Fixing the Pilot‑to‑Production Gap in Enterprise AI - with Ronny Fehling of HTEC

2026-05-22 · Source: The AI in Business Podcast · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management · Depth: Intermediate, extended

Summary

Ronny Fehling, Chief AI Transformation Officer at HTEC, identifies the "Pilot-to-Production Gap" in enterprise AI as a sequencing problem, not a technology or data failure. He explains that AI initiatives stall because pilots are often built "next to reality" rather than "inside reality," leading to integration, validation, and governance issues at the production threshold. Successful organizations, regardless of budget, utilize narrow "production slices" within real workflows, implement decision gates with "kill-switch" authority, and foster executive commitment to learning over fixed destinations. Fehling cautions against large-scale, top-down AI mandates and heavy investment in foundational platforms without prior production proof, citing recent multi-billion dollar capital injections into AI deployment companies.

Key takeaway

For Directors of AI/ML overseeing enterprise deployments, recognize that the "Pilot-to-Production Gap" is a sequencing challenge, not a tech issue. Focus on delivering small, valuable "production slices" within existing workflows, ensuring they solve real pain points. Implement decision gates with genuine "kill-switch" authority to pivot or stop initiatives that don't prove production viability, avoiding costly top-down mandates and premature platform investments.

Key insights

Enterprise AI initiatives stall due to poor decision sequencing and pilots built outside real workflows, not technology.

Principles

• Build AI inside real workflows.
• Use decision gates with kill-switch authority.
• Commit to learning, not just destination.

Method

Start with a narrow, valuable "production slice" (6-12 weeks) embedded in a real workflow, then use decision gates to de-risk and reduce uncertainty before scaling.

In practice

• Choose use cases where value is real.
• Prioritize usefulness from day one.
• Prove production viability before scaling.

Topics

• AI Adoption
• AI Pilot-to-Production
• Enterprise AI Strategy
• Production Slices
• Decision Gates
• AI Governance

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

Related on AIssential

• Fixing the Pilot‑to‑Production Gap in Enterprise AI - with Lawrence Whittle of HTEC Group — AI adoption requires shifting from isolated pilots to integrated, end-to-end deployments focused on measurable business value.
• Why Your AI Demo Will Die in Production — Most generative AI pilots fail due to "Production Debt" across five key areas, not just model limitations.
• FDEs & The Enterprise AI Adoption Wedge — The problem is not the AI model's capability, but enterprise integration and adoption challenges.
• The Memory Crunch Is Stress-Testing Enterprise AI — Enterprise AI's true challenge lies in fragmented, inconsistent data foundations, not model quality.
• Why AI that works in the lab often fails in production — and what actually fixes it — Successful enterprise AI requires bridging foundational research with applied development and rigorous, continuous evaluation.
• Issue #133 - AI-Assisted Coding: From Notebooks to Production — Data scientists using AI for production must shift from output-producing code to robust, production-grade code that *is* the result.

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