Internal Alignment for AI Assistant Development
A case study conducted with Logidot
Case Study Authors
Niccolo Corsini (Logidot), Dr Ayse Begüm Kilic-Ararat and Dr Arsham Atashikhoei (University of Bath)
Context
Logidot is a technology provider specialising in location-based Industrial IoT solutions for warehouses and factories. As part of an Innovate UK BridgeAI-funded project, the company is developing a new AI assistant for warehouse workers, a prototype that integrates natural-language interaction, real-time location intelligence and an AI-powered warehouse simulator to provide personalised, context-aware support for frontline staff and managers. Before introducing this technology to customers, the company wanted to understand how internal teams perceived its potential value and whether expectations aligned across leadership, commercial and technical groups. Four senior participants, the CEO, CTO, CRO and a Senior Software Developer, joined a 2-hour online workshop representing different organisational perspectives. The session aimed to surface assumptions, explore organisational priorities and create a shared foundation for the next stage of development.
Objective
The purpose of the workshop was to explore the priorities that different internal groups associate with the introduction of the new AI assistant. By examining how leadership, commercial and technical participants evaluated organisational metrics, the team sought to understand where their expectations converged or diverged. The session also offered Logidot an opportunity to introduce and contextualise their ongoing AI development work, helping participants consider how the emerging technology might deliver value both internally and to future customers.
Approach
During the workshop, participants engaged with the Metric Tool to evaluate 36 organisational metrics spanning operational performance, business strategy and human-centric priorities. Each internal group independently identified the metrics they viewed as most and least important when considering the potential value of the new AI assistant. The process enabled participants to make their choices visible in a structured and comparable format. By examining the resulting selections collectively, the workshop created a shared space for discussion and reflection, allowing the team to explore how different perspectives might influence the future direction of the technology.
Insights
The comparison of metric selections revealed clear differences in how leadership, commercial and technical groups perceive organisational priorities. Leadership placed strong emphasis on human-centric and decision-making metrics, reflecting a view of the AI assistant as a tool that should empower people, strengthen capability and support more informed decisions. Technical participants prioritised optimisation, asset utilisation and visibility, approaching the technology from the standpoint of system performance, operational efficiency and integration. The commercial perspective focused on cost, productivity and service quality, indicating an expectation of direct operational and financial impact. These patterns illustrated distinct mental models regarding how the AI assistant should generate value.
Despite these differences, all groups identified company value as a top priority, suggesting a shared understanding that the technology should enhance organisations’ strategic position and competitive strength. The divergence between human-centred and system-focused priorities, however, highlighted where internal expectations vary and where further alignment may be useful. As participants reflected on the results, the tool acted as a catalyst for revealing underlying assumptions and for showing how each role shapes its interpretation of customer needs, organisational challenges and the anticipated benefits of AI adoption.
The CEO observed that the most important takeaway was recognising the different perceptions held by each department regarding customer needs and priorities. This insight was considered essential for identifying blind spots and strengthening alignment across teams. The workshop also provided an opportunity for Logidot to present its emerging AI assistant, enabling participants to ground their reflections in the practical context of a technology designed to support onboarding, continuous upskilling and collaboration between human and automated operations.
Impact
“The workshop delivered a clear understanding of the priorities guiding different internal groups and highlighted areas of both convergence and divergence. This gave Logidot a stronger basis for decision-making as we advance the development of our AI assistant. The session contributed to greater cross-departmental awareness and encouraged early alignment around shared strategic goals, while also revealing where expectations differ and where further discussion is needed. The insights gained will support the company as it prepares to refine the technology and introduce it to its customer base.”
Niccolo Corsini Founder/CEO, Logidot
For further information on this case study please contact the P-LD at P-LD@bath.ac.uk
Acknowledgement
This work was supported by the Innovate UK led Made Smarter Innovation Programme: People-Led Digitalisation Engagement and Impact Acceleration [Grant Reference UKRI1436] Centre for People-Led Digitalisation, at the University of Bath, University of Nottingham, and Loughborough University.