Executives are rushing to implement generative AI, often driven by fear of missing out rather than by engineering readiness. Surveys point to a wide gap between ambition and reality: 91% of EMEA companies believe their Data Architecture will be ready to support AI within 2-3 years, yet only 26% currently have data that is actually organized and properly supervised. In Poland specifically, 75% of companies report a positive attitude towards AI, but only 7% have actually implemented it.

The most commonly cited internal barriers are limited data visibility (41%), cultural reluctance to share data (35%), and data dispersion across systems (34%).

The core thesis: clean data is the prerequisite for AI ROI

An AI model is only as reliable as the data it retrieves from. Building on top of chaotic legacy systems tends to produce unreliable outputs. Industry estimates suggest that, without proper data structure, data handling and cleanup can consume up to 35% of a project’s total budget, and 23% of companies report that data quality, availability, or organization issues actively block scaling of AI implementations.

Organizing your data platforms first is the more reliable path to AI ROI. Struct4 provides the data transformation framework needed to stabilize these environments before AI is layered on top.

The hidden costs of dirty data: Cloud and compute costs

Feeding unstructured or duplicate-heavy data into an AI model increases token consumption and compute time, since the model has to process noise alongside signal. Deduplication and data-quality management before ingestion reduce this overhead and cut down on errors caused by inconsistent data across systems

The hidden costs of dirty data: From hallucinations to security exposure

Unstructured or conflicting data increases the likelihood of LLM hallucinations, since the model has to fill gaps in ambiguous or contradictory input. Separately, exposing unclassified data to AI search indexes creates access-control risk: it is difficult to enforce permissions on data you haven’t inventoried, and audit or compliance reviews become harder without a documented data history.

Addressing the counterarguments: can large context windows replace data cleaning?

Some argue that large context windows let modern LLMs filter out noise on their own, making data pre-processing unnecessary. This claim doesn’t hold up well in practice: larger, messier context tends to degrade retrieval accuracy and increase both cost and hallucination risk, rather than eliminate the underlying data problem. Relying on the model to compensate for messy data is a workaround, not a fix.

Speed to market versus technical debt

Executives often argue that speed to market outweighs early data governance, pushing proof-of-concepts onto shaky foundations. In practice this tends to delay production deployment rather than accelerate it, because unresolved data-quality issues resurface once a pilot needs to scale. A separate concern: one-third of surveyed companies report having no data management policy at all, and no plan to introduce one.

A roadmap: preparing enterprise data before AI integration

A practical sequence looks like this: first, an “as-is” audit that classifies existing data and surfaces where it’s disorganized. Second, transforming raw data into a reliable, deduplicated “golden record” through a validation and merge pipeline (in Struct4’s implementation, orchestrated with Apache Hop). Third, establishing access controls and compliance mapping, with data-quality checks acting as gates both after initial ingestion and before golden-record publication.

Time spent on this preparation is better understood as an acceleration phase than a delay: clean data tends to make later model changes and scaling easier, rather than requiring rework each time.

Where this leaves you

Before signing another AI vendor contract, it’s worth auditing your current data readiness and asking your technical teams directly how organized and governed your data actually is. The estimates cited above — up to 35% of budget lost to data handling, and 23% of companies blocked from scaling — are a reasonable starting benchmark, not a guarantee of your own numbers.

Struct4 works with organizations on this kind of data preparation, structuring, and governance. We’re glad to discuss what this looks like for your specific environment.

If you’re navigating your own AI pilot and running into data-quality roadblocks, we’d welcome the conversation — get in touch with the Struct4 team.