Executive summary
b”>Part of ABB Group · Contributed Use Case73% of employees are enthusiastic about AI. The bottleneck isn’t willingness — it’s the organisation. B&R’s case shows what happens when individual readiness runs ahead of collective infrastructure.
Index deployed
HI Willingness Survey · 26 employees
Six dimensions mapped
Mindset · Safety · Support · Efficacy
4P roadmap
People · Platform · Policies · Projects
73%
Employees in most enthusiastic adoption group
4.5/5
Growth mindset — highest scoring dimension
3.84/5
Organisational support — lowest dimension
Lvl 1
Current HI maturity — local signals, not yet shared
Individual willingness is high — but the most skilled AI users don’t share their knowledge. Expertise stays tacit, person-dependent, and impossible to scale across the organisation.
CHI applied the HI Willingness Index across B&R’s Sales and Application teams, mapping six dimensions and identifying three adoption profiles — from enthusiastic champions to cautious engagers to one skeptic requiring targeted onboarding.
A concrete roadmap: role-specific learning tracks, a shared prompt library, clear AI governance guidelines, and structured knowledge-sharing practices. The Index becomes a recurring navigation tool as the organisation matures.
Introduction
CHI collaborated with B&R Industrial Automation A/S — part of ABB Group — through a master’s thesis project to deploy the HI Willingness Index: a survey instrument designed to measure the cognitive and psychological preconditions for human-AI co-creation in organizations. Applied across B&R’s Sales and Application teams, the survey was complemented by a qualitative interview with a senior team member, giving a rich picture of where the organization stands — and what is holding it back.
The Context
The collaboration originated from B&R’s ambition to turn AI adoption into measurable customer outcomes in the teams that matter most: the salespeople and application engineers who build and maintain customer relationships daily. The HI Willingness Index survey — completed by 26 employees — mapped perceptions across six dimensions: growth mindset, psychological safety, AI self-efficacy, organizational support, customer value impact, and sense of partnership in the AI journey.
The individual-level picture was strong. 73% of employees fell into the most enthusiastic AI adoption group, and psychological safety and growth mindset — two core preconditions for human-AI co-creation — scored highest across all categories, averaging 4.3 and 4.5 out of 5. Employees felt safe to experiment, ask questions, and believe in their own capacity to grow. These are rare organizational assets.
But the organizational layer told a more complicated story.
The HI Element
The survey revealed a stark gap between individual willingness and organizational enablement. One of the strongest findings in the dataset was the link between AI usage and perceived customer outcomes — employees who used AI tools regularly were consistently more likely to report faster response times, better decisions, and more tailored solutions for customers. This was not a marginal effect: it was the dominant pattern across the data.
Yet organizational support was the lowest-scoring dimension of all six — averaging just 3.84 out of 5, nearly half a point below psychological safety. Training was experienced as too generic, guidance on tool selection and prompt formulation was inconsistent, and data handling boundaries remained unclear. Most telling: the employees most skilled at using AI were also the least likely to share that knowledge — leaving expertise tacit, person-dependent, and impossible to scale. This pattern is visible in the relationship between regular AI usage and perceived internal sharing: higher AI use does not translate into stronger knowledge-sharing practices.
The respondent analysis further revealed three distinct profiles: 19 highly enthusiastic employees, representing 73% of respondents; 6 moderately enthusiastic or cautiously engaged employees, representing 23%; and one distinctly skeptical respondent, representing 4%. These profiles suggest that B&R should not treat AI adoption as a single, uniform process. Instead, highly engaged users can be mobilized as AI champions, cautiously engaged employees may benefit from clearer guidance and role-specific support, while skeptical employees require targeted onboarding that addresses concrete frustrations and demonstrates practical value.
Mapped against the HI Use Case Maturity framework, B&R currently sits at Level 1: local HI capacity signals — individual employees have found effective human-AI workflows, but learning remains undocumented and non-transferable. The 4P infrastructure needed to reach Level 2 — explicit HI capacity articulation — is not yet in place.
The Envisioned Outcomes
The study gives organizations like B&R a concrete starting point and a clear sense of where to improve. By repeatedly measuring the HI Willingness Index and mapping current practices against the 4P framework (Sherson et al., 2025), organizations can track progress and identify exactly which layer is holding them back across Projects, Platform, Policies, and People.
For B&R, the immediate next steps are clear: role-specific learning tracks (People), a shared prompt library (Platform), clear AI governance guidelines on tool selection and data boundaries (Policies), and structured knowledge-sharing practices such as use case sessions and AI champions (Projects). But the trajectory doesn’t stop there.
As organizational support strengthens and employees move from scattered individual use toward shared practice, co-design maturity naturally rises — from user-informed design toward participatory design with shared decision agency. With that shift comes a deeper collaboration mindset, opening the door to structured human-AI interaction through the FERC framework (Frame–Explore–Refine–Commit). At that stage, employees are no longer just using AI tools — they are actively governing how they interact with them, preserving authorship and judgment at each step.
This in turn creates the conditions for business model innovation: new workflows, new use cases, and new ways of delivering customer value that were not previously possible. Each iteration of the HI Willingness Index becomes a navigation tool — showing how far the organization has moved across all five maturity dimensions, and where to focus next.