Why Your AI Strategy is Failing the Audit: The Provenance Gap in Manufacturing
In the race to automate, many manufacturers are hitting a wall. It is not a lack of processing power or a shortage of LLMs. It is a trust problem. The “bottleneck” to scaling AI in the industrial sector is data provenance, the ability to prove exactly where a piece of data originated and how it has been handled. If you cannot verify your data’s history, you cannot use it in a regulated environment. For manufacturers, this is the difference between a successful AI rollout and a costly compliance nightmare.
The “Top-Down” Trap: Why Provenance is Ignored
This trust problem often begins in the boardroom. Driven by a fear of missing out, many boards are pushing AI initiatives from the top down. This creates “pilot purgatory,” where companies throw money at the latest technology without auditing the input source.
Jay Allardyce, CPO of Octave, and Chris Huff, CEO of Adlib, describe this as a “productivity paradox.” Manufacturers have spent decades creating data silos and placing humans in the middle to act as manual interpreters. When you layer AI on top of these fractured silos without establishing provenance first, the system breaks. You don’t get efficiency; you get “hallucinations” and a massive pile-up of rework.
From Silos to the “Agentic Layer”
Solving this paradox requires a shift in how we view software. We are moving away from traditional SaaS toward an Agentic Layer, where we no longer just use tools. We “hire” digital workers.
McKinsey has noted that digital workers are beginning to scale at a pace far beyond physical hires. However, for these agents to be effective and auditable, they require a rigorous “chain of custody” built on three pillars:
- Standardization: You must normalize hundreds of fractured file types, from 3D CAD drawings to site permits, into a single “language” the AI can read.
- Traceability: Every decision an AI agent makes must be traceable back to a source artifact to survive a regulatory audit.
- Human Orchestration: With provenance established, the goal is not to replace the worker but to turn the analyst into an orchestrator who manages a fleet of reliable digital agents.
Capturing Tacit Knowledge as Data
This shift to orchestration is the only way to solve the industry’s looming “brain drain.” As tenured workers retire, they take with them 30 years of tacit knowledge, the invisible patterns and shop-floor intuition that never made it into the manual.
The real power of scaling AI lies in capturing this human experience and turning it into part of your data provenance. When an AI agent is trained on the verified “why” behind a veteran’s decision, it allows the next generation of workers to bypass the traditional apprenticeship curve and move straight into high-value decision-making.
The Goal: Accuracy at Velocity
Ultimately, the manufacturing floor values Accuracy over Speed. Whether you are tracking every bolt on an aircraft or managing the structural integrity of a new build in a hurricane zone, your data must be defensible.
Speed without direction is a liability. By establishing a layer of data provenance, manufacturers move toward Velocity(Speed + Direction). In a regulated industry, that direction is provided by an accuracy layer that ensures every AI output is auditable and every digital worker is staying “on the rails.”
How to Move Forward
The tech race is a commodity race; do not wait for the “perfect” model. Instead, focus on your First Principles:
1. Define the Outcome: Start with the business value. Is this initiative driving margin or protecting top-line revenue?
2. Clean the Input: Use an independent third party to validate and normalize your data before it reaches the model.
3. Build an Ecosystem: Partner with platforms that prioritize frictionless integration over closed systems.
This article was written based on an interview that was recorded live this week, at the 30th Annual ARC Industry Leadership Forum (2026) in Orlando, Florida.
Jay Allardyce, CPO of Octave, and Chris Huff, CEO of Adlib, discuss the shift from AI experimentation to industrial scale. They dive into why data provenance and “digital workers” are the missing links for manufacturers trying to move beyond pilots and into defensible, auditable AI operations.
About the author
Lucian Fogoros is the Co-founder of IIoT World.