2026 Community of Human and Organizational Learning 32nd Annual Conference

Elite athletes visualize failure before it happens, yet most organizations wait for an incident to learn. What if frontline workers were trained like top performers instead of compliance check boxes?


Organizations invest significant time and resources in training yet many struggle to prove how learning translates into real-world performance and measurable risk reduction. Completion rates and certifications may demonstrate participation, but they rarely indicate readiness decision quality or the ability to perform under pressure.


High-performing athletes do not rely on repetition alone. They use visualization to mentally rehearse high-risk scenarios anticipate mistakes and execute effectively when it matters most. This session challenges leaders to apply the same performance principle to frontline work where the cost of failure includes injuries downtime regulatory exposure and reputational risk.


The session explores how organizations can move beyond compliance-based learning by connecting training visualization and operational data. By linking learning programs with incidents, near misses, audits, human performance indicators and making risk visible organizations can enable frontline teams to mentally rehearse the scenarios they are most likely to face. This approach transforms training from a static event into a dynamic system that reinforces critical behaviors before incidents occur. Attendees will learn practical executive-relevant approaches for aligning learning investments with real operational risk improving frontline adoption and creating continuous feedback loops between learning EHS and operations. The session highlight show connected systems allow leaders to measure what truly matters preparedness performance and risk reduction.


Key takeaways include:

1. How to shift learning from a compliance requirement to a performance and risk management lever
2. Why visualization is a proven but underutilized capability in frontline safety and operations
3. How to connect training with real-world safety and operational data to drive relevance
4. Strategies for improving frontline engagement without adding burden
5. Metrics that reflect readiness behavior change and impact on risk 

Artificial Intelligence is already drafting risk assessments, analysing incident data, generating safety communications, and automating assurance workflows. But does that trajectory end with AI supporting safety practice—or replacing it? This double breakout session uses a structured, high-energy debate format to test that question in real time, separating hype from operational reality and exposing what “replacement” really means in complex sociotechnical systems.
In the first 50-minute session, participants will nominate as speakers and be randomly allocated to either the Affirmative (“AI will replace the Safety Practitioner”) or the Negative (“AI will not replace the Safety Practitioner”). Teams will rapidly build their case using guided prompts and fast research, while the rest of the room engages in a facilitated, interactive discussion to surface assumptions, evidence, risks, and ethical boundaries. Together we will map the strongest arguments on both sides—covering capability, accountability, regulation, human judgement, leadership, organisational learning, and the messy realities of work-as-done.


In the second 50-minute session, the room becomes a formal debating chamber. Teams deliver timed opening statements, structured rebuttals, and closing arguments, supported by a timekeeper and clear rules of engagement. The audience acts as adjudicators, voting on the winner and sharing the reasoning behind their decision. The facilitator will officiate, synthesise themes, and provide feedback—connecting debate insights to practical implications for the safety profession: what to automate, what to augment, what to protect, and what new capability the next generation of safety practitioners will need.


Participants will leave with a sharper, evidence-informed view of AI’s realistic role in safety—and a clearer line between tasks that can be mechanised and responsibilities that must remain human-led.

Artificial Intelligence is already drafting risk assessments, analysing incident data, generating safety communications, and automating assurance workflows. But does that trajectory end with AI supporting safety practice—or replacing it? This double breakout session uses a structured, high-energy debate format to test that question in real time, separating hype from operational reality and exposing what “replacement” really means in complex sociotechnical systems.
In the first 50-minute session, participants will nominate as speakers and be randomly allocated to either the Affirmative (“AI will replace the Safety Practitioner”) or the Negative (“AI will not replace the Safety Practitioner”). Teams will rapidly build their case using guided prompts and fast research, while the rest of the room engages in a facilitated, interactive discussion to surface assumptions, evidence, risks, and ethical boundaries. Together we will map the strongest arguments on both sides—covering capability, accountability, regulation, human judgement, leadership, organisational learning, and the messy realities of work-as-done.


In the second 50-minute session, the room becomes a formal debating chamber. Teams deliver timed opening statements, structured rebuttals, and closing arguments, supported by a timekeeper and clear rules of engagement. The audience acts as adjudicators, voting on the winner and sharing the reasoning behind their decision. The facilitator will officiate, synthesise themes, and provide feedback—connecting debate insights to practical implications for the safety profession: what to automate, what to augment, what to protect, and what new capability the next generation of safety practitioners will need.


Participants will leave with a sharper, evidence-informed view of AI’s realistic role in safety—and a clearer line between tasks that can be mechanised and responsibilities that must remain human-led.