For decades, the promise of the digital factory was simple: more data equals more efficiency. Yet, as discussed at the ARC Industry Leadership Forum 2026, many manufacturers find themselves trapped in a productivity paradox. Despite having more sensors and dashboards than ever, teams are spending more time reconciling information than building products.
To break this cycle, manufacturers must stop using people as the “glue” between systems and start embracing the shift toward agentic orchestration, according to Chris Huff, CEO of Adlib Software, and Jay Allardyce, CPO of Octave, in an interview recorded at the event.
What Is Hand-off Waste in Manufacturing?
Hand-off waste occurs when design changes must pass through disconnected systems and humans are forced to act as manual bridges between engineering, permitting, and site management silos, consuming margins through schedule overruns and liquidated damages.
In large-scale industrial projects, design changes are a constant reality. These changes are triggered by everything from material shortages to site survey discrepancies. However, the way most organizations handle these changes is fundamentally broken.
Jay Allardyce, CPO of Octave, identifies this as a primary source of “waste.” When a design change occurs, the information must pass through multiple silos, permitting engineering and site management. Because these systems do not talk to each other, humans are forced to act as manual bridges. This hand-off waste eats directly into the thin margins manufacturers rely on. Every hour a human spends interpreting a fragmented data set is an hour lost to schedule overruns and liquidated damages.
From Software Tools to Digital Coworkers
The manufacturing industry is moving from traditional SaaS tools toward an agentic layer where AI agents function as digital coworkers, taking over the remedial tasks of data reconciliation.
The solution to this waste goes beyond additional software. It represents a shift in the nature of labor itself. The industry is moving from traditional SaaS toward an agentic layer, where teams “hire” digital teammates instead of relying on traditional software tools.
Chris Huff, CEO of Adlib, describes a transition from a world of slow apprenticeship to a world of agentic orchestration. In this new model, the digital worker is the primary engine. Unlike the basic automation of the past, these agents contextually understand specific operating procedures and regulatory guidance.
As these digital workers take over the remedial tasks of data reconciliation, the human role changes. A plant manager or engineer becomes an orchestrator, overseeing a fleet of agents that process data at a scale and speed no human could match.
How Can AI Solve Manufacturing’s Brain Drain?
Agentic orchestration captures the tacit knowledge of retiring workers by training AI agents on their decision-making patterns, preserving decades of operational experience as a digital asset.
As veteran workers retire, they take with them decades of tacit knowledge, the “why” behind how a plant operates that was never written down in a manual. This is the industry’s looming labor crisis.
The power of an agentic strategy is the ability to capture this human intuition and turn it into a digital asset. By training AI agents on the patterns of the most experienced workers, organizations ensure that their context stays in the building. A new hire can step into a high-stakes environment and instantly access thirty years of operational “wisdom,” effectively compressing the apprenticeship curve.
What Is Model Sovereignty in Manufacturing AI?
Model sovereignty is the ability to use multiple competing AI models for different tasks through an independent accuracy layer, rather than being locked into a single provider.
The biggest mistake a manufacturer can make in 2026 is waiting for a single “winner” in the AI race. Models are becoming commodities. The real value lies in model sovereignty.
By using an independent accuracy layer, manufacturers can use “multi-element voting.” This allows different AI models to compete to provide the most accurate answer for a specific task, whether it is precision part-matching or emergency response. Organizations are choosing the best “digital brain” for the specific job at hand rather than depending on one provider.
Breaking the Paradox
Manufacturers can break the productivity paradox by identifying hand-off points where engineers act as data bridges, standardizing document inputs for AI consumption, and capturing expert decision patterns before retiring workers leave.
To move forward, manufacturers should:
- Identify the hand-offs: Find where engineers are acting as “data bridges” and automate that transition.
- Standardize the input: Ensure 3D CAD drawings, PDFs, and site surveys are normalized so agents can read them without error.
- Capture the context: Use AI to document the decision-making patterns of retiring experts before they leave.
The goal is a frictionless flow where information moves as fast as the physical assembly line.
Traditional Approach vs. Agentic Orchestration
| Dimension | Traditional Approach | Agentic Orchestration |
| Data reconciliation | Engineers manually bridge system gaps | Digital workers automate hand-offs |
| Knowledge transfer | Slow apprenticeship | AI agents trained on expert patterns |
| AI model strategy | Locked into one provider | Model sovereignty with multi-element voting |
| Human role | Data bridge between silos | Orchestrator overseeing agent fleet |
| Design change handling | Manual routing through disconnected systems | Automated flow across engineering, permitting, and site management |
This article is based on a video interview with Chris Huff, CEO at Adlib Software, and Jay Allardyce, CPO at Octave, recorded with Lucian Fogoros of IIoT World at the 30th Annual ARC Industry Leadership Forum (2026) in Orlando, Florida. AI tools were used to help summarize and organize the content. Reviewed and edited by the IIoT World editorial team.
Sponsored by Adlib Software.
Frequently Asked Questions
1. What is the productivity paradox in manufacturing?
The productivity paradox in manufacturing describes the counterintuitive situation where increased investment in sensors, dashboards, and data systems fails to produce expected efficiency gains because teams spend more time reconciling fragmented information across disconnected silos than building products. The root cause is hand-off waste, where humans act as manual bridges between systems that do not communicate with each other.
2. What is agentic orchestration in industrial AI?
Agentic orchestration is a model where AI agents function as digital coworkers that contextually understand specific operating procedures and regulatory guidance, taking over the remedial tasks of data reconciliation. In this model, human workers shift from manually bridging data between systems to overseeing and directing a fleet of AI agents that process data at scale.
3. What is model sovereignty and why does it matter for manufacturers?
Model sovereignty is the ability to run multiple competing AI models through an independent accuracy layer, selecting the best model for each specific task rather than being locked into a single provider. This approach uses multi-element voting, where different models compete to provide the most accurate answer for tasks like precision part-matching or emergency response.
4. How does agentic AI address the manufacturing labor shortage?
Agentic AI captures the tacit knowledge of retiring veteran workers by training agents on their decision-making patterns, preserving decades of tacit knowledge as a digital asset. New hires can access thirty years of operational wisdom immediately, effectively compressing the apprenticeship curve.
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