Why Data‑Driven Efforts Stall in Fragmented Environments - with Jason Loomis of Freshworks

2026-06-30 · Source: The AI in Business Podcast · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Emerging Technologies & Innovation · Depth: Intermediate, extended

Summary

Jason Loomis, CISO at Freshworks, addresses the core challenge of enterprise AI adoption: balancing rapid deployment with robust security and governance. He proposes a sequenced approach, starting with regulatory compliance, progressing to data trust, and culminating in AI-specific security frameworks. Loomis emphasizes that AI's transformation speed is unprecedented, creating significant risks alongside benefits. He notes that many organizations mistakenly expect immediate cost savings from AI without adequate investment in dedicated budgets for experimentation and development. The discussion also highlights culture as a critical engine for successful AI integration, contrasting conservative, highly regulated sectors like healthcare with faster adoption in less regulated areas like customer experience (CX). Effective communication with leadership, focusing on financial impact—saving money, making money, or reducing risk—is crucial for securing AI investment.

Key takeaway

For Directors of AI/ML balancing rapid deployment with security, prioritize a sequenced approach: establish regulatory compliance, build data trust, then implement AI-specific security frameworks like OASP's top 10. Crucially, advocate for dedicated AI investment, framing it in terms of increased revenue, cost savings, or risk reduction, rather than expecting immediate returns. Foster a culture where teams embrace AI to enhance their value, ensuring internal alignment and smoother integration.

Key insights

Enterprise AI adoption requires a sequenced approach, prioritizing regulatory compliance, data trust, and AI-specific security frameworks to balance speed and safety.

Principles

• AI transformation speed is unprecedented.
• Culture drives successful AI adoption.
• Investment must precede AI cost savings.

Method

Implement AI security via a sequenced approach: begin with regulatory compliance, advance to data trust, then integrate AI-specific security frameworks like OASP's top 10.

In practice

• Prioritize OASP's top 10 for AI security.
• Frame AI investment as financial impact.
• Embrace AI to enhance team member value.

Topics

• AI Adoption
• Enterprise AI Security
• Data Governance
• Regulatory Compliance
• AI Investment
• Organizational Culture

Best for: CTO, VP of Engineering/Data, Executive, AI Security Engineer, Director of AI/ML, Consultant

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