Editorial
White Paper
Why Enterprise AI Fails at Scale
All forms of AI and analytics — data science and machine learning, generative AI and agentic AI — are built upon a foundation of analytical data infrastructure (ADI). So, as organizations face increasing executive pressure to deploy AI and turn it into a true engine of business value, a critical question emerges: What is the current state of their data infrastructure?
Without question, the answer directly impacts the success or failure of their AI-based initiatives.
At my company, we define analytical data infrastructure (ADI) as "the data workflow and technology ecosystem responsible for data ingestion, data persistence and data transformation.”
These capabilities, said Bill Hostmann, VP and Research Fellow at Dresner Advisory Services, “collectively support downstream consumption by analysts, analytic workflows, generative AI systems and AI agents.” In short, ADI is the operational backbone that determines whether AI efforts can scale effectively, operate reliably and deliver meaningful business outcomes.
Table of Contents
- The 3 Pillars Holding Up Your AI Strategy
- The Real Problem Isn't the Tools — It's the Glue
- 4 Infrastructure Patterns Every Data Leader Needs to Watch
- Where Enterprises Are Actually Spending
- The Roadmap That's Emerging Across the Industry
- Your AI Is Only as Good as Your Data Pipeline
The 3 Pillars Holding Up Your AI Strategy
ADI consists of three primary components:
- Storage and persistence
- Movement and quality (including ETL, ELT and MDM)
- Access and oversight (encompassing governance, observability, the semantic layer and data virtualization)
According to our research, organizations often feel pressure to address multiple challenges simultaneously, and we see a clear set of priorities.
Security and privacy stand out as a dominant concern. Close behind is data engineering, widely recognized as a critical enabler of success. This reinforces the reality that meaningful insights require strong data “plumbing” — or, as I describe it, industrialized data systems. Data engineering is essential not only for managing data but also for scaling data within an increasingly complex data ecosystem. Without question, organizations must manage many more data sources than in the past. As a result, what was once considered an “integration tax” at the periphery has now become a foundational requirement.
Related Article: The Rise of AI Factories: Inside the New Data-to-Agent Pipelines
The Real Problem Isn't the Tools — It's the Glue
The core technical challenge for analytical data infrastructure has shifted from selecting the right tools to integrating them into a coherent, scalable data system.
Organizations are no longer struggling with individual product adoption. They must instead grapple with how to seamlessly integrate security, business logic and cost predictability across the entire data supply and consumption chain.
At the same time, success increasingly depends on managing the coexistence of legacy system stability and modern cloud-based systems. The ability to operate and optimize across hybrid architectures — without disrupting existing systems and while enabling new capabilities — is a defining factor in organizational performance.
4 Infrastructure Patterns Every Data Leader Needs to Watch
Clear infrastructure patterns are just emerging.
The Cloud as an Anchor
First, the cloud has become the anchor of a strategy. Major cloud providers have become a primary infrastructure foundation, while engineering teams will continue to layer specialized third-party tools on top to address functional gaps and economic bottlenecks.
The strategy is no longer cloud versus best-of-breed — it is cloud as base, with targeted augmentations.
Platform Preference vs. Operational Reality
At the same time, “there is a growing tension between platform preference and operational reality," said Hostmann.
While 73% of organizations prefer a single-platform approach due to simplicity, 86% operate multi-vendor environments to meet needs around performance, resilience and compliance.
"Simplicity may be the aspiration, but diversification is the practical necessity.”
A Shift to Operational Hardening
Research portends there is a shift occurring from experimentation to operational hardening.
Organizations are moving beyond pilot projects and proofs of concept toward production reliability, cost predictability, real-time data movement and AI-ready architectures. Infrastructure decisions are increasingly evaluated through the lens of operational durability.
Balancing Stability and Innovation
Another pattern is the balance between warehouse stability and lake house innovation.
Traditional data warehouses remain the backbone for mission-critical analytics, providing trusted, governed reporting environments. However, increasing AI maturity involves accelerating adoption of more modular, lake house-centric architectures that better support scale, flexibility and advanced workloads.
All of this points to what Hostmann calls a managed coexistence era. "Here, infrastructure portfolios are split across new modernization initiatives, stabilized cloud implementations and hardened legacy core systems. Technical success depends less on wholesale replacement and more on coexistence, interoperability and seamless integration with mission-critical environments.”
Where Enterprises Are Actually Spending
Investment patterns show a clear enterprise priority.
For today’s modern data stack, organizations are concentrating spending on foundational capabilities:
- Data security and privacy (58%)
- Data engineering (56%)
- The data warehouse (55%)
These are not experimental investments — they are core infrastructure commitments designed to stabilize and scale analytic operations.
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At the center are what can be called data anchors. The data warehouse remains the primary source of truth, with 76% of organizations viewing it as established and mission-critical. Data lakes are also a significant priority, with 54% actively investing in them to support broader and more flexible workloads. Together, these platforms form the backbone of modern analytic environments.
In contrast, more advanced capabilities — data observability, semantic layers, data virtualization and real-time monitoring — are largely positioned on a 24-month horizon. The reason is pragmatic: teams are prioritizing reliable data movement, storage and governance before layering on higher-order optimization and abstraction technologies. "It is stability first; refinement second," said Hostmann.
The Roadmap That's Emerging Across the Industry
Go-forward strategies reflect a foundation-first mindset. The above research shows that only two categories of investment have surpassed 50%:
- Data security and privacy (64%)
- Data engineering (58%)
These are clearly viewed as prerequisites for everything else. Organizations are hardening the base before expanding capabilities.
At the same time, 20-30% of buyers are selecting secondary vendors that are tightly integrated with their strategic ADI providers. Even in multi-vendor environments, out-of-the-box connectivity is becoming a decisive selection criterion.
The next 12 months will bring a second implementation wave focused on governance and operational intelligence. Data governance and cataloging lead planned investments (34%), followed by data observability (29%). Yet platform consolidation in these areas lag significantly, with only 16% using a single platform for governance and cataloging and just 10% doing so for observability. This signals high fragmentation, early market maturity and persistent integration complexity.
Several strategic conclusions emerge.
- Security and privacy are universally dominant priorities and will remain so.
- Data engineering is the bottleneck: rising integration complexity and accelerating data volume growth are creating systemic constraints, making high-quality, resilient pipelines mission-critical.
- We have entered an integration economy in which integration capability itself is a competitive differentiator. The “integration tax” is unavoidable and must be deliberately engineered around.
- This is the era of managed coexistence. Wholesale migration is neither realistic nor necessary. Success will depend on interoperability across legacy systems, cloud platforms and modern architectures — without disrupting mission-critical operations.
Related Article: Data Engineering Is Key to Scaling AI — Here’s What the Latest Research Says
Your AI Is Only as Good as Your Data Pipeline
AI ambition now outpaces infrastructure readiness for many organizations. While executives push aggressively toward generative and agentic use cases, most enterprises are still building the foundations required to make those systems reliable, secure and economically sustainable. The research makes one reality unmistakable: AI success is primarily constrained by analytical data infrastructure.
Security and privacy have become non-negotiable. Data engineering has emerged as the systemic bottleneck. Integration — once viewed as an ancillary cost — has become a strategic competency. And rather than wholesale modernization, enterprises are operating in an era of managed coexistence, balancing warehouse stability, lake house flexibility, cloud anchoring and legacy durability.
The organizations that succeed in AI will not be those that chase the most experimental use cases. They will be those that industrialize their data foundations — hardening pipelines, enforcing governance, rationalizing multi-vendor complexity and engineering for interoperability at scale.
Stability first. Refinement second. Innovation on top.
In the end, AI does not transform organizations on its own. Analytical data infrastructure determines whether AI remains an aspiration — or becomes a business advantage.
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