Nirmalya Kumar, 2012

“There are four types of invisible innovation coming out of India.”

Marketing scholar Nirmalya Kumar used his 2012 TEDxLondonBusinessSchool talk to challenge the belief that India only supplied low-cost labor. He described forms of innovation hidden inside multinational research, outsourced product development, process improvement, and global management roles. The quote highlights a measurement problem: innovation is often counted by consumer brands or visible inventions while knowledge, design, and process advances remain embedded in supply chains. That can distort where leaders believe capability resides. The line is best treated as a hypothesis about how results are created, not as a universal law. The distinction matters because organizations often preserve the visible result while forgetting the less visible habits and relationships that produced it.

Companies can map which locations and partners originate patents, features, cost reductions, quality improvements, and management practices. Credit, career paths, and decision authority should follow the evidence rather than the headquarters address. Teams can translate that hypothesis into a defined behavior and a signal that matters to customers or colleagues. Reviewing the mechanism prevents a favorable outcome from being mistaken for proof that every assumption was correct.

Celebrating distributed innovation must not excuse wage gaps, extractive outsourcing, or the transfer of risk without power. Local teams need ownership, recognition, and a share of the value created, while data should distinguish genuine capability from reclassified routine work. Nuance protects the idea from becoming a justification for speed, pressure, or symbolic compliance. People responsible for execution should help define the measure because they see consequences a dashboard may flatten or miss.

Global collaboration and remote research make innovation increasingly difficult to assign to one place. Kumar’s idea encourages firms to see and invest in the networks already creating value instead of treating them as interchangeable capacity. Review one product’s history and name the people and locations behind its major improvements. Correct missing attribution, identify where decision rights lag expertise, and fund one locally proposed experiment with a clear path to adoption. When results are shared honestly, later decisions can build on evidence instead of authority or memory. The next team can reuse the insight while still testing whether its customers, risks, and operating environment are different. A small experiment is valuable only if it is large enough to reveal real behavior. A demonstration arranged for ideal conditions may prove technical possibility while hiding adoption, support, cost, and fairness. The next test should therefore include ordinary users, normal constraints, and a clear comparison. That produces evidence leaders can use without pretending a controlled pilot already represents the whole organization. Follow-up should include the people who use the process and those who absorb its risks.