Building a Virtuous Cycle of Analytics in Global Enterprises - with Barry McCardel of Hex

2026-03-11 · Source: The AI in Business Podcast · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Project & Product Management · Depth: Intermediate, extended

Summary

Barry McCardel, CEO of Hex, discusses how organizations can enhance data-driven decision-making by compressing the gap between executive questions and actionable insights. He highlights that enterprise data estates often prioritize platform expansion over decision velocity, leading to reporting layers that fail to accelerate strategic outcomes. The conversation emphasizes tightening feedback loops, operationalizing collaborative and AI-augmented analysis, and redefining data ROI based on adoption, trust, and measurable business impact, rather than solely on production metrics. McCardel argues against the traditional reliance on static dashboards, proposing a shift towards more conversational AI tools that allow for dynamic querying and deeper understanding of business trends. He also critiques the "tool creep" phenomenon and the common pitfalls in AI project implementation, advocating for a "shallow end" approach to AI adoption.

Key takeaway

For Directors of AI/ML and CTOs aiming to genuinely increase their organization's data-driven capabilities, prioritize shortening the lag between business questions and trusted answers. Your focus should shift from merely deploying dashboards to fostering collaborative, AI-augmented analysis that measures success by internal adoption and tangible business impact. Encourage rapid, iterative experimentation, starting with expert users, rather than pursuing large, top-down "magic pony" AI projects, to avoid common failure points and tool creep.

Key insights

True data-drivenness stems from rapid, trusted answers to business questions, not just dashboard proliferation.

Principles

• Data ROI is best measured by internal sentiment and partnership quality.
• Dashboards should initiate questions, not be the sole answer.
• High experimentation fosters successful AI adoption.

Method

Prioritize basic data accuracy and expert user adoption of AI agents first. This builds a trusted context corpus, enabling broader, more effective natural language self-serve capabilities across the organization.

In practice

• Evaluate data team ROI via internal sentiment surveys.
• Implement conversational AI to augment existing dashboards.
• Start AI initiatives with expert users for foundational context.

Topics

• Enterprise Data Strategy
• Data-Driven Decision Making
• AI-Augmented Analytics
• Data Team ROI
• Collaborative Data Platforms

Best for: Director of AI/ML, CTO, Executive

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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.