, and subject matter experts supplied the vital context that legacy information systems lacked.

Modern artificial intelligence systems do not possess that capacity for informal remediation. Autonomous AI systems act directly upon the data they are provided, operating at massive scale and executing decisions far faster than any human operator can step in to correct them.

This core reality forms the unifying thread connecting every insight shared during the event—from executive conversations and analyst evaluations to the hard-won lessons of enterprise customers. Ultimately, an organization’s artificial intelligence capabilities are only ever as reliable as the data foundation supporting them. Establishing a robust, unified data infrastructure and creating a rapid, frictionless path for application development must be treated as two halves of the exact same strategic imperative.