Gartner® Research
AI is showing up across manufacturing operations, but not every use case offers the same combination of business value and feasibility.
19
AI use cases assessed
8
Likely wins
8
Calculated risks
3
Marginal gains
19
AI use cases assessed
8
Likely wins
8
Calculated risks
3
Marginal gains
Making sense of manufacturing AI
Manufacturing leaders are being presented with AI applications across maintenance, quality, production, planning, workforce management and other operational areas.
The challenge is moving beyond “Where can we use AI?” toward more useful questions:
Gartner® provides actionable, objective insights on your mission-critical priorities. That features proprietary insights, deep coverage, industry knowledge, human intelligence, proven tools and benchmarks as well as world-class conferences and peer community.
Download the ReportLikely wins
Use cases that combine stronger potential value with greater feasibility.
Calculated risks
Opportunities that may offer meaningful value but come with greater implementation complexity or uncertainty.
Marginal gains
Applications where potential impact may be more limited.
Gartner evaluates 19 AI use cases across manufacturing operations based on their potential value and feasibility.
1. See the landscape
Understand the AI opportunity landscape
See how different manufacturing AI applications compare rather than evaluating each opportunity in isolation.
Separate potential from practicality
Consider both potential business value and the feasibility of implementing a use case in real manufacturing environments.
2. Decide where to act
Prioritize investment
Give operations and technology leaders a framework for deciding where AI investment may deserve attention first.
Move from experimentation to operational value
Use the assessment as a starting point for conversations about which AI initiatives can progress beyond pilots and into day-to-day operations.
1. See the landscape
Understand the AI opportunity landscape
See how different manufacturing AI applications compare rather than evaluating each opportunity in isolation.
Separate potential from practicality
Consider both potential business value and the feasibility of implementing a use case in real manufacturing environments.
2. Decide where to act
Prioritize investment
Give operations and technology leaders a framework for deciding where AI investment may deserve attention first.
Move from experimentation to operational value
Use the assessment as a starting point for conversations about which AI initiatives can progress beyond pilots and into day-to-day operations.
Explore the manufacturing AI use cases and framework for evaluating where AI can deliver operational value.
Tractian
Identifying valuable AI use cases is only the beginning. In maintenance and reliability, the opportunity becomes tangible when teams can use AI to understand asset condition, identify developing problems and take action before those problems become downtime.
Tractian brings mechanical, electrical and operational asset data together to help maintenance and operations teams move from signals to decisions.