In the first session, we shared the HRI framework for mapping organizational AI readiness:<\/strong><\/p>\now to measure digital effectiveness, where adoption barriers lie (governance, skills, digital mindset), and how to move from measurement to a concrete action plan. Participants also experienced our H&AI Impact Assessment firsthand, receiving an individual report on their relationship with AI tools and their digital attitude.<\/p>\n
In the second session, we opened the use case box through three live demos of AI tools applied to HR processes:<\/strong><\/p>\na travel policy assistant, an agent for building development plans from business strategy, and a socratic prompting exercise. For each, we discussed feasibility and implications.<\/p>\n
In the third session, we tackled governance models, presenting an AI Academy framework and a closing activation focused on AI’s impact on the workforce,<\/strong> imagining upskilling and reskilling plans across different time horizons.<\/p>\nThree reflections stood out from the discussions and group exercises across the three sessions.<\/p>\n
The first: AI adoption is an organizational question before it is an individual one.<\/h3>\n
Data from the Microsoft Work Trend Index 2026, showing that organizational factors contribute roughly two and a half times more than individual ones to AI impact, resonated strongly with the group. Without explicit leadership commitment, clear policies, and scaling mechanisms such as AI Champions, individual skills struggle to translate into real adoption. Without a genuinely enabling context, AI adoption remains a promise.<\/p>\n
The second concerns the personal “why” as the engine of adoption.<\/h3>\n
In discussing what had made the difference in their own AI journeys, participants shared that a manager’s push or peer imitation are valid triggers, but not sufficient ones. Adoption consolidates when there is a real problem to solve, or a task where gaining cognitive space for more strategic thinking becomes genuinely valuable. It is in that zone, between organizational mandate and individual motivation, that HR has an important role to play in making AI a concrete ally in everyday work.<\/p>\n
The third, emerging from the workforce impact exercise, is the need to move beyond a role-based lens and adopt a task-based one.<\/h3>\n
Understanding how AI changes work requires disaggregating roles into their component activities, distinguishing what gets automated from what gets redesigned, and holding two variables together that are often treated as one: an organization’s AI adoption maturity and its pace of change. Without this granularity, any reskilling plan risks remaining abstract.<\/p>\n
Behavioral Science in Service of Change<\/strong><\/h2>\nThe Behavioral Change lab was built in layers, with each session adding a new tool to the toolkit.<\/strong> The first session introduced the fundamentals: the ABC model (antecedent, behavior, consequences) and the concept of Target Behavior: the specific, observable, and measurable action that an agent takes or does not take in a given decision-making context.<\/p>\nThe second session introduced cognitive biases and Key Behavioral Indicators (KBIs)<\/strong>, working through a canvas linking target behavior to behavioral indicators. It also featured a deep dive into the technologies that are making behavioral interventions scalable today, anchored by a presentation on Merits delivered by Roberto Sanlorenzo, founder and CEO of the platform.<\/strong><\/p>\nThe third session closed the loop: from the limitations of mainstream change management models to the Head-Heart-Gut method, integrated into the ADKAR framework to produce what we called ADKAR+.<\/strong><\/p>\nHere too, the activations generated the most interesting insights.<\/p>\n
Working in small groups on real change projects, participants found it genuinely difficult to name the expected behavior. Moving from generic formulations\u00a0 as “adopt distributed leadership,” “close commercial meetings more effectively”, to behaviors that are observable, specific, and context-influenced<\/strong> is a cognitive effort that, as several participants pointed out, is rarely completed in most change initiatives.<\/p>\nYet that is precisely where the difference lies between a good intention and a change initiative capable of producing results.<\/p>\n
In the second session, the Target Behavior\u2013KBI canvas brought a second shift into focus: from KPIs to KBIs,<\/strong> from outcomes to the behaviors that predict them. The core insight (that behaviors are the predictors of results, and that acting on KBIs means intervening upstream of what we see in the numbers) allowed groups to return to their own projects with a different analytical lens,<\/strong> particularly on change challenges connected to digital transformation and AI.<\/p>\nThe third session introduced the most significant enrichment: the Head-Heart-Gut method as a tool for diagnosing barriers to change.<\/strong> The method starts from the idea that every decision and action involves three distinct “decision-making centers”: the Head, home of logic, analysis, and forecasting; the Heart, home of emotions, relationships, and a sense of belonging; the Gut, home of instinct, identity, and survival responses.<\/p>\nResistance to change often stems from one or more of these centers being blocked \u2014 and the nature of those blocks varies considerably. By embedding this lens into the five phases of the ADKAR model, we proposed a diagnostic map capable of showing not only where people stall in the change process, but why.<\/p>\n
Several participants recognized in this framework a language they had already been using intuitively \u2014 and, for the first time, a tool to apply it with discipline in their own projects.<\/p>\n
The Takeaways<\/strong><\/h2>\nLooking back at both journeys, what strikes us most is the thread connecting the two labs.<\/p>\n
Despite addressing seemingly different topics, both programs converge on the same core message, expressed in different vocabularies: change is designed by acting on behaviors and the context that enables them, not only on individual motivation or the technology lever.<\/p>\n
This is demonstrated by the fragility of interventions that act on awareness alone.<\/strong> A new technology does not translate into adoption without the organizational conditions to support it: sponsorship, policies, enabling frameworks, and scaling mechanisms. Equally, a change plan does not produce results without careful attention to the behaviors that are meant to bring it to life.<\/p>\nFrom this reading, a coherent role for HR emerges:<\/strong> choice architect on the behavioral front, and workforce transition conductor on the AI front.<\/strong> In both cases, HR’s strategic relevance lies not in the ability to cascade initiatives from the top, but in the capacity to design the contexts and organizational conditions in which people find it natural to adopt certain behaviors.<\/p>\nThe value generated by these two journeys lives, above all, in the people who inhabited them.<\/p>\n
We want to thank all participants for bringing their questions, doubts, experiences, and live projects into the sessions, contributing once again to making the HRI Labs a living space for dialogue and community building among HR professionals.<\/p>\n
A space where the challenges of the profession are faced together, and where conceptual frameworks become real tools only when they pass through the experiences of those who will put them into practice.<\/p>\n","protected":false},"excerpt":{"rendered":"
Change is not managed: it is designed. And AI is not adopted by mandate: it is enabled. The HRI 2026 Labs recap, at the intersection of behavioral science and AI adoption.<\/p>\n","protected":false},"author":5,"featured_media":5022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[32],"tags":[],"class_list":["post-5017","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-events"],"jetpack_featured_media_url":"https:\/\/hrintelligence.it\/wp-content\/uploads\/2026\/06\/ai-adoption-change-management.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hrintelligence.it\/en\/wp-json\/wp\/v2\/posts\/5017","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hrintelligence.it\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hrintelligence.it\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hrintelligence.it\/en\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/hrintelligence.it\/en\/wp-json\/wp\/v2\/comments?post=5017"}],"version-history":[{"count":4,"href":"https:\/\/hrintelligence.it\/en\/wp-json\/wp\/v2\/posts\/5017\/revisions"}],"predecessor-version":[{"id":5024,"href":"https:\/\/hrintelligence.it\/en\/wp-json\/wp\/v2\/posts\/5017\/revisions\/5024"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/hrintelligence.it\/en\/wp-json\/wp\/v2\/media\/5022"}],"wp:attachment":[{"href":"https:\/\/hrintelligence.it\/en\/wp-json\/wp\/v2\/media?parent=5017"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hrintelligence.it\/en\/wp-json\/wp\/v2\/categories?post=5017"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hrintelligence.it\/en\/wp-json\/wp\/v2\/tags?post=5017"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}