In 2002, a product launched amid extraordinary hype. Dean Kamen had been working on it for years. Steve Jobs reportedly said it would be bigger than the PC. The Segway, when it was finally revealed, was a personal electric scooter. By 2020, production had stopped.
In 2013, Google launched Google Glass, the wearable computer that was supposed to transform how people interact with information. By 2015, the consumer version was discontinued. The product had been extensively tested, backed by one of the world's most sophisticated technology companies, and built from genuine technical innovation. It failed because the people who built it did not understand how other people would experience wearing it in public.
The data was not the problem
Both organisations had access to substantial data. Neither lacked analytical capability. The Segway team knew the physics, the battery chemistry, the regulatory landscape. The Google Glass team understood the hardware, the software, the use cases. What they did not understand was the context in which their product would actually live: the urban environments that would not accommodate a personal scooter, the social dynamics that would make wearing a camera on your face feel invasive rather than convenient, the lived reality of the people who were supposed to use what they had built.
Context is not a data problem. Data tells you what has been recorded. Context is the web of lived meaning, social expectation, cultural understanding, and experiential reality within which any innovation must find its place. It lives in people. It requires structured collective deliberation to surface — deliberation that preserves the diversity of perspectives, the connections between them, and the lived understanding that no analytics platform can access.
The gap between a technically excellent innovation and one that finds its place in the world is almost never a technical gap. It is a context gap.
McKinsey's 84/6 problem
The pattern is consistent enough that McKinsey has put numbers to it: 84% of enterprise leaders recognise the importance of innovation, and only 6% are satisfied with their results. The gap between recognition and execution is not primarily a gap of effort or resource. It is a gap of method.
The standard innovation process — workshops, idea management platforms, customer panels, analytical synthesis — is designed to generate a high volume of inputs and converge on the most promising ones. What it systematically cannot do is surface the understanding of the context those inputs will enter. Ideas generated in workshops reflect the frames the workshop facilitator brought to the exercise. Ideas ranked by platform voting reflect the preferences of the people who participated. Neither process produces genuine understanding of the lived human reality that will determine whether the innovation succeeds.
What the societal voice makes possible
The most useful intelligence for innovation is not expert analysis of existing data. It is structured collective deliberation that brings together the perspectives of the people who will live with the innovation — alongside the specialists who will build it, the analysts who understand the market, and the practitioners who have seen similar attempts before.
This is what collective sensemaking infrastructure changes for innovation processes. A SparkMap on a product challenge does not generate a ranked list of ideas. It produces a structured map of how the relevant community understands the problem the innovation is supposed to address — with its lived reality, its tensions, its unexpected connections, and the understanding of context that no workshop or analytics process can produce.
The organisations that build genuine competitive advantage from innovation are not those with the most sophisticated idea platforms. They are those that understand their territory better than their competitors — not just the data about it, but the context: the lived human understanding that determines which innovations will find their place and which will not.
What changes
The sequence changes. Context-building before ideation, not analysis after it. Structured collective deliberation with the people who understand the territory — the societal voice alongside the specialist voice — before the categories are fixed and the brief is written. That understanding becomes the intelligence that makes innovation decisions better: not more certain, but better grounded in the reality the innovation will meet.