As enterprises double down on AI, there’s a hard truth many leaders are starting to face — they’ve invested in the promise of AI, but they can’t always trust the data behind the predictions.
Whether you're training a model, building RAG pipelines, or scaling intelligent automation, AI outcomes are only as reliable as the data feeding them. Yet most organizations still can’t answer a critical question with confidence: Is our data truly AI-ready?
That’s where Qlik Trust Score™ for AI comes in.
The Hidden Risk in Every AI Project
For years, data trust was treated as an IT hygiene issue — something to be dealt with behind the scenes. But AI has made trust a front-and-center business issue. If you can’t trust your data, you can’t trust your AI.
And it’s not just about accuracy. AI requires something more: data that’s fresh, representative, context-aware, and aligned to real-world use. These needs aren’t addressed by traditional data quality tools with intrinsic data quality metrics.
According to a recent Qlik survey, only 42% of executives say they fully trust their AI-generated insights — even though nearly 90% see AI as core to their competitive advantage. That trust gap isn’t just theoretical. It puts real business decisions, customer experiences, and regulatory compliance at risk.
Introducing Qlik Trust Score™ for AI
Included in Qlik Talend Cloud®, Qlik Trust Score™ for AI is a purpose-built metric that gives organizations a clear, objective way to assess the readiness of their data for AI use. It builds on our foundational Qlik Trust Score — which measures core dimensions like completeness, discoverability, and usage — by adding AI-specific lenses that reflect the demands of modern machine learning and GenAI systems:
Diversity: Is your data representative of the populations or patterns you’re modeling? Biased data leads to biased outcomes.
Timeliness: Is your data fresh and up to date? Stale data derails models and undermines decisions.
Accuracy: Does the data meet defined business expectations and rules? If not, even a well-tuned model will misfire.
Together, these dimensions offer a continuous signal of trust — not a one-time audit, but a dynamic, monitorable score that evolves as your data evolves.
Why It Matters Now
Responsible AI isn’t just a governance checkbox — it’s your competitive differentiator. Companies that can ensure that their data is trustworthy will build better models, make faster decisions, improve RAG responses and earn more confidence from customers.
Qlik Trust Score for AI makes that possible by:
Quantifying data trust across the data pipeline
Detecting changes in source data that may impact model accuracy and business outcomes before failures occur
Helping customers align with AI assurance principles, like those in the NIST AI RMF, through trust signals such as timeliness and diversity
Integrating continuous trust monitoring with ongoing data operations
And with the new Qlik Trust Score Historization feature, organizations can reach a whole new level of data observability by tracking changes in trust over time and correlating the results to downstream AI performance.
From Gut Feel to Governance
As Drew Clarke, Qlik’s EVP of Product & Technology, explains, “If you can’t measure trust, you’re gambling with outcomes, compliance, and customer experience.”
The time for guessing is over — don’t just hope your data is AI-ready. With its AI-specific dimensions like accuracy, diversity, and timeliness, and the ability to monitor trust over time, the Qlik Trust Score for AI can help you build the foundation for responsible, scalable AI. Explore more about how it works and possible next steps your organization can take today.
In this article:
AI