The new enterprise AI expert every company needs - and why

2026-07-01 · Source: News and Advice on the World's Latest Innovations | ZDNET · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Advanced, medium

Summary

Boomi CEO Steve Lucas, speaking at the company's World Tour 2026 event in London, highlighted the "frontier engineer" as the critical new role for enterprises seeking competitive advantage in AI. This expert possesses an advanced degree in data and neural networking, understanding how to optimize frontier models and neural networks for practical application, rather than just building them. Lucas emphasized the scarcity of such talent, estimating fewer than 3,000 individuals globally can build and train models at scale, and even fewer in non-tech enterprises deeply grasp neural network mechanics. Unlike transient roles like prompt or harness engineers, the frontier engineer offers enduring skills essential for squeezing productivity from AI deployments. This role bridges the gap between strategic AI leadership and hands-on AI builders, focusing on effective model exploitation.

Key takeaway

For IT professionals considering career paths in AI, prioritize developing deep expertise in data science and neural networking. Your focus should be on understanding and optimizing frontier models, as this "frontier engineer" skill set offers enduring value beyond transient roles like prompt engineering. Invest in advanced training to become the rare expert who can effectively exploit AI models, ensuring your organization gains a critical competitive advantage and maximizes productivity from its AI investments.

Key insights

Frontier engineers with deep data and neural networking expertise are vital for optimizing enterprise AI models.

Principles

• Enduring AI skills require deep data science.
• Optimize frontier models for competitive edge.
• Neural network understanding is a rare asset.

In practice

• Focus on advanced data and neural networking.
• Prioritize model optimization over basic prompting.
• Seek experts to exploit AI model capabilities.

Topics

• Frontier Engineer
• Enterprise AI
• Neural Networks
• Data Science
• AI Model Optimization
• AI Career Paths

Best for: CTO, VP of Engineering/Data, Executive, IT Professional, Director of AI/ML, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by News and Advice on the World's Latest Innovations | ZDNET.