Assess and Prioritize Agentic AI Use Cases in Manufacturing

Manufacturers are being pushed toward autonomous outcomes without the operational foundations to support them. Most manufacturing environments remain fragmented across systems and processes.

Agentic AI is being evaluated as a capability, when it should be evaluated at the level of decisions. Current discourse treats agentic AI as something to deploy across functions, rather than something to apply selectively based on the nature of specific decisions.

The value of agentic AI is easy to demonstrate in isolation but difficult to prove at the system level. This makes it difficult for CIOs to translate promising pilots into board-level business cases, as value depends not only on model performance but also on the coherence of the entire operating system.

Our Advice

Critical Insight

CIOs should treat agentic AI as a decision-rights problem, explicitly defining where machines can act and where humans must retain control, and enforcing those boundaries before scaling autonomy across manufacturing.

Impact and Result

Start where decisions are structured and operationally contained before expanding autonomy. Agentic AI should not be introduced where decisions are complex, high-risk, or deeply interdependent across the value chain.
Measure success based on decision reliability under variability.
Strengthen the decision execution layer so that insights can translate into coordinated action. Improving data quality alone is insufficient if decisions cannot be executed consistently across systems.
Define control boundaries explicitly before allowing agents to operate within workflows.

Assess and Prioritize Agentic AI Use Cases in Manufacturing Research & Tools

1. Assess and Prioritize Agentic AI in Manufacturing – A step-by-step document that helps CIOs and manufacturing leaders understand where agentic AI can unlock new operating capabilities.

Identify high-value opportunities for agentic AI by connecting manufacturing business priorities to the operational capabilities required for autonomy. To move from AI experimentation to responsible agentic AI adoption, manufacturers must understand where agents can create meaningful value, what decisions they can safely support or execute, and what data, process, integration, and governance foundations are needed before autonomy can scale.

This storyboard will help you understand the capabilities and opportunities of agentic AI in manufacturing, assess where agents can sense, decide, coordinate, and act across Plan, Source, Make, and Deliver operations, evaluate use cases against business value and operational readiness, and build a practical roadmap for responsible adoption. It will also help you define the controls, decision rights, and accountability structures required to ensure agentic AI improves performance without introducing unacceptable risk.

2. Agentic AI Use Case Tool for Manufacturing – A structured tool to help manufacturing leaders prioritize agentic AI opportunities and build a roadmap for responsible adoption.

This tool guides manufacturing leaders through the evaluation and prioritization activities required to build a practical agentic AI adoption roadmap. This Excel workbook helps you connect business goals to agentic AI opportunities, assess where autonomous or semi-autonomous capabilities can create measurable value, and determine which use cases are most appropriate to pursue first across Plan, Source, Make, and Deliver operations.

Embed decision intelligence into systems to drive real-time, closed-loop execution.

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We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.

What Is a Blueprint?

A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.

Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.

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Guided Implementation 1: Establish familiarity

Call 1: Review the capability map/value stream map.
Call 2: Identify domains where agentic AI may create value.

Guided Implementation 2: Understand criteria

Call 1: Understand agentic AI use cases across value stream domains.
Call 2: Define the decision boundaries and expected operational outcomes.

Guided Implementation 3: Evaluate use cases

Call 1: Evaluate fitment & risk scenarios.

Guided Implementation 4: Define value

Call 1: Estimate value for each use case using metrics.

Guided Implementation 5: Prioritize deployment

Call 1: Prioritize use cases by balancing value, risk, and readiness.
Call 2: Develop a sequenced adoption roadmap.

Author

Shreyas Shukla

Contributors

Chulanga Perera, Head of IT, Global CPG Company
Kartik Ramdoss, Customer Success Manager, Global SaaS Company
Steven Schmidt, Sr. Managing Partner, Info-Tech Research Group
Craig Broussard, Senior Executive Counselor, Info-Tech Research Group
2 anonymous contributors