Engagement pathways

Organizations deploying AI in high-stakes human systems without a senior safety and governance function of their own.

Standing advisory: a senior partner who knows the deployment, sits with leadership on cadence, and is accountable for the governance posture as the system, the vendor, and the caseload change.

A governance function that exists — decisions get made with the failure modes in the room, before incidents rather than after them.

Leaders who suspect their AI deployment has pathways and pressures nobody has mapped.

A sourced intake and structured diagnosis: map the actual system — models, people, records, and the connections between them — then identify which pathways carry risk, which pressures act on staff, and where correction capacity really sits.

A defensible map of your deployment's shape, with the risk pathways named and prioritized — the document the rest of governance hangs from.

Organizations weighing governance options and wanting them tested before committing budget and policy to one.

PAN-informed stress testing of your governance options against stylized scenarios of your deployment's shape: which levers act on which pathways, what each is likely to change directionally, and what can backfire — with every limitation stated. Illustrative and decision-supportive, never a forecast or a calibration to your named deployment.

An evidence-weighted ranking of your options, iatrogenic costs included — direction and shape you can take into a decision, plus the written boundaries of what the exercise does not establish.

Organizations whose controls were designed once and have been drifting since.

A standing review cadence: revisit the deployment as models update, staff turn over, and caseloads move; recheck that write gates, review steps, and escalation triggers still do what they were installed to do; recalibrate what has drifted.

Governance that tracks the deployment instead of the launch memo — drift caught as a correction, not as an incident.

Boards, executive teams, agencies, and professional bodies that need a shared working model of AI risk in human systems.

Talks and working sessions built on the Center's material: the documented case histories, the error-propagation model, and the governance patterns — tuned to the audience's domain and delivered plainly.

A leadership group with a common vocabulary for the risks and the levers — able to interrogate vendors, read incidents, and make governance decisions together.

Public agencies, researchers, and mission-driven organizations working on AI governance in social services and adjacent fields.

Collaboration on the questions this Center exists for: grounded case analysis, governance frameworks for human-services deployments, and research that treats frontline workers and the people they serve as the point, not an externality.

Joint work products — analyses, frameworks, publications — with the same sourcing discipline as everything on this site.