The AI credibility crisis in enterprise data

You've invested in the models. You've scaled the infrastructure. But your teams are still verifying AI answers before they act on them. Why? Because enterprise data lacks the governance layer AI actually needs. 

In this webinar, we'll explore findings from the 2027 Data & Analytics Insights Report conducted with Hanover Research— surveying 325 global data decision-makers — and unpack the widening gap between confidence in AI and trust in AI outputs. You'll learn why 54% of organizations trust AI but verify critical results, and what 38% say would close that gap: the ability to trace answers back to source data. 

We'll show how AI architectures built on a governed semantic layer outperform those querying raw tables at scale, how Simba Agentic Intelligence connects AI to live enterprise data without moving or copying it, and how business context applied at the source eliminates hallucinations and creates audit trails that hold up under regulatory scrutiny.

Whether you're building AI into customer-facing products, deploying enterprise analytics in regulated industries, or scaling AI across your organization, this session answers the question your teams are asking: How do we trust AI when it matters most?

What you'll learn:

• Why data governance is the architecture that AI depends on

• The difference between governance added after the fact and governance built in from the start

• How to deliver consistent, verifiable AI outputs that reduce debate and accelerate decisions

• Why on-premises and hybrid deployment matter for organizations that can't move data to cloud-only platforms