The two conflicting mandates that hindered the project discussed in the case study were:
In the multi-agent field service example, the specific simple task successfully offloaded to an AI agent was reading the temperature on a machine and providing a response. This was further augmented by a diagnostic agent which used standard operating procedures to aid the human agent.
The transcript states that the team pivoted for a few reasons, one being a focus on applying a similar idea to a use case with more narrowly defined data needs that is much more repeatable and predictable. The transcript does not provide further details on other reasons for the pivot.
This video features C. Merrell Stone discussing human-AI collaboration, emphasizing that effective integration isn't straightforward. Stone presents a case study illustrating challenges and proposes a framework for making better decisions about building and integrating AI systems into human work systems. The core idea revolves around differentiating between "simple" and "complex" work to determine the appropriate application of AI agents.
According to C. Merrell Stone's framework, simple work is characterized by being repeated, well-defined, and having predictable patterned inputs and/or outputs. Complex work, in contrast, is contextual, dependent on the situation, often creative, unique, and requires decision-making, problem-solving, or the creation of new things. Stone emphasizes that "simple" doesn't mean "easy," merely predictable, and "complex" doesn't mean unpredictable, just sensitive to initial conditions and not always having a clear outcome.