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Jessica
Saini
Data Scientist
RBC Group
Jessica Saini is a Data Scientist with a proven track record of driving AI adoption across financial services, consulting, and research. Her career journey began as an Engineer at JP Morgan Chase, followed by roles at KPMG, Vector Institute and RBC Capital Markets. Her expertise spans the end-to-end lifecycle of building scalable AI applications, from data engineering and modeling to deployment and optimization. Jessica leverages the latest technologies to deliver meaningful, business-aligned solutions that bridge the gap between innovation and real-world impact. Jessica is a strong advocate for promoting gender diversity in the field of AI and actively mentors aspiring professionals to break barriers and thrive in their careers. She has had the privilege of working with some of the most talented individuals and organizations globally, and she channels this experience into her work as an advisor for early-stage startups, helping them navigate data strategy, AI adoption, and growth. As a public speaker and mentor, Jessica brings together her passion for technology, business, and personal growth. She is deeply committed to empowering others to create impact, share their voices, and shape the future of AI responsibly and inclusively.
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20 November 2025 16:00 - 16:30
Panel | Explainability and transparency in autonomous agents
If your autonomous agent made a costly decision tomorrow, could you explain why? As these systems move from lab experiments to enterprise-scale deployment, trust becomes a make-or-break factor. How do you explain decisions made by agents that operate with a high degree of autonomy and ensure they align with business goals, compliance needs, and user expectations? In this panel, engineering and technical strategy leaders share how they’re embedding transparency into agentic AI architectures, from design through to live production environments. Expect candid discussion on the trade-offs between speed, performance, and explainability and what it really takes to win stakeholder confidence at scale. Key takeaways: → Architecting for explainability without sacrificing performance. → Tooling and frameworks for monitoring autonomous decision-making. → Techniques for surfacing model reasoning to non-technical stakeholders. → Balancing governance requirements with innovation velocity.