Building groundwork for responsible AI to modernise a safety-critical and unionised sector

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This case study focuses on a strategic innovation company with fewer than 50 people, referred to here as InnovationCo, working inside a larger public body. Its mission was to use technology to modernise a sector that was traditionally slow to change. Without an internal HR team, InnovationCo relied on the parent organisation’s central HR for people support. 

To support the action research, a working group of mostly engineers and an ergonomics specialist was formed. Their goal was to expand the use of AI-based video imaging video reviews for maintenance planning in a specific region, in line with national policy. While HR was not part of the working group, IFOW spoke with a national HR lead to gain broader insights into the parent organisation’s culture and constraints.  

Operational context 

InnovationCo operated in a safety-critical, hierarchical sector that had a strong union presence. Small changes to processes or how people work could have major ripple effects on safety standards and management practices and trigger formal engagement with the trade union. These factors created an uncertain environment for innovation. As AI use and its impacts grew, many employees experienced high levels of anxiety.  

Challenge 

The main hurdle was the parent organisation’s fragmented structure and outdated processes. Because the project was primarily built for technical safety, there was no space for the team to formally flag – early on – how AI would change their daily work. As a result, complex problems went unaddressed. 

This created a ripple effect of challenges: 

• Fragmented decision-making. Without a clear way to ‘own’ decisions, teams worked in silos. For example, the parent organisation’s national HR lead saw the risk of ‘tissue rejection’ from the workforce and recommended early, frequent engagement with union colleagues. However, the national HR lead was not involved in the pilot phase. 
• Outsourced strategy. In the absence of a clear internal governance, the technology provider began to steer the project. This risked aligning the AI tool with the technology provider’s goals rather than InnovationCo’s mission or the needs of the engineers. 
• Operational paralysis. Teams felt stuck. They wavered between focusing on their own tasks and worrying about how broader organisational changes would be perceived. Information wasn’t shared across regions, which stifled any chance of peer learning or a shared vision. 

Ultimately the lack of coordinated oversight meant that the AI maintenance planner was being implemented for employees, rather than being designed deliberately to augment their skills. The team could see that redesigning workflows would be a helpful way to break through these organisational hurdles. But this remained a thought exercise rather than a practical shift because they felt they lacked the authority to change them.

What they did 

To help InnovationCo navigate these hurdles, IFOW led a series of interviews and workshops to create space for dialogue. The goal was to move beyond the technical and surface human opportunities and risks. 

Through interviews and observing a safety workshop and team meetings, IFOW identified that while the lead engineers were highly engaged, other critical functions like HR remained at a distance. This confirmed that the challenge wasn’t just technical. The deeply rooted organisational hierarchy made cross-functional collaboration difficult. 

ngineers, parent organisation leads and the technology provider came together to do the three targeted activities at a workshop run by IFOW: 

• System mapping. Participants mapped out how information flowed. This helped everyone see the system and identify who really needed to be in the room for decisions on the AI tool. It also gave them a chance to discuss openly the challenges they perceived. 
• Augmentation journey. Participants looked at the evolution of the AI tool, from an individual engineer’s resource for fault detection toward a coordinated system for planning preventative maintenance. They discussed the ways an engineer’s skills and judgment could be augmented over time. This helped identify decisions on how the AI tool would impact work and workflows. 
• Defining success. Participants began identifying measurable indicators of success to capture the people impact alongside technical ones. 

After the workshop, IFOW met with the working group to translate the workshop themes into actionable insights. The working group realised that a single regional use case could not be viewed in isolation, so the discussion shifted to process and governance gaps at national level: 

• Gap in responsibility. Current innovation processes didn’t allow for a systematic, cross-functional assessment of the AI tool. This meant that the people impact was a major strategic risk, but the responsibility for assessing the impacts was left to individual project teams. 
• Risk of escalation. Given the sector’s strong trade union presence, leaving the people impact unaddressed at organisational level risked creating future conflict. If significant risks around changing responsibilities in safety-critical areas surfaced too late, disputes could escalate. 
• Barriers to innovation. Outdated processes and rigid efficiency targets worked against the project because they didn’t capture social or secondary impacts. Instead, they drove up a sense of pace and anxiety that restricted the team from reflecting on how best to use the time freed up by the AI tool. 
• The value of job design. Considering the job design of those affected by the AI tool should be built into the procurement and pilot phases and revisited as the tool evolved. It shouldn’t be a one-off exercise. 

Outcomes 

The collaboration between IFOW and InnovationCo elevated the project from a technical implementation to a broader, strategic organisational evolution. The action research process surfaced four key outcomes: 

• Secured leadership buy-in. Senior management were engaged to trigger and require a formal change management process. They recognised that these systemic challenges must be addressed at a national level, rather than being left to local teams. 
• Proactive union engagement. The working group committed to bring trade union representatives into the conversation before formal consultation begins. By establishing joint working groups in neglected areas like job design, human control over decisions and procurement, the organisation has started the process  of moving towards a more transparent, less conflict-prone relationship. 
• A strategic pause for better design. Internal innovation processes were intentionally paused to allow for a deeper look at job design and workflows. This led to a proposal for a new work design panel within the ergonomics function so that the people impact could be mapped properly before moving forward. 
• New holistic metrics. InnovationCo began to develop an augmentation policy and reviewing its efficiency metrics. New success indicators have been set to move beyond simple technical output, which includes better information sharing and tracking the people impact of AI.  

The progress that was observed provides a vital starting point. To ensure a responsible and effective rollout, the senior leadership must translate these early wins into a long-term strategy that puts job design at the heart of the project’s success. 

Learning points 

• Prioritise cross-functional engagement. Integrating AI is not a purely technical project. Early involvement from HR and operational teams avoids fragmented decision-making and ensures systemic impacts are understood across the entire organisation. 
• Formalise change management to break silos. Ad-hoc testing without a clear roadmap risks reinforcing information silos and limiting what the organisation can learn. To ensure a responsible rollout, the organisation must establish formal governance and clear channels for sharing knowledge. 
• Take ownership of job design. While technology providers engage with end-users, their commercial interests don’t guarantee a focus on people impacts. Organisations must take the lead in assessing how AI will change work and augment people’s skills and judgment. 
• Set strategy early to prevent drift. Strong internal governance prevents external interests from taking the lead. Setting clear objectives from the start ensures the technology remains a tool for the organisation’s priorities, not the other way round.