Building Trust in AI Systems with Scott Robohn

Scott Robohn of Solutional presented on building trust in AI systems at AI Field Day 9, emphasizing that trust must be intentionally built, not merely assumed, with any new technology. He noted that AI is still in a phase of finding its appropriate use cases rather than being a universal solution, despite intense industry pressure for rapid adoption. Robohn’s core message, underscored by musical analogies, was to “slow down” and “be deliberate” in AI implementation, advocating for a cautious approach over hasty deployment.

He outlined a systematic, iterative process for successful AI integration, beginning with investigation and experimentation to explore capabilities. This should lead to clearly defining the problem AI is intended to solve, followed by selecting candidate solutions and conducting formal Proof of Concept (POC) processes. The process then moves to limited deployments before scaling to full deployments, with a continuous plan for iteration to adapt to the fast evolving AI landscape. Robohn highlighted his own experience with a Bell Atlantic POC in 1996 as a foundational example of structured evaluation.

Further considerations for building trust include the critical role of telemetry for proof and transparency, particularly for agentic AI, with an initial bias towards comprehensive data collection. Organizations must develop and actively influence their AI policies to guide usage. Implementing robust Role-Based Access Control and Identity and Access Management for AI agents, limiting privileges to only what is necessary, is also vital. Accountability for AI systems must rest with people, at both organizational and operational levels, as machines cannot be held responsible. Financial implications, such as token pricing and the choice between local and hosted models, along with the organization’s approach to open-source or open-weight models, also require careful planning.