Bigeye Staff
bigeye-staff
Thought leadership
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February 12, 2026

Webinar Replay: Insights from 400+ Enterprise AI Initiatives

min read

Bigeye Staff
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In the first session of Bigeye’s AI Guardian Webinar Series, Bigeye CEO Eleanor Treharne-Jones is joined by EVP of Digital Strategy, Danielle Crop (former CDO at American Express and Albertsons), and Shawn Rogers, CEO of BARC US, to unpack new research based on 421 global enterprises actively deploying AI. The conversation focuses on what changes when organizations move from experimentation to production, and why many AI initiatives stall while trying to scale.

The panel explores several of the most consistent blockers enterprises are now facing, including the sharp rise of data quality as the #1 barrier to AI success, the reality of unexpected compute and platform costs, and the increasing need for lineage, monitoring, and runtime controls as agentic systems become more common. The discussion also highlights what separates AI leaders from followers, including the governance foundations leaders put in place early, and the risks that emerge when AI initiatives expand faster than enterprise oversight.

If your organization is navigating the gap between AI pilots and production delivery, this session offers a grounded look at what enterprise teams are learning in real time, and what it takes to build AI systems that are not just powerful, but trustworthy.

Watch the full replay above to explore the research findings and hear directly from leaders operating at the front edge of enterprise AI.

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Resource
Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
$15,000
3
12
$540,000
Data analyst
$12,000
2
6
$144,000
Business analyst
$10,000
1
3
$30,000
Data/product manager
$20,000
2
6
$240,000
Total cost
$954,000
Role
Goals
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Monitoring
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights
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Bigeye Staff

about the author

about the author

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