Pharma — Cutting Product Search Time by More Than 60%
How a focused ML solution can create significant productivity value by reducing time spent searching for the right product information.
This page continues the same transformation case in depth. It covers the situation, transformation logic, implementation considerations, practitioner lessons and a reusable framework.
← Read the transformation in under a minuteWhat the case teaches when you look beneath the headline.
1. The situation
Not every high-value AI transformation needs to be enterprise-wide. Sometimes a narrow workflow contains enough repeated friction to justify a focused ML intervention.
2. Productivity problems are often search problems
Knowledge workers can lose significant time locating the right information, especially when product portfolios are large or terminology varies.
3. Define relevance before building search
A better search experience depends on what “right product” means in the business context. That requires domain logic, not only a technical retrieval mechanism.
4. Measure time-to-answer
The >60% reduction is powerful because the metric is directly tied to the workflow friction the solution was meant to remove.
5. Focused AI can be strategically useful
Small, measurable interventions can build confidence in AI by solving concrete work rather than asking the organization to absorb a broad transformation all at once.
What practitioners can reuse.
Choose measurable friction
Narrow use cases can create strong value when the pain is frequent and quantifiable.
Define relevance explicitly
Search improvement requires agreement on what a useful result looks like.
Measure workflow time
Time-to-answer can be a clearer value metric than technical model scores.
Use focused wins to build credibility
Practical productivity gains can strengthen confidence in broader AI adoption.
A practical way to approach a similar problem.
- Measure current search effort — Quantify how long users spend locating information.
- Define a good result — Capture relevance rules and common user intents.
- Structure the data — Prepare product information for retrieval and ranking.
- Build and test the solution — Compare search quality against real tasks.
- Measure time saved — Track end-user productivity improvement.
- Improve from failed searches — Use misses to refine data and relevance logic.
Use the case as a discussion guide.
- How much time do users lose searching today?
- What makes one result more relevant than another?
- Is the underlying product data structured well enough?
- Which technical metric correlates with user productivity?
- How will failed searches improve the solution?
Return to the one-minute transformation summary.
← The transformation in under a minute