This live webinar is led by Alexander Rispal. Register / Join the webinar:
https://connect.clickmeeting.com/ai-at-work-what-to-do-first-and-what-to-avoid-/register
Meeting ID: 465-347-221
Alexandre Rispal is a French academic, entrepreneur, and
thought leader in insurance and technology. He holds a
Master’s in Public Administration from Sciences Po Lille, a
Doctor of Business Administration from LIGS University, and
certificates from Harvard Business School Online
(Innovation) and Harvard Law School (International
Finance). With over 30 articles published in Forbes France
and 10 books on marketing, social innovation, and the
metaverse (Argus de l’Assurance and Kawa editions), he is
a prolific voice in the industry. Alexandre has lectured at
Insurtech France and leading business schools in France
and the UK, and has spoken at over 50 international
conferences across the US, Israel, Italy, and beyond.
Awarded France’s Best Young Manager in Insurance in
2017, he has held executive roles as CEO and CRO in top-
ranked fintechs and insurtechs, including Moonshot
Insurance (KPMG Top 100 Fintech, 2019) and Wakam
(Insurtech100, 2020). His research includes several Scopus-
indexed articles. Today, he advises and accelerates
businesses as an operating partner and consultant, bridging
academia and industry innovation.
Information about
the webinar Webinar Content & Aims:
The session AI at Work: What to Do First (and What to Avoid) is designed to help professionals take practical first steps with AI in their organizations. We’ll cover:
• Practical starting points: Identifying quick wins (automating repetitive tasks, using low-code/no-code tools), piloting projects with clear goals, and focusing on high-value use cases, exploring IVRT methodology (Incremental Changes x Velocity of use cases implementation = Radical Transformation)
• Common pitfalls to avoid: Data quality issues, unrealistic expectations, ethical/compliance risks, and the importance of team buy-in.
• Key takeaways for attendees: A ready-to-use framework to launch their first AI project, criteria for selecting the right tools, and metrics to measure success (time saved, error reduction, etc.).
