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At the Manufacturing Innovation Conclave during ARC Advisory Group’s 24th India Forum in Bengaluru, held July 9–10, 2026, Harish P., Product Management Lead, and Abhishek Wani, Technical Team Lead at AmiT, presented “AI Adoption in Discrete Manufacturing: An Indian Context.” AmiT is the digital manufacturing business of the Ace Micromatic Group.
The session examined the realities of AI adoption across India’s discrete manufacturing sector, with a focus on digital maturity, connected factory data, traceability, and operational readiness. Rather than treating AI as a standalone technology, the speakers discussed how manufacturers can build a stronger digital foundation through integrated workflows, digital threads, and intelligent manufacturing systems. This can help organizations progress from traditional operations toward AI-enabled decision-making and more autonomous manufacturing.
Their presentation can be viewed on YouTube or here:
Beyond the AI Hype
Across India’s manufacturing sector, AI is increasingly viewed as a tool for improving productivity, quality, and operational efficiency. However, many organizations still face a fundamental challenge: data exists, but it is fragmented.
Production data may reside in one system, maintenance records in another, quality information in spreadsheets, and planning knowledge in the minds of experienced employees. While individual departments may be digitally equipped, factories often operate as disconnected units.
This creates what the speakers described as “smart departments, not smart factories.” The result is delayed decision-making, limited visibility, and heavy dependence on manual coordination. AI cannot create meaningful outcomes in such an environment unless the underlying systems are connected.
The Need for a Manufacturing Operating System
To overcome these challenges, the speakers introduced the concept of a Manufacturing Operating System, or Manufacturing OS.
A Manufacturing OS acts as an orchestration layer that connects:
Machines.
Operators.
Production processes.
Quality systems.
Maintenance activities.
Enterprise applications.
AI and analytics.
The Manufacturing OS provides an orchestration layer between people and machines, creating a single source of truth for factory operations
Rather than creating another software platform, the goal is to build a unified operational environment in which data can move across departments.
The framework follows a clear hierarchy:
Machines and people create value.
Data captures operational activity.
Workflows provide context.
Applications enable execution.
AI generates insights.
Business intelligence supports decisions.
This model reinforces a critical point: AI is not the foundation of digital manufacturing. It is a layer that sits on top of strong operational processes and reliable data.
Machines and People Remain the Foundation
One of the strongest messages from the session was that value is created on the shop floor, not by AI systems.
Machines manufacture products, while people operate equipment, maintain quality, and solve operational problems. AI can assist with these activities, but it cannot compensate for poor processes or unreliable information.
Manufacturers often expect AI to solve operational inefficiencies automatically. In reality, AI amplifies existing capabilities. If workflows are not standardized or data is incomplete, even advanced AI systems will deliver limited value.
The foundation for AI success remains operational excellence.
Building an AI-Ready Data Layer
The speakers repeatedly stressed the importance of AI-ready data.
A modern factory generates information from hundreds of touchpoints, including production equipment, quality stations, maintenance activities, warehouses, and operators. However, collecting data is only the first step. To unlock its value, organizations must ensure that data is accurate, consistent, traceable, enriched with the appropriate business context, and accessible across functions.
For many manufacturers, improving data visibility and consolidating operational information across systems can address a significant share of business challenges. These improvements can deliver meaningful benefits before advanced AI initiatives are introduced.
Creating Digital Threads and Traceability
Traceability has become a business necessity in modern manufacturing. Companies increasingly need to know:
Which machine produced a component.
Which operator performed the task.
Which process was followed.
Which inspections were completed.
Which batch or product was affected.
To achieve this, the speakers highlighted the importance of creating digital threads that connect information across three dimensions:
Part.
Process.
Product.
This structure provides both forward and backward traceability, enabling faster root-cause analysis, stronger compliance, and better quality control.
Digitizing Engineering Information
Manufacturing begins with engineering drawings, yet much of this information continues to be consumed manually.
The presentation highlighted how AI can accelerate drawing digitization by extracting dimensions, inspection characteristics, and manufacturing requirements automatically. Information that traditionally required hours of manual effort can be converted into structured digital data within minutes.
More importantly, this information can be connected directly to quality plans, inspection workflows, and shop-floor operations, reducing delays between design and production.
Extracting Value from Legacy Machines
A particularly relevant topic for Indian manufacturers was the digitization of legacy equipment.
Many factories continue to rely on machines that are 10 to 20 years old. These assets remain critical to production but often lack modern connectivity features. Replacing every machine is neither practical nor economical.
Instead, sensors, gateways, and communication interfaces can capture operational data from older equipment and integrate it into the broader digital ecosystem. This approach allows manufacturers to modernize operations without replacing productive assets that continue to generate business value.
Practical AI Applications
Once a strong digital foundation is established, AI can be applied across several high-impact areas.
Predictive Maintenance
By analyzing machine behavior, operating conditions, and historical failure patterns, AI can identify potential equipment issues before breakdowns occur.
Potential benefits include:
Reduced downtime.
Improved reliability.
Better maintenance planning.
Extended asset life.
Intelligent Scheduling
Production scheduling is one of the most complex challenges in discrete manufacturing. AI-driven scheduling systems can evaluate machine availability, due dates, capacity constraints, and production priorities simultaneously. This enables manufacturers to optimize throughput and respond more effectively to changing customer demand.
Computer Vision
Computer vision serves as the visual intelligence layer of the factory. Applications include:
Part counting.
Product validation.
Safety monitoring.
Intrusion detection.
Presence verification.
These solutions can improve visibility while reducing dependence on manual monitoring.
Agentic AI and Decision Support
The presentation also explored the emergence of agentic AI, a new generation of intelligent systems designed to support operational decisions.
Unlike traditional dashboards, agentic systems can analyze historical service records, maintenance logs, root-cause analyses, and real-time production information to answer questions directly.
For example, an AI agent can:
Identify why a machine stopped.
Recommend corrective actions.
Suggest preventive measures.
Assist operators in troubleshooting.
This shifts manufacturing systems from information providers to decision-support partners.
The Most Important Question: Is Your Factory Ready?
The central takeaway from the session was that AI adoption is ultimately a question of digital maturity.
A large enterprise may still be struggling with disconnected systems, while a smaller manufacturer may already have a highly integrated digital environment. Company size and revenue do not determine AI readiness.
Manufacturers should first evaluate:
Data quality.
Process maturity.
Traceability.
System integration.
Workforce readiness.
The question is not simply, “How do we implement AI?” The more important question is, “Is our factory ready for AI?”
Building the Foundation for AI-Ready Manufacturing
Harish and Abhishek’s presentation underscored that successful AI adoption in manufacturing depends on more than technology alone. By strengthening connectivity, traceability, data quality, and operational maturity, manufacturers can create the foundation needed to scale AI effectively and support more intelligent, autonomous operations.