This year, between April and June, we brought our community two three-session labs, each dedicated to a challenge that sits at the heart of today’s HR agenda.
On one side: moving AI from experimentation to widespread adoption. On the other: managing organizational change that has become a structural condition rather than an exception.
This is how the two HRI 2026 Labs came to life: HR as protagonists of AI Adoption and Behavioral Science in Service of Change , designed for the HR professionals in our community.
The two challenges at the center of our Labs represent two pressing themes on the HR agenda, but for different reasons.
On the AI front, the question is no longer whether to adopt the technology. It’s how to turn AI investments into real impact: widespread use, redesigned processes and workflows, value generated in a sustainable way. This is the step that separates experimentation from adoption, and it’s precisely here that HR is being called to play a new role, still taking shape.
According to the latest ISTAT report on Business and ICT (December 2025), while large Italian companies are broadly aligned with the European average on AI adoption (53% vs. 55%), SMEs and mid-sized firms lag significantly behind, one more reason to address the topic within our community, where barriers and accelerators coexist depending on context.
On the organizational change front, the scenario is one of transformation that has shifted from an occasional exception to a structural condition. Market data confirms this: according to Gartner, the average number of planned changes per year rose from 2 in 2016 to 10 in 2022.
Yet classical change management models are showing their limits in the face of this new reality. Boston Consulting Group estimates that 75% of transformation programs fail to generate long-term value. And when we asked our community last year how effective their change management practices were, the response confirmed the paradox: widespread recognition of strategic relevance, alongside practices still perceived as largely ineffective.
It is on this gap between awareness and the capacity to act that we wanted to build a peer-to-peer space, grounded not in top-down content delivery but in shared experiences and actionable takeaways.
Here is what emerged.
HR as Protagonists of AI Adoption
The AI Adoption lab was designed in three stages, each with a different focus.
In the first session, we shared the HRI framework for mapping organizational AI readiness:
ow 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.
In the second session, we opened the use case box through three live demos of AI tools applied to HR processes:
a travel policy assistant, an agent for building development plans from business strategy, and a socratic prompting exercise. For each, we discussed feasibility and implications.
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, imagining upskilling and reskilling plans across different time horizons.
Three reflections stood out from the discussions and group exercises across the three sessions.
The first: AI adoption is an organizational question before it is an individual one.
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.
The second concerns the personal “why” as the engine of adoption.
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.
The third, emerging from the workforce impact exercise, is the need to move beyond a role-based lens and adopt a task-based one.
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.
Behavioral Science in Service of Change
The Behavioral Change lab was built in layers, with each session adding a new tool to the toolkit. 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.
The second session introduced cognitive biases and Key Behavioral Indicators (KBIs), 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.
The 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+.
Here too, the activations generated the most interesting insights.
Working in small groups on real change projects, participants found it genuinely difficult to name the expected behavior. Moving from generic formulations as “adopt distributed leadership,” “close commercial meetings more effectively”, to behaviors that are observable, specific, and context-influenced is a cognitive effort that, as several participants pointed out, is rarely completed in most change initiatives.
Yet that is precisely where the difference lies between a good intention and a change initiative capable of producing results.
In the second session, the Target Behavior–KBI canvas brought a second shift into focus: from KPIs to KBIs, 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, particularly on change challenges connected to digital transformation and AI.
The third session introduced the most significant enrichment: the Head-Heart-Gut method as a tool for diagnosing barriers to change. 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.
Resistance to change often stems from one or more of these centers being blocked — 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.
Several participants recognized in this framework a language they had already been using intuitively — and, for the first time, a tool to apply it with discipline in their own projects.
The Takeaways
Looking back at both journeys, what strikes us most is the thread connecting the two labs.
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.
This is demonstrated by the fragility of interventions that act on awareness alone. 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.
From this reading, a coherent role for HR emerges: choice architect on the behavioral front, and workforce transition conductor on the AI front. 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.
The value generated by these two journeys lives, above all, in the people who inhabited them.
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.
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.