Kurt Lewin proposed that behavior is a function of the person and their environment: B=f(P,E). Thomas Gilbert’s Behavior Engineering Model (BEM) operationalized Lewin’s insight by isolating six variables that influence organizational performance:
• Knowledge, Capacity, and Motivation within the Person (25 percent of performance variation)
• Information, Resources, and Incentives within the Environment (75 percent of performance variation)
Gilbert’s work offers a humbling insight for Learning and Development (L&D) professionals: Most performance problems are not learning problems. Only 11 percent of performance discrepancies are attributed to deficiencies in Knowledge and Skills, while Information (35 percent) and Resource (26 percent) deficiencies together account for 61 percent.
WHERE DOES AI FIT IN?
Artificial intelligence (AI) can transform data into contextualized information and deliver it precisely when and where needed. Rather than pulling people away from work for instruction, AI can provide task-based instructions within the flow of work. Agents can augment human capacity in virtual and physical contexts. And by eliminating bureaucratic redundancy and drudgery, AI can free people to apply their curiosity, experience, intuition, and ingenuity to intrinsically motivating work.
In short, predictive AI knows. Generative AI creates. Agentic AI acts. Physical AI delivers. That means AI does not fit neatly into the Environment side of BEM. Its affordances traverse Lewin’s equation, simultaneously transforming the work environment and augmenting the individual.
The AI era demands that we reimagine work, worker, workplace, and workflow from the ground up. What work needs doing? Which entity—human and/or machine—is best equipped to do it? Where should it occur as workplaces span physical and virtual domains? How should workflows be redesigned when sensing, creating, deciding, acting, and doing can be dynamically distributed between carbon and silicon?
START WITH 6 QUESTIONS
Human Performance Technology offers a methodology for redesigning organizational performance around a different unit of performance: the Humachine. Gilbert’s BEM gives us six questions to start this redesign journey: How are you leveraging AI to…
1. Turn data into contextualized insight that improves decisions? (Information)
2. Put intelligent tools and agents directly into workflows? (Resources)
3. Make goals, feedback, and consequences visible in prioritizing work activity? (Incentives)
4. Replace instruction before work with instructions within work? (Knowledge)
5. Augment what people can accomplish cognitively and physically with workflow agents? (Capacity)
6. Eliminate drudgery and unleash human curiosity, ingenuity, and purpose? (Motivation)
The opportunity to reimagine organizational performance extends beyond productivity and profitability to human, organizational, and ecosystem flourishing. Are you ready to stop fitting AI into existing work activity and start redesigning work around what humans and machines can uniquely do best?