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"Chris transformed our early-stage GenAI tool into a scalable product with clear business value."

Project Overview
 

GSK needed to improve the performance and usability of its internal GenAI knowledge discovery tool; a system designed to help employees quickly retrieve scientific research documentation, internal reports, and domain insights.
 

By enabling employees to find information without relying on external vendors, the platform had the potential to save millions in research costs and dramatically accelerate internal productivity. The Trust required a clear product vision, defined use cases, and a roadmap to secure further investment.

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As an AI Product Consultant, I was responsible for strengthening the product direction, improving user experience, and driving adoption:

  • Owned the full product roadmap for the GenAI platform

  • Worked closely with engineering teams on feature development and prioritisation

  • Improved the user experience and information retrieval flows

  • Partnered with internal teams to design and validate new AI use cases

  • Identified data needs and worked with stakeholders to enrich training data

  • Built a business case and supporting evidence to secure future funding

  • Engaged with global functions to align tool capabilities with R&D, commercial, operations, and knowledge teams

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Key Deliverables

  • Improved UX and user journey for the GenAI search tool

  • Prioritised, user-value-based product roadmap

  • Portfolio of validated GenAI use cases across multiple teams

  • Data strategy and requirements documentation

  • Evidence-backed business case for continued funding

  • Adoption and enablement framework for internal teams

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Outcomes

  • Improved GenAI research retrieval accuracy, reducing reliance on external intelligence vendors and driving significant annual cost savings

  • Optimised the internal AI tool’s UX, enabling employees to locate scientific insights in minutes instead of hours, improving R&D productivity

  • Delivered new AI use cases that unlocked measurable efficiency gains across multiple business units

  • Strengthened data pipelines, improving model performance and reducing rework and wasted effort across research teams

  • Built the business case that helped secure funding for the next product phase by demonstrating clear commercial and time-saving value

  • Increased tool adoption and usage, amplifying internal savings and improving utilisation of existing R&D knowledge assets

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Goals & KPIs

  • Improve relevance and accuracy of research document retrieval

  • Increase platform usage, adoption, and user satisfaction

  • Reduce dependency on external search and intelligence vendors

  • Expand the library of validated GenAI use cases across teams

  • Build a compelling business case for sustained investment

  • Strengthen data foundations to improve model performance

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Challenge

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  • Existing AI search experience was inconsistent and difficult to use

  • Users struggled to discover relevant research documents quickly

  • Heavy reliance on external intelligence vendors created unnecessary cost

  • Lack of structured use cases and no scalable roadmap for new capabilities

  • Limited internal understanding of how GenAI could support different functions

  • Funding dependent on demonstrating clear value and adoption potential

This created a major opportunity to improve internal R&D efficiency, reduce spend, and build a scalable AI product foundation.
 

Strategy & Approach

 

1. Deep product and UX evaluation
Conducted a full audit of current experience, identifying usability gaps, friction points, and opportunities to streamline workflows.
 

2. Engineering collaboration & roadmap ownership
Worked closely with engineering to:

  • Prioritise features based on user value

  • Improve retrieval accuracy

  • Simplify interface and interaction models

  • Enhance underlying data pipelines
     

3. Use case discovery & validation
Partnered with multiple business units to identify high-value GenAI use cases, ensuring alignment with research, compliance, and operational needs.
 

4. Data strategy development
Defined data requirements to improve model accuracy and expand document coverage, supporting long-term product scalability.
 

5. Business case & funding strategy
Created a structured case demonstrating:

  • Vendor cost savings

  • Time savings for researchers and teams

  • Scalability of new use cases

  • Increasing internal adoption and productivity benefits
     

6. Operational alignment & cross-team enablement
Collaborated with product, research, compliance, and data teams to ensure alignment and smooth integration into workflows.

Hi

Get in touch to discuss my experience, past projects, or any new project you’d like to explore.

Man at Carnival
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