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From Data Confusion to AI Confidence

Data Is the Foundation of Trustworthy AI

AI PULSE SURVEY | VOL 2

5 min read

Data isn’t just fuel—it can also be friction. The latest AI Pulse Survey reveals that the biggest barrier to AI success isn’t technology—it’s trust. Organizations that outperform in AI are those that trust their data, govern it well, and empower their teams to use it confidently. This report highlights how data confidence drives ROI, where gaps persist, and what leading organizations are doing to turn complexity into clarity.

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What your peers are saying about AI data confidence

Vol. 2 Key Findings

AI Maturity Stages Defined

These 5 stages of maturity were leveraged by respondents to benchmark their status.

Scaling AI? Scale your data confidence too

Data Confidence by AI Maturity

Confidence in data: a maturity marker

Key takeaways

Progress with AI closely correlates with the quality and management of data.
Early-stage AI adoption often begins with not-fit-for-purpose, incomplete or imperfect datasets, which are refined over time through iterative processes.
As organizations mature, their data practices become more structured and intentional.

Data Confidence by Industry

Technology sector leads the way in data confidence

Key takeaways

A strong culture of innovation and early AI adoption – along with easy access to digital infrastructure and fewer regulatory barriers – gives the technology sector a distinct competitive edge over other sectors.
Financial services data trust variability is significant in a sector where minor data issues can lead to serious consequences.
Retail and consumer packaged goods confidence levels are mixed which may reflect the challenges of managing large volumes of customer and supply chain data across multiple channels.

Data Confidence Pays Off

Organizations confident in their data are 3x more likely to exceed AI ROI expectations

Key takeaways

Data confidence grows with maturity.
This confidence is built through a combination of governance, training, and transparency—not just technical infrastructure.
As organizations mature, they become better equipped to manage and trust their data, which directly fuels AI success.

Bias Awareness Evolves with Maturity

Stage 1 and Stage 5 both report low bias—but Stage 1 likely isn’t seeing it, while Stage 5 is actively reducing it.

Key takeaways

Bias is always present; but recognition, detection and mitigation depends on data literacy and maturity. As organizations progress, they develop the literacy and frameworks needed to identify and reduce bias—transforming it from a hidden risk into a managed variable.
Stage 5 organizations mitigate bias proactively through governance and transparency.
Early-stage organizations may need better tools and frameworks to identify bias. Their relatively simple use cases may also factor into making bias less likely.