NEWSLETTER
On July 10, 2025, Validity released the State of CRM Data Management in 2025 report, based on a survey of over 600 professionals from the US, UK, and Australia. The conclusion is clear: most companies want to implement artificial intelligence, but their CRM systems aren't ready.
Specifically, only 24% of respondents believe that more than half of the data in their CRM is accurate and complete. Additionally, 45% admit their data isn't ready to feed AI systems, and 37% have lost sales due to decisions based on incorrect information.
Why data quality matters
Implementing AI tools without reliable data is like putting a Formula 1 engine in a car with no wheels. CRM data quality determines whether your AI models can:
- Personalize content and messaging.
- Accurately predict which leads will convert.
- Automate tasks without errors or duplication.
- Actually increase team productivity.
According to the report, many companies rush to adopt generative AI without first addressing fundamental data issues. 54% already use it, even though more than half acknowledge serious data quality problems.
Signs your CRM data isn’t ready
Across our projects in energy, retail, and tech sectors, we consistently identify red flags in CRM data quality:
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Incomplete or inconsistent fields (e.g., leads without industry, country, or source).
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Unmanaged duplicates: the same company listed multiple times.
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Lack of standardization (e.g., “Spain”, “España”, “ES”).
- Poorly structured properties that break automation.
Real consequences: AI that can’t learn, teams that don’t trust
- Lead scoring models that fail.
- Conversion reports that don’t add up.
- Automated workflows that trigger incorrectly.
Worst of all, sales teams stop trusting the system. This leads to reliance on external spreadsheets, reduced visibility, and decisions based on gut feeling.
How to improve CRM data quality
At Hike & Foxter, we recommend a gradual, pragmatic approach. Here’s a simple guide to get started:
1. Audit your current database
- What percentage of your contacts have key fields filled in?
- How many leads are duplicated?
- How many unique values exist for each critical field (e.g., industry, country)?
2. Define a clear taxonomy
You don’t need 50 new properties. But you do need to:
- Set unique, standardized values for each key field.
- Use dropdown menus instead of free-text fields.
3. Automate cleanup processes
- Workflows to detect contacts with missing fields.
- Duplicate detection (manually or with built-in tools).
- Rules to prevent malformed record creation.
4. Train your team
Technology alone isn’t enough. Data quality improves when sales and marketing teams understand how completing fields properly directly impacts opportunities and revenue.
5. Prepare your data for AI
If you're aiming for predictive scoring, AI agents, or personalized content, start by:
- Requiring mandatory data in pipeline stages.
- Tagging lead sources correctly.
- Segmenting based on business logic (not just technical fields).
Already using AI? Time for a health check
Validity's report shows many teams are already using AI… on incomplete data. If that’s your case:
- Review which variables your AI relies on.
- Validate if the underlying data is accurate.
- Monitor whether automated actions are generating noise or errors.
Conclusion: without data quality, AI doesn’t take off
The desire to apply AI is legitimate and strategic. But as this report shows, data quality is the real bottleneck.
At Hike & Foxter, we help companies prepare their CRM systems so that AI has the right foundation: clean, structured, and aligned with business objectives.
If you’re considering generative AI, predictive scoring, or smarter automation—let’s talk about your data first. Because without quality, everything else is on shaky ground.
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