From Brownfield to Agentic: Retrofitting Brownfield Plants with an Accuracy & Trust Layer for Trusted Action | Adlib x IIoT World x Siemens
Move beyond “AI chat” toward operational AI, by turning CAD, SOPs, logs, and compliance docs into validated, AI-ready inputs your plant can defend.
Why smart factory AI stalls in the real plant
Manufacturers are eager to put AI to work, but the highest-value operational context still lives outside core systems. In most brownfield environments, “truth” is buried in CAD drawings, SOPs, inspection reports, maintenance logs, and compliance records, formats that are hard for AI to interpret reliably.
That gap creates a familiar cycle: pilots look promising, then stall at scale when teams realize the inputs aren’t trustworthy enough to drive action. And when unverified documents are pushed into AI anyway, the risk isn’t theoretical: inaccurate outputs can trigger rework, exceptions, and governance concerns.
What this session is about
The industry is shifting from AI that answers questions to AI that can take action, coordinating decisions across maintenance, quality, and production workflows. But autonomy on the shop floor only works when it’s grounded in verified, high-fidelity data.
This session blends two realities:
- Brownfield modernization is the norm, not the exception.
- Agentic workflows raise the bar for accuracy, traceability, and control.
You’ll see a practical blueprint for building an upstream Document Accuracy & Trust Layer, the control point that converts document chaos into trusted, machine-navigable inputs that can safely feed AI agents and enterprise systems.
Why this matters
If your AI program depends on unstructured plant documentation, “good enough” inputs become expensive fast:
- More manual validation downstream
- More model tuning and remediation
- More exceptions and operational friction
- More scrutiny from quality, audit, and compliance stakeholders
Cleaning and validating documents upstream reduces downstream churn, so AI initiatives deliver outcomes faster and with less risk.
Key takeaways
Walk away with an actionable approach to:
- Unlock brownfield “dark data” by converting legacy documentation into usable, AI-ready inputs (CAD/SOP/logs).
- Put trust controls in front of agents and systems using measurable governance (accuracy scoring, thresholds, routing, and expert review) before outputs impact operations.
- Maintain an audit-ready trail: what was processed, how it was processed, what rules were applied, and what required human validation.
- Feed high-fidelity outputs into PLM/MES/ERP and AI workflows, without brittle, one-off integration projects.
Who should attend
- VP/Director of Digital Manufacturing / Smart Factory leaders
- OT/IT leaders responsible for modernization in brownfield plants
- Quality, compliance, and governance stakeholders supporting AI programs
- Manufacturing data/platform teams feeding PLM/MES/ERP and analytics
Session outline
- Why “agentic” fails in the real plant: autonomy without verified inputs becomes operational risk
- Brownfield reality check: where the most important context lives (and why AI can’t use it yet)
- The Document Accuracy Layer: the upstream control point that makes outcomes verifiable
- Trust controls that scale: scoring, thresholds, routing, and human-in-the-loop review
- Where it lands: PLM/MES/ERP + AI workflows grounded in evidence-grade inputs
Our Speakers
Anthony Vigliotti
Anthony Vigliotti is Chief Product Officer at Adlib Software with 20+ years in business workflow and intelligent document processing. He started in manufacturing at Xerox, designing components for toner-cartridge remanufacture and earning a patent in the process. Seeing how outdated document processes slowed operations, he pushed for new automation approaches—momentum that led Xerox to sponsor his return to university for a master’s in Information Technology. Since then, Anthony has led product management, alliance/partner strategy, and product development across Xerox, Notable Solutions (NSi), Nuance, and Kofax. His work centers on turning unstructured content into reliable, compliant, AI-ready data that accelerates real-world outcomes in regulated industries. Anthony holds a B.S. in Mechanical Engineering and an M.S. in Information Technology, both from the Rochester Institute of Technology.
Chris Huff
Chris Huff is a growth-focused tech and SaaS executive with a proven track record in scaling enterprise software companies backed by private equity and venture capital. He’s known for building strong teams and driving value through operational efficiency, revenue growth, and product innovation. Prior to joining Adlib, Chris was CEO at Base64.ai and Chief Strategy & Growth Officer at Tungsten Automation (formerly Kofax), where he led strategy, product, AI, GTM, marketing, and partnerships. He also co-led Deloitte’s U.S. Public Sector Intelligent Automation practice and served as a Major in the U.S. Marine Corps. Chris brings deep expertise in AI, automation, and digital transformation—and a clear vision to expand Adlib’s impact across regulated industries.
Mathias Oppelt
Mathias Oppelt leads customer- and startup-driven innovation at Siemens, where he builds and scales new industrial businesses by connecting ventures, technologies, and enterprise use cases. With 17+ years of hands-on experience across engineering, product management, R&D strategy, and global business leadership, he has built and led international teams, launched digital and software-driven offerings, and driven measurable outcomes in complex industrial environments.
