The biggest risk to your AI program isn't the model you chose. It's the duplicate records, stale data and siloed storage quietly poisoning the pipeline.

The Short Version

• Gartner predicts that through 2026, organizations will abandon 60% of AI projects that are not supported by AI-ready data.
• Duplicate records, stale data and siloed storage each create distinct, predictable failure modes in an AI pipeline.
• AI systems do not automatically know when enterprise source data is wrong, outdated or duplicated. They can produce confident outputs from flawed inputs.
• Data engineering is an ongoing operational discipline, not a one-time pre-project setup task.
• In a 2023 evaluation commissioned by 1touch.io, third-party test lab Tolly reported 98.6% accuracy in its structured-data test and 100% in its unstructured flat-file test of the Inventa platform, technology Everpure acquired in 2026.
• Data readiness belongs at the start of any AI initiative, assessed alongside use case definition, architecture and model selection.

Gartner predicts that through 2026, organizations will abandon 60% of AI projects that are not supported by AI-ready data. The warning is straightforward: data is often the first and most underestimated point of failure.

Poor data quality, stale records, duplicated entities and fragmented storage all create predictable failure modes in AI systems. An AI system's answers are constrained by its training, retrieved context and source data. If that data is outdated, inconsistent or poorly governed, the output reflects those flaws at scale.

The architecture, the model selection and the deployment strategy all matter. Each can be undermined when the data entering the system is duplicative, stale or poorly governed.

The Model May Not Be the First Problem

When an AI project underperforms, the reflex is often to blame the model, the prompt design or the infrastructure. Those are fixable. What is harder to see and harder to correct is the condition of the data feeding the system.

AI models amplify whatever patterns exist in the data they draw from. If that data contains duplicate records, the model can treat them as distinct entities and produce contradictory outputs. If it contains stale information, an org chart from 18 months ago, pricing updated quarterly, customer records that have not been validated in years, the model may present all of it as current. Unless freshness and validation controls are built into the system, it has no reliable way to tell current information from superseded information.

Traditional analytics often included more visible human review points, giving analysts a chance to question anomalous results before they moved downstream. Automated AI workflows can propagate errors faster and across more decisions. Fluent, confident-sounding output is not evidence that an answer is accurate.

Before any AI project launches, IT directors need to answer one question: How can I trust the data we are feeding the model?

What Bad Data Actually Looks Like in an Enterprise Environment

Bad data takes several forms, and each creates distinct failure modes in an AI pipeline.

Duplicate records are among the most common. When the same customer, asset or event appears multiple times with slightly different attributes, a retrieval-augmented generation (RAG) system may retrieve several competing versions of the same answer. Duplicates can crowd retrieval results, overweight repeated claims and expose inconsistent facts. Without entity resolution, timestamps and conflict-handling logic, the model may not reconcile those versions reliably.

Stale data may be the more dangerous problem because it is invisible. A Landbase review of industry estimates suggests B2B contact data can decay by more than 20% annually, with substantially higher rates reported for some data types and populations. Product specifications, compliance policies and internal procedures go stale too, at rates that vary by organization. An AI agent working from a knowledge base that has not been curated in a year or more may confidently cite superseded procedures and discontinued products. In that case the primary failure is stale grounding data rather than model hallucination: the system is answering from a source that was never updated.

Siloed data compounds both problems. When finance, operations, HR and sales maintain separate data stores with different schemas and governance rules, AI systems draw from fragmented contexts. The same question can get a different answer depending on which data source the system happened to query.

An IDC white paper sponsored by Everpure ranked redundant data, siloed storage architectures and obsolete data among the leading contributors to poor data quality in enterprise AI environments. Those are not edge cases. They are common characteristics of enterprise data environments built before AI consumption became a design requirement.

Data Engineering Is a Foundation, Not an Afterthought

The phrase "garbage in, garbage out" predates AI by decades. What has changed is the scale and speed at which that garbage now circulates. AI systems can present flawed source data in fluent, authoritative language and propagate it across automated workflows.

Data engineering, combined with data governance, covers the full lifecycle: discovering what data exists, preparing and structuring it for downstream use, building pipelines that ingest and transform it reliably and maintaining quality as volumes grow. Most organizations scope this narrower than it is. Discovery and governance are one layer. Pipeline design, schema management, deduplication, orchestration and performance tuning for AI inference are a separate layer that is just as demanding.

Precisely and Drexel University's 2026 research exposed a significant perception gap. Nearly nine in ten data and analytics leaders said their data was AI-ready, yet 43% identified data readiness as the most significant barrier to aligning AI with business objectives. Gartner estimates, based on its 2020 research, that poor data quality costs organizations at least $12.9 million a year on average. Leaders believe they are ready while continuing to report serious readiness problems. That gap is expensive.

Data intelligence tools accelerate this work. Rather than requiring data engineering teams to manually inventory what exists across fragmented environments, data intelligence tools can automate discovery, classify assets and surface issues such as stale, copied or low-trust data before those assets enter AI pipelines. Engineers gain a governed, better-understood map of the data estate earlier in the project lifecycle. That means pipelines are built on a reliable foundation rather than corrected after the fact when AI outputs reveal the gaps.

Announced at Pure Accelerate 2026 in Las Vegas, Everpure Data Intelligence targets the discovery and governance layer specifically. Built on Everpure's acquisition of data management firm 1touch.io, it scans across SaaS applications, cloud environments, on-premises storage and mainframe systems. Unlike discovery implementations limited primarily to pattern matching, Everpure adds semantic context: it works to understand data meaning, lineage, sensitivity and business and regulatory context. That semantic and governance context is an important part of what AI agents need to locate and use enterprise data reliably. In a 2023 evaluation commissioned by 1touch.io, third-party test lab Tolly reported 98.6% accuracy in its structured-data test of more than 60 million rows and 100% accuracy in its unstructured flat-file test of the 1touch.io Inventa platform.

That visibility matters because you cannot govern what you cannot find. Many enterprise data landscapes have accumulated years of ungoverned storage: application exports that were never cleaned up, shadow IT repositories and duplicated datasets spread across multiple systems with none designated as authoritative. Data Intelligence surfaces that picture without requiring data to be physically consolidated first.

For data engineering teams, that inventory is a force multiplier. Schema alignment, deduplication and pipeline design all depend on knowing what data exists and what condition it is in. Everpure Data Intelligence delivers that context automatically across environments that would otherwise require extensive manual cataloging. Everpure claims the platform can cut scanning costs by as much as 99%, though its public solution brief does not disclose the benchmark methodology or comparison baseline. Engineers can prioritize remediation on the assets that matter most to AI workloads and build pipelines with confidence rather than discovering data quality problems at inference time.

An IDC white paper sponsored by Everpure found that 94% of respondents rated data quality as important or very important to AI project success. Data intelligence addresses it by giving engineering teams the visibility to act and the governance framework to sustain it. For organizations deploying RAG systems or agentic AI workloads, Everpure exposes governed, semantic context through APIs and Model Context Protocol integrations. Agents get the context they need to locate trusted data. The retrieval, indexing and access architecture around them still has to be engineered.

Daymark's Take

Data engineering is one of the primary determinants of whether an enterprise AI initiative performs reliably or fails in production. That means designing pipelines, transforming data into usable formats, maintaining quality at scale and building the orchestration that keeps AI workloads current. It requires dedicated expertise and architectural commitment. No platform eliminates that work.

Everpure Data Intelligence accelerates this process. Before a single pipeline is designed, the platform gives data teams a cross-environment, governed view of what exists across SaaS, cloud, on-premises and mainframe systems. It classifies assets, maps data relationships and surfaces contextual metadata that agentic AI systems can use to improve retrieval, grounding and policy-aware actions. Data engineers use that view to prioritize remediation and target their effort on the assets that matter most to AI workloads. That clarity can support faster adoption, lower engineering cost and higher quality and relevance in the data that reaches AI systems.

What Everpure does not do is replace the engineering. Data still needs to be transformed, deduplicated, governed and, where the architecture requires it, moved, indexed or exposed through controlled pipelines. Discovery and classification tell you what you are working with. The engineering work that follows is what makes that data usable.

For most organizations, choosing the model is the easy part. Knowing what data you have, how it is governed and what transformation it needs before it can reach AI systems is where initiatives actually succeed or fail. Daymark has the expertise across the intelligence and engineering layers to help answer those questions and act on them.

About Daymark

Daymark Solutions has been an IT Integrator since 2001, headquartered in Burlington, Massachusetts and serving customers across North America. Daymark designs, implements and supports enterprise infrastructure across two converging practices: a modern data center practice spanning virtualization, enterprise storage, data protection, networking and cybersecurity as well as a Microsoft cloud and AI practice covering Azure, M365, Copilot, Copilot Studio, Foundry and Fabric.

Daymark holds Microsoft Frontier AI partner status, the designation Microsoft reserves for partners with the advanced certifications and proven delivery record to lead enterprise AI engagements. Daymark also operates a dedicated Azure Government practice, including GCC High enclave design and implementation for defense industrial base contractors managing CUI and pursuing CMMC compliance.

If you’ve got questions or want to discuss how these announcements can work within your environment, please contact us. We’re excited about the shift this complete platform architecture will bring to our customers.