Afroditi Katika
Director of Product, Intelligencia AI
Afroditi is Director of Product at Intelligencia AI responsible for data products and tech-enabled internal efficiencies. A computer engineer by training who transitioned into product management over the past 10+ years. Afroditi has previously worked in consulting (Accenture) and in public sector entities (European Commission, BBC News, Transport for London). Since 2020 she focuses on AI applications in Healthcare both in industry and in an active research collaboration with the Nursing Department of the University of Athens.
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Implementing AI tools to Automate Repetitive Tasks and Improve Efficiency.
Scaling Human Expertise with AI—Without Sacrificing Wellbeing
As AI capabilities evolve, many companies are re-evaluating how to scale internal operations without overburdening their teams. In this session, we’ll explore how Intelligencia AI has approached this challenge within the context of clinical data curation—a process that involves harmonizing more than 1.5 billion data points on a daily basis.
Rather than framing AI as a replacement for human expertise, Intelligencia focused on designing tools and workflows that support and extend the capacity of their Clinical Data Experts. Over the past two years, this has required deliberate choices about which problems to address with AI, how to balance exploratory research with product strategy, and where the use of large language models (LLMs) added value—or fell short.
This session will cover:
1.Decision-making around build vs. don’t-build when it comes to internal AI tools
2.How product and research teams collaborated to stay aligned
3.What worked, what didn’t, and what’s still in progress when it comes to AI integration
4.Reflections on maintaining team wellbeing in the face of scaling demands
The discussion aims to offer a candid look at the practicalities of embedding AI into operational workflows, with lessons applicable to any team navigating similar trade-offs.
Rather than framing AI as a replacement for human expertise, Intelligencia focused on designing tools and workflows that support and extend the capacity of their Clinical Data Experts. Over the past two years, this has required deliberate choices about which problems to address with AI, how to balance exploratory research with product strategy, and where the use of large language models (LLMs) added value—or fell short.
This session will cover:
1.Decision-making around build vs. don’t-build when it comes to internal AI tools
2.How product and research teams collaborated to stay aligned
3.What worked, what didn’t, and what’s still in progress when it comes to AI integration
4.Reflections on maintaining team wellbeing in the face of scaling demands
The discussion aims to offer a candid look at the practicalities of embedding AI into operational workflows, with lessons applicable to any team navigating similar trade-offs.
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