Executive Overview

Enterprises have made substantial investments in modern data platforms, cloud migration, analytics modernization, and AI initiatives. Yet, despite these efforts, enterprise data continues to fall short of the trust required to scale AI initiatives with confidence.

In 2026, the challenge is no longer about data availability or access. Most organizations have more data than ever. The real constraint lies in trust: whether enterprise data is complete, explainable, governed, and operationally reliable enough to support AI-driven decision-making at scale.

Chief Data Officers (CDOs) find themselves navigating an increasingly complex environment where fragmented data ownership, inconsistent governance, brittle pipelines, and limited accountability undermine AI outcomes. AI models may perform well in pilots but fail in production when exposed to real-world data variability, regulatory scrutiny, and operational dependencies.

This session examines why enterprise data still isn’t trusted for AI, where structural and operational constraints persist, and what must change in 2026 as organizations move from AI experimentation to AI accountability.

Drawing from real-world enterprise scenarios and emerging data operating models, this webinar helps data leaders understand how trust breaks down across the data-to-AI lifecycle and what redesigns are required to make AI dependable, scalable, and defensible.

What to Expect

This webinar provides a clear, operating-level perspective on:

Why enterprise data fails trust tests even after years of modernization
Where current data and AI architectures break under real production conditions
How governance, quality, lineage, and context gaps directly impact AI outcomes
What CDOs must redesign in 2026 to enable trusted, outcome-driven AI

This is not a product-focused discussion.

It is a data trust and AI execution discussion.

Agenda

Why Enterprise Data Still Isn’t Trusted for AI
- Data platforms optimized for access and analytics, not AI reliability
- Trust assumed at ingestion rather than validated continuously
- Fragmented ownership across data engineering, governance, security, and AI teams
- Inconsistent data quality, lineage gaps, and limited explainability
- AI pilots succeeding in isolation but failing at enterprise scale
- Metrics focused on data availability and pipeline uptime, not AI readiness
Where CDOs Are Hitting Real Constraints
- Legacy governance models unable to keep pace with AI-driven data usag
- Manual stewardship and policy enforcement that do not scale
- Limited visibility into how data is transformed, combined, and consumed by models
- Weak feedback loops between AI outcomes and upstream data corrections
- Regulatory and risk pressures increasing without corresponding control mechanisms
How AI Changes the Definition of Data Trust
- Shift from static data quality checks to continuous trust validation
- Context, lineage, and explainability becoming mandatory, not optional
- AI exposing hidden data issues faster and more visibly than analytics
- Trust moving from dataset-level assurance to use-case and model-level assurance
- Shared accountability between data, AI, risk, and business teams

What This Means for CDOs in 2026

As AI becomes embedded into core business processes, CDOs must confront critical design questions:

What does “AI-ready data” mean operationally, not theoretically?
Which trust controls must be automated versus manually governed?
How do you govern data when AI systems generate, transform, and act on it?
What operating model aligns data teams with AI outcomes, not just platforms?
How do you measure trust beyond quality scores and policy compliance?

These are operating-model decisions, not tooling decisions.

Key Questions This Session Will Answer

Why does enterprise data still fail AI trust tests despite modernization?
Where exactly do trust gaps emerge across the data-to-AI lifecycle?
How does AI fundamentally raise the bar for governance, quality, and context?
What must CDOs redesign before scaling AI across the enterprise?

Why Attend

Enterprise AI is moving from experimentation to accountability. As AI systems influence decisions, automate actions, and interact with customers and employees, data trust becomes a business-critical requirement rather than a technical aspiration.

This webinar equips data leaders with a clear, practical understanding of:

Why AI initiatives stall due to unresolved data trust constraints
How AI exposes weaknesses in current data operating models
What structural and governance changes are required in 2026

You will leave with clarity in an increasingly noisy AI landscape.

Who Should Attend

Chief Data Officers (CDOs), CDAIOs and Data Leaders
Heads of Data Governance, Data Quality, and Metadata
AI, Advanced Analytics, and Data Science Leaders
Risk, Compliance, and Data Security Leaders