AI is everywhere. From marketing campaigns to sales forecasting to customer service, organizations are investing massively in generative AI and smart agents. Yet in practice, we see that many of these initiatives disappoint. Not because the technology is failing, but because the foundation is missing: clean, reliable data.
AI is not a magic bullet that automatically creates value. AI is powerful, but also sensitive. Even the most sophisticated models can fail when fed contaminated, incomplete or inconsistent data. Research from Forrester suggests that data quality is currently the primary limiting factor for adoption of generative AI in B2B contexts: the classic "garbage in, garbage out" applies now more than ever.
MIT Sloan/Redman research shows that organizations lose between 15% and 25% of their revenue each year due to poor data quality. Think duplicate records, missing fields and inconsistent data. The result? Errors, inefficiencies and endless correction work. For a company with €10 million in sales, this means that €1.5 to €2.5 million just goes up in smoke.
This is why data quality is not a luxury project, but an absolute necessity. Without a solid data foundation, AI applications are doomed to fail and deliver frustration rather than value.
Moreover, recent research shows that the real breakthrough only comes when organizations proactively ensure data quality: not just cleaning up after the fact, but preventing errors at the source and structurally improving processes.
Because one thing is certain: without clean data, AI gives misleading outputs, undermines trust and can even lead to wrong strategic decisions.
Yet governance often remains an understudy. Everyone feels involved in data, but no one is accountable. This leads to fragmented initiatives and endless discussions about who owns "the right customer record."
The truth is simple: without accountability no data quality and without data quality no AI success. This leads to:
Without clear ownership, a vacuum is created: data quality decisions go unimplemented. And at a time when AI needs trust, this is fatal.
The idea that AI only adds value in large, complex implementations is a fallacy. It is precisely small, targeted AI agents that can deliver significant savings, provided your data foundation is in order.
Prospecting: by automating 50% of repetitive prospecting tasks, you save an average of €40,000 per BDR per year.
Customer service: if 25% of tickets are handled automatically via smart ticket deflection, that will save €45,000 per service employee per year.
These are not theoretical calculations, but practical results that we are already seeing at organizations. The key? Reliable, consistent and structured data.
Only when AI agents are running on a solid data foundation can you roll them out cross-functionally. Then you shift from marginal improvements per single use case to scalable returns that are felt throughout the organization.
The key question is not whether you will deploy AI, but whether your organization is ready. And that starts with confrontational questions:
| Topic | Question |
|---|---|
| Data Discipline | How do we ensure uniformity without frustrating users? |
| Mandatory Fields | Which fields are really mandatory, and how do we create support? |
| Ownership | Who should have ownership for data quality - person (marketing manager) or function (sales operations)? |
| Product Owner | If HubSpot is a business platform, what specifically can a Product Owner contribute to adoption and data quality? |
| Superadmins | What belongs to the role of superadmins and what, on the contrary, should be invested in the business? |
| KPIs | What signals or KPIs do we use to structurally monitor data quality? |
| Process changes | What approval structure is needed before making impactful changes? |
| Onboarding | How do we ensure that new employees learn the right CRM habits from day one? |
| AI & Data | Where does AI really help, and where should basic structures be in place first? |
| Adoption vs. Data | How do we ensure both broad adoption and reliable data quality? |
| Single Source of Truth | How real is this for us and how do we deal with ERP or external data? |
| Dashboards | How do we balance freedom for users with one reliable set of control information? |
| Continuous improvement | How do we prevent proliferation and still ensure continued development? |
Successful organizations invest data quality explicitly. With an Executive Sponsor who carries the strategic vision, a Product Owner who prioritizes, Data Stewards who safeguard data quality, and IT who ensures system integrity.
It's not about titles, it's about mechanism: who is accountable, who decides, and who safeguards? Some key roles are:
When these roles are not sharply defined or overlap, gaps occur. The result: decision-making slows, data quality deteriorates and governance does not deliver the impact needed.
Based on practical experience and best practices, these are the factors that make the difference:
In addition:
You can't think of AI as additional functionality; it's a strategic path. And as Forrester notes, operational data quality is the primary limiting factor for genAI adoption in revenue and growth functions.
Without good data governance:
In contrast, with a solid governance foundation, you can:
Use these as springboards, but let the real work begin in your organization.
AI will determine which organizations win and which lag behind in the coming years. Not because it is the latest hype, but because it is capable of fundamentally changing business operations.
The only question is: Is your data clean enough for AI to deliver on its promise? As an Elite HubSpot Partner, we help organizations make AI truly scalable.