…by Anurag Sanghai, Director of Technology, Intellicus Technologies
The fourth industrial revolution (Industry 4.0) has ushered in a new era of smart, connected, data-driven manufacturing. Today, IoT sensors, machine logs and enterprise systems generate large volumes of data. Based on this data, AI can detect patterns, predict outcomes, automate repetitive processes and help humans take critical decisions that impact quality, safety, cost and efficiency.
AI adoption in manufacturing is accelerating rapidly. Manufacturers are realizing measurable gains from reduced downtime, increased equipment effectiveness, streamlined supply chains and ultimately better product quality.
Data challenges in smart manufacturing
Beyond automating assembly line tasks, smart factories are converging technology, continuous data streams and real-time analytics to create production systems that are adaptive, self-optimizing and autonomous. AI agents can independently monitor production conditions, equipment performance and process variations. Instead of simply raising alerts for human intervention, they can dynamically adjust parameters and optimize workflows, anticipating deviations and preventing failures.
However, autonomous AI systems demand a strong, trustworthy data foundation. Critical decisions that impact product reliability and workplace safety must be based on data that is complete, accurate, contextual and governed. Moreover, the decisions must be explainable, with clear traceability, as even minor data inaccuracies can cascade into costly operational mistakes and major safety risks. For instance, a faulty insight generated from incorrect sensor data can result in an expensive line stoppage – resulting in production losses and putting workers at risk.
Engineering trusted data for AI success
As per research by Capgemini and Microsoft, only 5% of industrial companies have deployed AI in manufacturing at scale. A key reason is the lack of robust data foundations in this sector. Data remains locked in silos and legacy enterprise systems. Moreover, inaccuracies from sensors operating in harsh industrial environments and manual data entry errors jeopardize the reliability of data captured. These hurdles hold manufacturers back from transitioning to AI-driven operations. Poor quality, siloed data can skew AI model training, hide early signs of equipment failure and generate unreliable recommendations. Developing better algorithms or creating downstream fixes cannot help mitigate these risks.
Instead, checks that infuse confidence in the data that AI uses should be built into the analytics architecture itself. This starts with automated data validation at ingestion—setting up rules that detect anomalies, missing values and out-of-range readings. Flawed data should be stopped at source, instead of letting it propagate through the system.
Additionally, it is critical that secure, standardized data sharing protocols are established across all plants, systems and external partners. Robust practices must be instituted to protect the data in motion across the manufacturing ecosystem. Integrity checks and anomaly detection at various stages should be integrated to strengthen confidence in the data. Unexpected process behavior caused by possible data drift or sensor degradation should be flagged in real time.
Governance: The rulebook for responsible AI
Beyond the technology stack, trust in data-driven decisions requires clearly defined rules and accountability. Robust data governance practices help ensure that AI operates within boundaries that are transparent, ethical and aligned with business expectations.
Effective governance aligns AI and data practices with industry standards and regulatory requirements. It reduces risk and enables confidence with end-to-end data lineage and traceability. Organizations can track where data originates, how it is transformed and how it influences models and decisions. Clear ownership of data assets and AI models reinforces accountability, ensuring that quality, security and performance are actively managed and not implicitly assumed.
The need for human oversight remains an integral part of governance. Vigilance in recognizing and mitigating algorithmic bias is essential. Teams should be trained to understand data ethics, model limitations and the implications of AI-driven decisions—especially in areas impacting worker safety and regulatory compliance.
Leveraging a strong data foundation to deliver real-world impact
Organizations that have invested in strong data foundations are seeing measurable results with AI initiatives moving from experimentation to production. For instance, by unifying and cleansing production data from multiple sources, automakers can leverage AI-driven insights to monitor tool health, pre-empt faults and reduce defect rates significantly.
Similarly, pharmaceutical giants can unify massive volumes of manufacturing data into a trusted data lake. They can leverage near real time insights to accelerate delivery timelines and meet compliance requirements with greater ease.
To ensure long-term success, manufacturers should select specific use cases, start with focused pilots and scale these systematically once they demonstrate business value.
The way forward
According to Deloitte’s 2025 Smart Manufacturing and Operations Survey, 85% of respondents believe their smart manufacturing initiatives will transform how products are made and improve agility. But, true manufacturing intelligence can only be built on a trusted data foundation.
In coming times, real-time data quality monitoring will become the gold standard. Validation and quality checks shall move closer to the source, using edge computing. Errors can thus be detected and corrected before data enters the systems. Use of explainable AI (XAI) will make autonomous decisions transparent and auditable.
Manufacturers who reap the benefits of AI would be those who establish clean, trusted and governed data architectures that feed agents and models with accurate, up to date information. They will be able to scale AI seamlessly and thrive in the era of smart manufacturing.
