Most orgs can tell how many AI tools they've deployed. Far fewer can tell how those tools are actually being used—or where their people have started relying on AI even for decisions that were never meant to be automated. This creates some critical exposures:
Deloitte refunded the goverment hundreds of thousands of dollars after AI-generated reports scandal. iTutorGroup had to settle after their AI screened out older candidates. Similar seemingly compelling AI solutions have actually cost companies $4.4M on average.
The Milan Olympic Committee had the resources, the expertise, the global spotlight. Still they faced worldwide embarrassment for opening the Games with AI slop. The ceremony ended. The memes didn't. People will remember.
Swedish FinTech Klarna aimed to replace 75% of their customer service agents with AI. After 22% drop in headcount, they learnt that AI wasn't meeting their standards and had to rehire more support stuff, which took additional time and cost them the very efficiency they were chasing.
Even perfectly compliant and perfectly accurate AI can cause problems by leading people to stop developing their own judgement. Eventually, there may be no one left to tell the difference between a good and a bad recommendation or to step in if something goes wrong.
RAD Labs are here to develop a research-backed monitoring engine that identifies which decisions carry real risk, so you can focus resources where they matter. No more blanket precautions, instead you get:
Most organizations know AI creates risk but can't quantify it. RAD monitoring engine puts actual numbers on exposure levels for critical decisions.
Generic monitoring tells you what went wrong. RAD diagnostics tell you why—be it model limitations, human behavior, or contextual factors.
Productive organisations need advice that does not waste their time. Get deployment-ready guidance: what to address first, how to address it, and what risk reduction to expect.
Standard monitoring relies on intuition and experience, but the risks AI poses are unprecedented. Unexpected, emerging, and seemingly minor risks that scale exponentially remain overlooked. RAD approach, grounded in scientifically unique methodology, mitigates that by paying attention to:
Standard tools check AI in isolation. RAD tracks the decision-making system as a whole, measuring both AI and human behaviour to catch collaborative collapse.
Not all decisions carry equal weight. RAD assesses human-AI collaboration across compromising conditions, revealing patterns characteristic of real-world scenarios.
Task-based analysis identifies patterns in how different types of decisions break down. RAD applies these insights to your contexts and delivers valuable recommendations quicker.
Establish a coordinated network aligned on RAD objectives.
Identify gaps most relevant to building a monitoring solution.
Produce early empirical results to guide metric and solution development.
Implement functional monitoring features and test them in a live environment.
Put forward initial evaluations and use ongoing findings to improve the platform.
Expand the platform to cover more institution types using adaptable task types.
Answer more relevant questions to continuously improving the solution and inform safeguards.
Use quantity data from across institution types to develop a formal certification.
Use technical research to inform safeguards development and policy recommendations.