The blind spot of the AI strategy: the data basis
- Harriet Moser
- 5 days ago
- 2 min read
Updated: 5 days ago
Companies dream of AI-powered personalization, predictive customer service, and automated product development. But the foundation for this is missing – a consistent, clean dataset.
Why is this? Marketing, sales, and product development often operate in separate data environments. Marketing maintains contacts in its CRM system, sales uses Excel spreadsheets, and product development collects usage data in its own system. The result: duplicate data records, conflicting information, and communication breakdowns.
Why AI is doomed to fail without a shared dataset
An AI is only as good as the data it's fed. If your systems don't communicate with each other, at best you'll train three mediocre AIs – at worst, the AI recommendations will contradict each other.
Only when all departments access the same data set can AI reach its full potential: recognizing patterns, uncovering connections, and making predictions that individual departments would never see.
My three tips for AI-ready data structures:
Cross-functional AI use case workshop brings marketing, sales, product development, and customer service together. The question isn't "Which AI tools do we want?", but rather "What decisions would we make better if we had the bigger picture?"
Shared data space as the basis for AI: A central data pool that all departments can access – structured, maintained and with uniform standards.
Launching an AI quick win on shared data begins with a concrete use case: an AI that automatically identifies customer needs from all customer touchpoints (website, sales conversations, support requests, product usage). Once teams experience how AI works on shared data, the desire to collaborate emerges.
The inconvenient truth: Most companies don't fail because of a lack of AI technology. They fail because of siloed thinking and fragmented data. AI can't perform miracles – it can only reveal what's already in your data. And if that data is scattered across five systems, the AI will only see fragments.
The crucial first step is therefore not the selection of AI tools, but the question: Do we have the data basis so that AI can really move us forward?
