Applying AI in life sciences requires more than model development: it requires clear problem definition, appropriate methodology, and alignment with real decision-making needs.
This on-demand webinar, led by Nechama Katan, provides a structured walkthrough of how teams can move from initial problem framing to operational AI analytics that are usable, reliable, and fit for purpose.
In this session, you’ll explore:
- The essential steps from defining the problem to delivering usable AI-driven analytics
- Key questions that ensure GenAI and agentic systems are applied to the right problems
- Methods for producing outputs that are reliable, relevant, and decision-ready
- Critical checkpoints for moving from analysis to operational deployment
This session reflects a core focus of AnalytiCon: designing AI analytics that move beyond experimentation and into real-world application, where outputs support decisions and integrate into business workflows.
About the Presenter
Nechama Katan helps organizations define, drive, and deliver data-focused technology initiatives. With deep experience in data management, technology integration, and project lifecycles, she guides teams from identifying business needs through implementation and integration. She previously served as Director of Innovative Data Analytics at a large pharmaceutical organization. She holds an M.S. in Mathematics from the Courant Institute at NYU and an M.S. in Statistics from Columbia University.
Most organizations don’t lack AI models—they lack systems that make those models usable.
In practice, AI initiatives often stall after prototyping. Not because the models underperform, but because they aren’t integrated into workflows, don’t support real decisions, or require too much effort to operationalize.
Closing that gap comes down to three things: usability, integration, and adoption.
Download the full infographic to use as a reference for evaluating and operationalizing AI analytics initiatives.