Why Historical Business Data Fails AI (and What to Do About It)

thaborwalbeek
Jan 12
4 min read

Garbage In, Garbage Out — Data Preparation for AI

Series 1: Preparing Your Data for AI (Part 2 of 4)

We have data, so we can start using it for AI

This is a common assumption when AI initiatives are discussed. They have been collecting data over years, their systems are stable (mostly transactional), reports are reconciled and dashboards are used on a daily basis by the business. So for a human this data appears to be reliable and well understood (with all its caveats as well).

But as we have seen, for AI systems this will not work as much, leading disappointing results, unstable models or results that are hard to explain.

The reason for this is not that historical data is useless, it was just never designed for learning (and therefore prepared for AI models).

Why Historical Data Exists in the First Place

To understand why historical data struggles with AI systems, we first have to look at the history of that data. Why does it exist at all?

Most (enterprise) data was once created to support:

Transactions
Operational processes
Compliance and audit requirements
Financial and management reporting

These systems are not meant for consistent data for the long-term. They need to be correct at the moment of execution. It actually represents how an organization operated at a specific point in time, and the longer the systems exist, the more changes might have occurred.

In contrast to the AI system, it treats the historical data as evidence and tries to learn patterns from it. So, it becomes harder for the AI system to learn these patterns if the source systems have changed their logic, their inputs and requirements.

Data that works well for the reporting purposes (and their interpretation) will be problematic when using it for training models.

The Hidden Instability in Historical Data

So yes, those historical datasets look stable for the current purpose, but they contain many layers of hidden change over time.

Examples include:

Schema drift
- Columns are added, renamed, repurposed, or deprecated over time.
Changing definitions
- A “customer,” “active user,” or “revenue” field may represent different concepts in different years.
Process changes
- New workflows, new tools, or reorganizations subtly alter how data is generated.
Manual corrections
- Data is fixed after the fact, often without traceability or consistent rules.

Humans cope with this remarkably well. Analysts know that “before 2021 this field meant X” or that “this system was unreliable for a few months.” Models have no such context unless it is made explicit.

From a model’s perspective, these changes are not history—they are noise.

Why Humans Can Work Around This (and Models Can’t)

When humans analyze historical data, they bring context that is not stored in the dataset itself:

Domain knowledge
Business judgment
Awareness of exceptions
An understanding of which numbers to trust and which to treat cautiously

While humans can see those inconsistencies and work with it, models do not have that kind of context.

So this means that an AI model will treat all data in the same way, unless told explicitly otherwise. So a change in the data somewhere 1 or 2 years back can be interpreted by the human, but for the AI model it looks like a sudden shift in reality. Therefore, hard to understand this shift and work with it.

As a result, models may:

Learn patterns that no longer apply
Overweight outdated behavior
Misinterpret corrected or backfilled data
Encode historical quirks as predictive signals

What was manageable for humans becomes structural problems for AI.

The Common Mistake: “Fixing” History

When teams discover these issues, the instinctive response is often to clean or normalize historical data until it looks consistent.

This is risky.

Attempting to retroactively “fix” history can:

Remove important signals about how the business actually evolved
Introduce assumptions that were never true at the time
Blur the line between observed data and reconstructed data

For AI, this distinction matters. Models need to know what actually happened, not what would have been convenient if it had happened differently.

The goal is not to make historical data look perfect. The goal is to make it interpretable.

What This Means in Practice

Preparing historical data for AI requires a different mindset than preparing it for reporting.

In practice, this means:

Preserve raw historical data
- Keep records of what was recorded, even if it is messy or inconsistent.
Capture context explicitly
- Document when definitions, processes, or systems changed—and make that information usable downstream.
Separate evidence from interpretation
- Distinguish between observed values and derived or corrected values.
Design forward-looking pipelines
- Accept that historical data is imperfect and focus on making future data generation more consistent and explicit.

Historical data should be treated as evidence about the past, not as a clean foundation for the future.

Why This Matters for AI Readiness

If historical data is used naively, AI systems will learn from artifacts rather than reality. The result is often:

Models that perform well in validation but fail in production
Predictions that degrade as business processes evolve
Outcomes that are difficult to explain to stakeholders

By acknowledging the limitations of historical data early, organizations can avoid building AI systems on unstable foundations.

This is not about delaying AI adoption. It is about preventing expensive rework and loss of trust later on.

What Comes Next

In the next post, we will move from why historical data fails to where many AI data problems actually begin: data ingestion and data organization.

Understanding how data enters your platform—and how it is structured from day one—is critical if you want to avoid locking historical problems into future AI pipelines.

This article is part of the series Garbage In, Garbage Out — Data Preparation for AI, exploring how organizations can build data foundations that actually work for AI.