JARVIS FRAMEWORK

The JARVIS framework is a simple but structured set of prompts to help business leaders, product teams, and AI practitioners stress-test the behavioural fit of a proposed AI/Emerging Tech system before it moves from pilot to production. Each part of the framework draws on insights from behavioural economics, decision psychology, sociology of technology, and philosophy of agency. The goal is to support better conversations about when and how AI tools work—not just technically, but organisationally and ethically. The name “JARVIS” began as a nod to Iron Man’s AI assistant—an intelligent system that enhances, rather than replaces, human capability. In the Marvel universe, J.A.R.V.I.S. doesn’t act alone; it augments Tony Stark’s judgment, filters complexity, and operates within clearly defined constraints. That relationship—collaborative, contextual, and responsibility-aware—inspired the early thinking behind this framework. If we want AI in the real world to be more than just an automation engine, we need design principles that treat it as a partner in decision-making, not a black box. The acronym that followed just made it easier to remember.

 

The JARVIS Framework emerged from a series of difficult conversations—often behind closed doors—where technically sound AI tools failed to gain traction. These weren’t failures of logic or performance, but failures of alignment: between system outputs and human expectations, between automation and organisational roles, between insight and action. I began assembling the framework not as a grand theory, but as a practical response to a simple, stubborn question: Why don’t people use the intelligent systems they’ve asked for? Over time, those informal diagnostics turned into a structured behavioural lens—one that could be applied early enough to catch the friction before it spreads.