Bio
Kieran began his career at IQVIA, delivering brand and competitive intelligence solutions for large pharma and watching data interaction evolve from business intelligence to self-service reporting. Now at Norstella, he’s focused on bringing mastered pharma data into customers’ AI-enabled tools, driven by a passion for democratizing data so valuable insights reach everyone who needs them.
What makes Norstella’s AI different?
When people talk about AI, it’s easy to get caught up in the technology, the models, , the efficiency. But the tech is only ever as good as the data underneath it. For me, the real craft is in the curation. Aggregating content is easy; plenty of people do it. The hard part, and what Norstella’s subject-matter experts have been doing for decades, is taking complex data sources and resolving, mastering and interpreting it into something you can trust. A drug’s development history, a trial’s true status, a competitor’s pipeline: it’s understood and maintained by people who know the industry. That’s the rigour our AI inherits.
What can pharma teams do now that was not possible two years ago?
What’s genuinely new is who can reach the data. Two years ago, interrogating a pipeline or pulling competitive intelligence meant going through a specialist who knew where the data lived and how to query it. If the data is trusted and well-mastered underneath, anyone can find the insight they need for themselves. That shifts what data literacy means. It’s less about knowing code and more about asking the right questions and thinking critically about the answers. The barrier moves from technical skill to curiosity, and the expertise baked into the data finally reaches everyone who needs it, not just those who could navigate the tooling.
How can AI accelerate the journey from pipeline to patient?
The journey from pipeline to patient is a series of decisions, and each one is slowed by the time it takes to gather and make sense of the evidence. Which indication to prioritise, where the competitive white space is, how to design a trial, when to engage payers. Each has traditionally meant weeks of pulling data together before anyone decision making. AI compresses that. When agents can reach data directly, much of the evidence-gathering happens in the background and teams spend more of their time on the judgement itself. Implemented well, AI shortens the path to each decision and, in turn, the path to getting treatments to patients.