Priyachandar P, Country Head & Director, NI India, Emerson in an interaction with Janifha Evangeline. X, Editor, Asia Manufacturing Review shared insights on how Artificial Intelligence is transforming Testing, Inspection, and Quality Assurance across India’s manufacturing sector, whether AI-powered testing systems can help Indian manufacturers reduce defects, downtime, and production costs more effectively, and the role predictive testing and real-time quality monitoring will play in the future of smart manufacturing in India, and more.
How is Artificial Intelligence transforming Testing, Inspection, and Quality Assurance across India's Manufacturing Sector?
AI is changing how engineers work with test data. Instead of reviewing individual results, they can identify patterns across large volumes of measurement data, investigate anomalies and access relevant engineering knowledge more quickly.
As products become more connected and software-driven, test engineering workflows must keep pace with growing complexity. AI can help engineers spend less time searching for information and more time solving technical problems.
AI supports engineering expertise rather than replacing it. The goal is to reduce friction in increasingly complex test workflows while preserving the rigor, accountability and judgement that validation, verification and production testing require.
Can AI-Powered testing systems help Indian Manufacturers reduce defects, downtime, and production costs more effectively?
Yes, particularly when AI is combined with automated test systems and structured engineering data. One of the biggest opportunities is earlier detection of abnormal behavior before it develops into a wider production issue or lengthy troubleshooting cycle. AI can also help engineers identify patterns across large volumes of test results that would be difficult to analyze manually.
Combined with automation, this can improve test consistency, reduce debugging effort and shorten validation cycles. These improvements depend on reliable measurements, effective test processes and the ability of engineering teams to act on the insights generated. For manufacturers operating at increasing scale and under cost pressure, this can improve engineering efficiency and help lower the overall cost of quality.
What role will predictive testing and real-time quality monitoring play in the future of Smart Manufacturing in India?
Predictive testing and real-time quality monitoring can help manufacturers move from reviewing results after a failure towards identifying emerging issues sooner. As connected manufacturing systems generate more data, the challenge is no longer simply collecting information, but determining which signals require attention and what action to take.
By combining reliable test measurements with system data and analytics, engineers can identify trends earlier, investigate deviations and respond before small issues become larger problems.
As smart manufacturing continues to expand, manufacturers are also placing greater emphasis on standardizing test systems and methodologies across teams and sites. Consistent test processes and comparable data are prerequisites for meaningful analytics and effective AI applications. Success will depend not only on AI, but also on trustworthy measurements, connected engineering data and a clear process for turning insights into engineering action.
Also read: How AI is Transforming Manufacturing Faster Than Ever
How are AI-Driven Test Technologies improving product reliability, Compliance, and Global Competitiveness for Indian Manufacturers?
Product reliability starts with rigorous testing and repeatable processes. AI can help engineers analyze results faster; identify unusual behavior and direct attention to measurements that warrant closer investigation. This can accelerate learning and troubleshooting while allowing engineers to focus on the decisions that ultimately determine product performance.
Compliance depends on documented test procedures, traceability and auditable test records. AI can help engineers find and interpret growing volumes of validation data more efficiently, while established test processes and controls remain the foundation for compliance. As standards and regulatory requirements evolve, manufacturers need the flexibility to adapt test coverage without compromising traceability or consistency.
An open, software-defined approach can make it easier to adapt test systems, integrate engineering data and introduce new analysis capabilities as requirements change. Manufacturers that combine strong engineering practices with more effective use of their data will be better positioned to improve quality, respond to evolving requirements and compete in global markets.
Will AI redefine traditional testing jobs, and what new skills will manufacturing professionals need to stay relevant?
AI is a powerful accelerator, but engineers remain responsible for critical decisions. Testing still requires domain knowledge, sound judgement and accountability. AI can automate repetitive activities, accelerate access to information and reduce the effort required for routine development and analysis tasks, allowing engineers to spend more time investigating problems and making technical decisions.
Future test engineers will need a broader combination of skills spanning software, automation, data analysis and traditional test and measurement expertise. They will also need to assess AI-generated guidance, verify results and ensure that engineering decisions remain grounded in trustworthy data. The most successful professionals will be those who combine engineering knowledge with modern digital tools while maintaining responsibility for the final outcome.
How are automotive, electronics, aerospace, and industrial manufacturers leveraging AI to accelerate testing and product validation?
Across these industries, AI is increasingly being used to help engineers navigate complex test environments, analyze growing datasets, investigate anomalies and accelerate debugging and root-cause analysis.
The application differs by industry. Automotive engineers are validating software-defined vehicles, ADAS technologies and increasingly complex power electronics. Semiconductor and electronics teams must interpret large volumes of characterization and validation data, while aerospace programs require rigorous traceability across long and complex test cycles. Industrial manufacturers can use test and equipment data to investigate inconsistent performance and recurring faults across production environments.
In each case, the effectiveness of AI depends on the quality and consistency of the underlying test data. The greatest value comes from AI that understands the test and measurement context and works within established engineering workflows, rather than from generic AI tools operating without that domain knowledge. Reliable measurements, consistent data and human validation remain essential to achieving trustworthy results.
Also read: How Industrial AI Transforms Manufacturing
Is AI-Powered testing becoming a strategic advantage for Indian manufacturers seeking faster innovation and growth?
Yes, provided it is built on a strong test foundation. As products become more intelligent, connected and software-driven, testing is becoming a strategic capability throughout development rather than simply a final validation step. Manufacturers that can turn trustworthy test data into actionable engineering insight will be better positioned to resolve issues sooner, accelerate development and bring products to market with greater confidence.
This is particularly relevant in India as investment in semiconductors, electronics, mobility and advanced manufacturing continues to grow. The advantage comes from combining AI with an open, software-defined test platform, connected engineering data and strong engineering expertise.
Reliable measurements and repeatable processes provide the foundation. AI can then help engineers analyze results more efficiently, identify issues earlier and make better-informed decisions. Manufacturers that can operationalize trustworthy test data across the product lifecycle will be better equipped to scale innovation while maintaining quality, compliance and global competitiveness.