Insight & Foresight

Enabling faster research planning and delivery through AI/ML

Challenge

Researchers working under grant and publication pressure faced fragmented planning workflows across multiple tools and datasets. Complex protein associations and large product catalogues increased planning time and the risk of missed research opportunities.

Risks

Reducing planning time without disrupting existing high-converting journeys
For the business, limited insight into emerging research trends constrained proactive portfolio investment.
Introducing new digital capability also carried risk to revenue-critical search journeys.

Priorities

Reducing planning time without disrupting existing high-converting journeys
Introducing AI as an augmentation, not a replacement
Prioritising speed, stability, and adoption over feature depth
De-risking impact through phased exploration and clear success metrics

Direction

Reducing planning time without disrupting existing high-converting journeys
For the business, limited insight into emerging research trends constrained proactive portfolio investment.
Introducing new digital capability also carried risk to revenue-critical search journeys.

Outcomes

Researchers were able to explore pathways and product associations previously hidden across disparate sources. Planning shifted from manual, multi-tool workflows to a single exploratory experience, improving confidence and breadth of consideration.

Impact

Demonstrated feasibility of AI-driven planning within existing channels
Established a foundation for scalable digital services alongside physical products
Improved collaboration across Product, UX and Data Science
~75% reduction in average planning time (≈3 weeks → 3–5 days)

Learnings

Predictive planning must enhance trusted search behaviours
Novel interaction models require careful onboarding and terminology clarity
Phased rollout is critical to adoption and risk management