Manufacturers continue to face significant challenges. Rising raw material and component costs due to new trade policies and tariffs, coupled with a tight global talent pool, are putting pressure on productivity and profits. According to the US National Association of Manufacturers (NAM), 60% of manufacturers say attracting and retaining employees is their top challenge^1. The need for agility has never been greater.

Emergence of AI Agents
This is accelerating manufacturers' adoption of AI agents in their drive toward more autonomous operations. Powered by a unified IT/OT data estate, AI agents enable manufacturers to produce superior products faster with less waste, driving cost and sustainability advantages. They help frontline workers anticipate and address issues across the design and engineering, procurement, production, and field services value chain, reducing errors and accelerating innovation and time-to-market. 

For example:

1. Retrieval agents: Follow specific instructions and rules to retrieve and summarize information and answer questions.
2. Task agents: Automate repetitive tasks within workflows using rules-based automation and predefined knowledge and skills.
3. Autonomous agents: Dynamically plan, adapt to changing conditions, and learn without constant human intervention.

Retrieval Agents: Knowledge Capture and Employee Support
At DENSO Corporation, Avanade helped globally distributed product design and engineering teams use generative AI to access R&D intelligence from a diversity of product lines.

Meanwhile, a global construction material innovator enhanced R&D processes using an AI agent. Using hybrid keyword and semantic search and document ranking algorithms on scientific papers, we boosted answer accuracy. Researchers can now quickly access the collective intelligence of their entire global team using the chatbot.

Additionally, a wind farm and power generation technology leader is working with Avanade to enable retiring workers to share best practices with the next generation of apprentices entering the workforce. Engineers video their work with mixed reality headsets, transforming audio and video data into succinct guides. This improves new employee onboarding, reduces downtime, and provides scalable workflow guidance.

Task Agents: Factory Operations
Combining insights from multiple software systems across manufacturers’ go-to-market value chain is now possible. Avanade maps data previously locked in siloed ERP (Enterprise Resource Planning), MES (Manufacturing Execution System), and CMS (Condition Monitoring System) applications using industry standards to power the next generation of AI agents.

Bridgestone is partnering with Avanade to confront production challenges head-on, focusing on critical issues related to production disruptions and scheduling inefficiencies, such as yield loss, which can escalate into quality issues.

At Schaeffler, employees can use an AI agent to easily access metrics such as scrap rates, yields (the proportion of usable or acceptable components), and energy usage over time. Previously, engineers might spend hours identifying detailed downtime reasons. Now, they can simply ask the agent, “What caused the downtime on Line 3 yesterday?” and receive an instant, detailed response.

Autonomous Agents: Enhanced Field Service
Multi-agentic AI systems are emerging to manage tasks across complex workflows in manufacturing. These systems can orchestrate the work of multiple agents and initiate actions using RPA (Robotic Process Automation). They leverage large and small language models and iterative feedback loops to improve continuously. 

Microsoft Dynamics 365 now includes autonomous agents for sales, service, finance, and supply chain teams. For example, the Customer Knowledge Management Agent can transform field services by ensuring knowledge articles are up-to-date. It analyzes case notes, transcripts, summaries, and other artifacts from human-assisted cases to uncover insights.