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.
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.
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40% report severe limitations due to poor data quality, silos, or outdated processes.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.
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.
These are the phases we recommend implementing if you want to turn your data architecture into a competitive advantage:
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)
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?
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
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…)
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.
At Hike&Foxter, we help you build the digital foundations your business needs to grow with confidence.