AI promises efficiency, automation, and transformation - but enterprises often struggle to realize these benefits. The culprit isn’t the AI algorithms themselves - it’s the data foundation. Without robust enterprise data management, AI initiatives stall, budgets overrun, and ROI slips away.
In this article, we’ll break down the five biggest data challenges slowing down enterprise AI projects and how leaders can address them with the right strategy and technology.
5. Data silos across departments
One of the most common and damaging data challenges in large enterprises is the persistence of data silos. A silo occurs when data is stored in isolated systems that don’t talk to each other. Marketing might rely on a CRM, operations on an ERP, and finance on a separate accounting platform. While each system works for its own purpose, the lack of integration creates a fragmented data landscape.
For AI initiatives, this is a critical roadblock. AI models need access to holistic datasets to uncover patterns, generate insights, and recommend actions. When data is scattered across departments, leaders face three issues:
- Duplicate and inconsistent records that confuse analytics.
- Limited visibility across functions, slowing decision-making.
- Longer time-to-insight, as analysts spend weeks reconciling multiple systems instead of training models.
Take an example: imagine a retail enterprise where sales data lives in Salesforce, inventory data in SAP, and customer support logs in Zendesk. An AI model predicting churn would require all three datasets. But without integration, the insights remain incomplete, and worse, misleading.
Breaking silos requires strong enterprise data management practices like centralized storage, standardization, and governance frameworks. This ensures data flows seamlessly across functions and becomes truly AI-ready. A practical roadmap is shared in this guide on AI-ready data in a few steps.
4. Poor data quality and inconsistency
The saying “garbage in, garbage out” holds especially true for AI. If the data fed into a model is incomplete, outdated, or inconsistent, the output will be unreliable at best, and dangerously misleading at worst. Gartner estimates that poor data quality costs enterprises an average of $12.9 million each year, through inefficiencies, lost opportunities, and compliance risks.
The issue is compounded in enterprises where multiple departments collect data independently, each using different formats or standards. For example, a customer’s date of birth might be recorded as 01/02/1985 in one system and 1985-02-01 in another. To a human, the difference is obvious; to an AI model, it could mean two different people entirely.
Addressing this challenge requires enterprise data management frameworks that enforce consistency through automated validation, cleansing, and monitoring. Modern approaches use pipelines that continuously check for missing values, normalize formats, and flag anomalies before they reach the analytics or AI layer.
For enterprises looking to operationalize these practices quickly, platforms like Lakestack provide prebuilt pipelines and a no-code environment to enforce data quality at scale, without adding more engineering debt.
3. Governance and compliance risks
AI systems don’t just need data - they need data that’s handled responsibly. Enterprises operating in regulated industries such as healthcare, banking, and insurance face strict frameworks like HIPAA, GDPR, and SOC 2. If governance practices are weak, AI projects risk legal penalties, reputational damage, and loss of customer trust.
The common pitfalls include:
- Lack of clear access control policies, leading to unauthorized usage.
- Poor visibility into data lineage makes it hard to track how information flows.
- Incomplete audit trails complicate compliance checks.
Strong enterprise data management ensures that AI doesn’t become a compliance liability. By embedding governance into pipelines - through role-based access, encryption, and monitoring - enterprises can balance innovation with regulatory obligations.
More often than not, projects fail not because of the technology itself but because governance frameworks are overlooked. This is explored further in a breakdown of why do AI projects fail.
2. Unstructured data overload
Over 80% of enterprise data today is unstructured - ranging from PDFs and images to IoT sensor streams and call recordings. While structured databases are relatively easy to query, unstructured data requires advanced processing and storage strategies. Traditional warehouses were not built to handle this scale and diversity, which means enterprises either underutilize valuable information or spend heavily on patchwork integrations.
The challenge here isn’t just volume - it’s usability. AI models need structured representations, but without a unified pipeline, most unstructured data remains locked away.
A real-world example comes from the automotive sector, where customer feedback, sensor data, and service records were scattered across multiple systems. By unifying them into a single lakehouse, the company was able to train predictive models that improved customer retention and optimized supply chains. You can see the details in this case study on building a data lakehouse for an automotive company.
1. Lack of real-time insights
In fast-moving industries, timing is everything. A delayed insight is often as bad as no insight at all. Yet many enterprises still rely on legacy BI systems that deliver batch-based reports - sometimes hours or even days after the fact.
This lag makes it difficult for frontline teams to respond to emerging trends or risks. For instance, a hospital analyzing patient vitals in near-real time can intervene before conditions worsen, whereas a hospital working on weekly reports will always be too late.
AI thrives on real-time, query-ready data that can be tapped by executives, analysts, and even non-technical staff. Enterprise data management platforms that integrate streaming data pipelines with low-latency querying unlock this potential.
To see how organizations approach this shift, Tableau provides a useful overview of enterprise data management best practices in modern analytics ecosystems.
The way forward
AI projects fail not because of a lack of innovation, but because enterprise data management is overlooked. Enterprises that invest in strong data strategies, modern lakehouse platforms, and governance frameworks not only reduce risk but also accelerate ROI.
The shortcomings of traditional approaches are explained further in an analysis of why data lakes and warehouses fall short in the age of AI.
Solutions like Lakestack bring together data lake and warehouse capabilities in a single AWS-native platform, helping enterprises cut time-to-insight from months to weeks.
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