Collaborative AI

Overview

Logica is Charles River Labs’ AI-driven drug discovery platform. At the time, it was in beta and delivered insights through a Power BI dashboard. While helpful, this wasn’t the premium, customer-centred experience you’d expect for a high-value scientific partnership. My work focused on grounding the service in real customer needs and reframing Logica as a clearer, more contextual, and more collaborative experience that matched the sophistication of the underlying science

Organisation

Charles River Labs

My responsibilities

Discovery Research, Journey Mapping & Synthesis, Service Visioning, Wireframes, Prototyping

Challenge

Logica’s AI could analyse huge chemical datasets and speed up drug discovery. However, the way customers experienced it didn’t match the promise. Clients were paying premium fees, yet receiving results through static dashboards that were hard to interpret and easy to misread without context. The challenge was to bring the value to the surface: make insights clearer, build trust in the outputs, and shape an end-to-end experience that felt like a true collaborative service and not just data delivery.

Research playback insights 1
Research playback insights 1

Approach

Scoping and strategy → Framed the discovery around key customer segments and success criteria; set research questions to pinpoint friction in communication, data delivery, and interpretation, and clarify what a 'premium partnership' should feel like. Mixed-methods research → Combined interviews, observation, and surveys with secondary research and competitor analysis; mapped the end-to-end journey to surface breakdowns and synthesised insights into clear opportunity themes to inform the service vision. Co-creation and testing → Translated findings into visual artefacts (journey maps, themes, mock-ups) to align cross-functional teams; facilitated focused workshops with scientists, sales, and marketing to co-design future-state concepts, then created wireframes and partnered with a designer on prototypes to test with scientific and commercial audiences and iterate on feedback. Used simple scenarios to show how AI could make the experience clearer, more personalised, and more collaborative.

Approach

Scoping and strategy → Framed the discovery around key customer segments and success criteria; set research questions to pinpoint friction in communication, data delivery, and interpretation, and clarify what a 'premium partnership' should feel like. Mixed-methods research → Combined interviews, observation, and surveys with secondary research and competitor analysis; mapped the end-to-end journey to surface breakdowns and synthesised insights into clear opportunity themes to inform the service vision. Co-creation and testing → Translated findings into visual artefacts (journey maps, themes, mock-ups) to align cross-functional teams; facilitated focused workshops with scientists, sales, and marketing to co-design future-state concepts, then created wireframes and partnered with a designer on prototypes to test with scientific and commercial audiences and iterate on feedback. Used simple scenarios to show how AI could make the experience clearer, more personalised, and more collaborative.

Logica UI mock-ups
Logica UI mock-ups

Impact + Outcomes

Approach

The work helped the team shift from a reporting dashboard mindset to a more premium, customer-centred service vision. It aligned scientists, sales, marketing, and leadership around shared insights and practical design principles, and surfaced a set of quick wins to improve the near-term customer experience. While the recommendations weren’t fully taken forward, the project reinforced that translating complex science and data into clear principles and scenarios is what makes AI innovation feel accessible, valuable, and genuinely collaborative.

Scoping and strategy → Framed the discovery around key customer segments and success criteria; set research questions to pinpoint friction in communication, data delivery, and interpretation, and clarify what a 'premium partnership' should feel like. Mixed-methods research → Combined interviews, observation, and surveys with secondary research and competitor analysis; mapped the end-to-end journey to surface breakdowns and synthesised insights into clear opportunity themes to inform the service vision. Co-creation and testing → Translated findings into visual artefacts (journey maps, themes, mock-ups) to align cross-functional teams; facilitated focused workshops with scientists, sales, and marketing to co-design future-state concepts, then created wireframes and partnered with a designer on prototypes to test with scientific and commercial audiences and iterate on feedback. Used simple scenarios to show how AI could make the experience clearer, more personalised, and more collaborative.

Impact

The work helped the team shift from a reporting dashboard mindset to a more premium, customer-centred service vision. It aligned scientists, sales, marketing, and leadership around shared insights and practical design principles, and surfaced a set of quick wins to improve the near-term customer experience. While the recommendations weren’t fully taken forward, the project reinforced that translating complex science and data into clear principles and scenarios is what makes AI innovation feel accessible, valuable, and genuinely collaborative.

Previous — Scaling a service experience

Previous — Scaling a service experience

Previous — Scaling a service experience

Previous — Scaling a service experience

Previous — Scaling a service experience

Previous — Scaling a service experience

Next — AI to accelerate R&D

Next — AI to accelerate R&D

Next — AI to accelerate R&D

Next — AI to accelerate R&D

Next — AI to accelerate R&D

Next — AI to accelerate R&D

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