No solid base, no AI performance: the challenge of the Data Foundation

In a business context where AI has become the new standard for efficiency and scalability, many organizations face a paradox: they have advanced technology, but they fail to achieve consistent results. The issue usually isn’t the algorithm—it’s the foundation. The Data Foundation is the true determinant of success or failure for any AI, automation, or CRM strategy.

This is confirmed by the latest TDWI (Transforming Data With Intelligence) study, published in June 2025, which warns that more than 49% of companies still lack a database ready to scale artificial intelligence projects.

The Data Foundation: more than just infrastructure

Having a modern data platform doesn’t mean having a solid foundation. The TDWI study emphasizes that an effective Data Foundation must meet three conditions:

- Data quality and governance from the source
- Scalable and connected architecture
- Real-time activation capability

When a company fails in any of these three areas, AI becomes more of a promise than a real business lever.

Key findings from the study

Here are some of the main conclusions of the report:

Only 10% of companies claim to have a fully operational Data Foundation.
40% report severe limitations due to poor data quality, silos, or outdated processes.
Most organizations suffer from fragmentation across data sources, preventing a 360-degree view of the customer.
55% of companies already using AI operationally do so despite their technical limitations, not because of their strengths.

In other words, many companies are running with a backpack full of ballast. And that limits the performance of their AI, automation, or CRM tools.

Why does this matter for your CRM or marketing?

At Hike&Foxter, we see it frequently: companies investing in advanced CRMs, analytics platforms, or generative AI engines… without first securing the technical and structural foundation of their data.

The result:

– AI models that fail in production.
– Automations triggered incorrectly.
– Unreliable analytics reports.
– Inconsistent customer segmentations.

All of this can be avoided with a well-designed Data Foundation, connected to key processes and with controlled data flows.

How to build a real Data Foundation

These are the phases we recommend implementing if you want to turn your data architecture into a competitive advantage:

1. Technical and functional audit

Before incorporating AI, it's important to review:

What data sources exist and how they are integrated
The degree of duplication, obsolescence, or noise they contain
Where the main bottlenecks are (latency, format, access)

2. Standardization and governance

Without a common taxonomy and control rules, any automation attempt will be fragile. This involves:

Defining unified structures (customers, products, interactions…)
Establishing automatic validation rules
Creating clear roles: who creates, modifies, or validates data?

3. Connected and flexible architecture

A data warehouse alone is no longer enough. You need to:

Connect CRM with analytics, automation, and digital channels
Use scalable environments (Snowflake, BigQuery, Azure Fabric)
Consider data mesh or federated architecture if there are multiple business units

4. Real-time activation

The value of AI lies not just in predictive analysis but in its ability to act. Therefore:

Connect your Data Foundation with activation tools (such as Customer Data Platforms, personalization engines, RPA)
Ensure data flows in real time
Prioritize use cases with direct business impact (retention, up-selling, lead scoring…)

Conclusion

Investing in AI, automation, or CRM platforms without a solid Data Foundation is like building a house on sand.
Before thinking about “which model to use,” you should ask yourself “what data feeds it and how is it governed?”

A robust and well-connected infrastructure not only improves your current projects but also prepares you for what’s next: autonomous agents, contextual decisions, predictive personalization, and end-to-end automation.

Want to strengthen your Data & Tech Foundation?

At Hike&Foxter, we help you build the digital foundations your business needs to grow with confidence.

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No solid base, no AI performance: the challenge of the Data Foundation

In a business context where AI has become the new standard for efficiency and scalability, many organizations face a paradox: they have advanced technology, but they fail to achieve consistent results. The issue usually isn’t the algorithm—it’s the foundation. The Data Foundation is the true determinant of success or failure for any AI, automation, or CRM strategy.

This is confirmed by the latest TDWI (Transforming Data With Intelligence) study, published in June 2025, which warns that more than 49% of companies still lack a database ready to scale artificial intelligence projects.

The Data Foundation: more than just infrastructure

Having a modern data platform doesn’t mean having a solid foundation. The TDWI study emphasizes that an effective Data Foundation must meet three conditions:

- Data quality and governance from the source
- Scalable and connected architecture
- Real-time activation capability

When a company fails in any of these three areas, AI becomes more of a promise than a real business lever.

Key findings from the study

Here are some of the main conclusions of the report:

Only 10% of companies claim to have a fully operational Data Foundation.
40% report severe limitations due to poor data quality, silos, or outdated processes.
Most organizations suffer from fragmentation across data sources, preventing a 360-degree view of the customer.
55% of companies already using AI operationally do so despite their technical limitations, not because of their strengths.

In other words, many companies are running with a backpack full of ballast. And that limits the performance of their AI, automation, or CRM tools.

Why does this matter for your CRM or marketing?

At Hike&Foxter, we see it frequently: companies investing in advanced CRMs, analytics platforms, or generative AI engines… without first securing the technical and structural foundation of their data.

The result:

– AI models that fail in production.
– Automations triggered incorrectly.
– Unreliable analytics reports.
– Inconsistent customer segmentations.

All of this can be avoided with a well-designed Data Foundation, connected to key processes and with controlled data flows.

How to build a real Data Foundation

These are the phases we recommend implementing if you want to turn your data architecture into a competitive advantage:

1. Technical and functional audit

Before incorporating AI, it's important to review:

What data sources exist and how they are integrated
The degree of duplication, obsolescence, or noise they contain
Where the main bottlenecks are (latency, format, access)

2. Standardization and governance

Without a common taxonomy and control rules, any automation attempt will be fragile. This involves:

Defining unified structures (customers, products, interactions…)
Establishing automatic validation rules
Creating clear roles: who creates, modifies, or validates data?

3. Connected and flexible architecture

A data warehouse alone is no longer enough. You need to:

Connect CRM with analytics, automation, and digital channels
Use scalable environments (Snowflake, BigQuery, Azure Fabric)
Consider data mesh or federated architecture if there are multiple business units

4. Real-time activation

The value of AI lies not just in predictive analysis but in its ability to act. Therefore:

Connect your Data Foundation with activation tools (such as Customer Data Platforms, personalization engines, RPA)
Ensure data flows in real time
Prioritize use cases with direct business impact (retention, up-selling, lead scoring…)

Conclusion

Investing in AI, automation, or CRM platforms without a solid Data Foundation is like building a house on sand.
Before thinking about “which model to use,” you should ask yourself “what data feeds it and how is it governed?”

A robust and well-connected infrastructure not only improves your current projects but also prepares you for what’s next: autonomous agents, contextual decisions, predictive personalization, and end-to-end automation.

Want to strengthen your Data & Tech Foundation?

At Hike&Foxter, we help you build the digital foundations your business needs to grow with confidence.

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Salesforce has once again set the pace for innovation with the announcement of Agentforce and Marketing Cloud Next—two solutions redefining automation within the CRM ecosystem. These innovations not only incorporate artificial intelligence but place generative AI at the core of business operations, enabling virtual agents to act autonomously across sales, marketing, and customer service processes.

In this article, we analyze the key aspects of this evolution and its impact on business productivity, with a focus on CRM and advanced analytics.

How Amazon uses AI in Prime Day 2025 to personalize the shopping experience

Amazon is preparing for its 2025 Prime Day, which will take place from July 8 to 11, with a clear focus on enhancing the shopping experience through artificial intelligence–powered tools. During the 96-hour event, exclusive to Prime members, AI will help users find exactly what they need at attractive prices—saving time and increasing relevance.

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