Why Some AI Projects Fail? - The Hidden Cost of Fragmented Data

You have invested in AI, hired the greatest AI talent, and above all, you have executive buy-in. That's great but still something is still not clicking and you are trying to figure out why.

Here is what most companies discover too late and not surprisingly, it is not a technology problem. It turns out to be a fundamental disconnect between your business information and your data reality.

The Million-Dollar Mistake

Take the example of a global insurance company that learned this the hard way. They spent millions on an AI system to predict customer churn. The technology was cutting-edge. Their data science team was world-class. But six months in, the predictions were barely better than guesswork.

The real problem wasn't technical, but it was conceptual.

The company had never clearly defined what "customer," "product," and "relationship" actually meant across their business.

As a result:

• A "customer" in sales wasn't the same as a "customer" in claims
• A "product" meant different things to different teams
• "Customer relationships" had multiple, conflicting definitions

I am sure many can relate to this, right? This fundamental confusion manifested in fragmented systems such as the following -

• Sales CRM with one view of customer
• Claims system with another view
• Policy platforms with yet another
• Customer service tools with their own version
• Regional databases with local variations

Getting to True Information Alignment

But here is the thing, even if you went about connecting systems, it wouldn't help. What you really need is define your core business information concepts. This means establishing the following.

Information Domain Concepts

We need to first start by defining the business information model.

• What are the fundamental building blocks of your business? (customers, products, agreements, locations)
• How are they defined? What are their essential characteristics?

Relationship Concepts

We should also capture the relationship concepts.

• How do these building blocks relate to each other? What rules govern these relationships?
• How do these relationships change over time?

Business Objects

And then the business objects.

• What specific instances of these concepts matter most? How are they identified and tracked?
• What makes them unique?

Building from Information to Implementation

Here is how one company went about with their approach:

Defining your core information concepts: First, they defined their core information concepts:

• Customer (individual, household, business)
• Product (offerings, services, entitlements)
• Agreement (contracts, terms, conditions)
• Relationship (connections, hierarchies, roles)

Mapping your relationships: Then they mapped how these concepts relate to each other.

• How customers connect to agreements
• How products link to services
• How relationships influence interactions

Developing your data architecture: Only then did they went on to tackle their systems.

• Created unified customer profiles and standardized product definitions
• Aligned agreement structures and built consistent relationship tracking

This resulted in 70% reduction in data conflicts, 85% faster AI model deployment, 3x improvement in prediction accuracy, and 45% increase in cross-selling success.

From Concept to Reality

As you can see, this is a business problem, first and foremost. The key is to take a progressive approach starting from information mapping to aligning relationships and building a data architecture before your implementation starts.

Information Mapping

• Document your core business objects
• Define key relationships
• Establish clear ownership

Alignment Assessment

• Where definitions conflict
• Where relationships break
• Where inconsistencies exist

Progressive Implementation

• Start with highest-impact concepts
• Build out related structures
• Expand systematically

The Bottom Line

As you translate this understanding into your data architecture, your data architecture team needs to focus on designing unified data structures and implementing them consistently across your organization. Remember to establish clear ownership for each piece of the puzzle, which includes knowing who is responsible for maintaining these definitions and relationships.

Before another AI investment, I hope you will invest in understanding your fundamental business information concepts.

The most sophisticated AI cannot overcome confused business definitions and relationships.

Therefore, I conclude by saying that your next step is not just another data integration project, but getting clarity about what your critical business information really means and how it connects.

The question isn't "How can we integrate our data?" but "Do we truly understand our core business information and relationships?"

These are two different things and it first starts from the business. Hope this article helped you comprehend this critical distinction that could decide the eventual success of your AI projects.

#DataStrategy #AI #InformationArchitecture #cio #cto #ceo #BusinessTransformation #Innovation

All opinions are my own and not those of my employer.

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