Artificial intelligence is quickly moving from pilot projects to core operations in manufacturing. As companies face ongoing supply chain disruption, labor shortages, and margin pressure, AI is emerging as a practical way to improve productivity, efficiency and decision-making at scale.
What is changing now is not just the ability to analyze data. It is the growing ability to act on it.
Adoption moves beyond experimentation
Manufacturers have spent the past few years exploring AI through pilots and point solutions. Today, adoption – and value realization - is accelerating as organizations look for measurable outcomes tied to cost, productivity, and resilience.
AI is increasingly being used to:
- Monitor operations in real time and provide actionable output
- Anticipate supply chain disruptions and provide useable options
- Reduce manual and repetitive work through process automation
- Improve visibility across production and fulfillment to improve decision-making
These capabilities are helping manufacturers respond faster to changing conditions, understand where improvements can be made, and operate more efficiently.
Expanding use cases across operations
AI applications in manufacturing now span the full value chain, from planning through production and delivery. Technologies such as machine learning, process intelligence, generative AI, and now agentic AI tools are being applied to both strategic and day-to-day decisions.
Common use cases include:
- Predictive maintenance to reduce unplanned downtime
- Quality monitoring to identify defects earlier
- Demand and supply planning optimization
- Workflow automation across departments to accelerate outcomes
In many cases, AI is helping organizations move from reactive decision-making to more proactive and predictive approaches.
A shift toward action-oriented AI
A notable trend is the rise of systems that can take action, not just provide data insights or recommendations.
This approach allows AI to trigger workflows, coordinate decisions, and respond to events automatically within defined parameters and humans in the loop. Instead of requiring teams to interpret dashboards, act manually, or make decisions on siloed data, some decisions can be made and then implemented in real time by the system itself.
Examples include:
- Adjusting production schedules in response to delays
- Updating work orders based on changing conditions
- Identifying alternate suppliers when demand shifts
- Initiating supplier communication when risks are detected
This shift is helping reduce operational friction and freeing up employees to focus on higher value work.
Data remains a limiting factor
Despite advances in AI capabilities, data challenges continue to be a barrier for many manufacturers.
AI systems depend on access to consistent, high-quality data across operations. Fragmented systems, incomplete datasets, and lack of integration can limit the effectiveness of even the most advanced tools.
To address this, manufacturers are investing in:
- Integrated data environments across ERP and operational systems
- Data standardization and governance practices
- Improved connectivity between supply chain, production, and planning
Data is critical to AI output, meaning organizations that establish a strong data foundation will be better positioned to scale AI initiatives.
From potential to performance
One of the ongoing challenges for manufacturers is translating AI investment into tangible ROI. While interest remains high, success often depends on how deeply AI is embedded into everyday workflows.
Companies seeing the most impact tend to focus on operational integration rather than standalone deployments.
As adoption matures, the focus is shifting from experimentation to execution and value-based outcomes.
What comes next
AI is expected to play an increasingly central role in manufacturing operations. As capabilities evolve, more organizations are exploring how to move beyond insight and toward systems that can support or automate action.
Many manufacturers are still working to bridge the gap between early AI experimentation and real operational impact. Understanding what separates those seeing results from those still in pilot mode is becoming increasingly important.
Read the full article:
https://www.infor.com/industries/industrial-manufacturing/ai-in-manufacturing
