Technology Quotient began as a question that kept appearing in the same form across a decade and a half of enterprise AI work: why do identical technology investments produce such radically different outcomes?
The answer was never the technology itself. It was always something upstream — in how people relate to change, how organizations are structured to absorb it, or how societies create the conditions for it to take root. TQ is the name for that upstream variable.
This research program has three aims. First: to build a rigorous, measurable definition of technology quotient that applies at the individual, organizational, and societal levels. Second: to develop a scoring methodology that makes TQ actionable — not a vague aspiration, but a set of specific dimensions with specific bottlenecks and specific paths to improvement. Third: to contribute to the public understanding of why technology adoption fails, and what can be done about it.
The Annual TQ Index is the program's primary empirical output — a cross-industry benchmarking study that will track TQ at scale over time. Participants receive the full findings when published.
Building at the intersection of AI, enterprise technology, and organizational capability. The Technology Quotient framework grows from 15+ years of direct experience with why technology adoption succeeds and fails in practice — across industries, org sizes, and geographies.