Emilie Lundblad

Director of Data & AI, Amesto Nextbridge

Director of Data & AI at Amesto Nextbridge with 12+ years experience in the Data & AI field.

On a mission to improve the world with Data & AI, using data to make business both more efficient, more sustainable and more valuable to their customers.

Emilie Lundblad holds a minor in Machine learning & AI with MIT, and is giving guest lectures in the fall of 2022 on the course; «AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment.» In addition she is busy finishing up her executive master in IT management at ITU in Copenhagen, where she is also doing guest lectures in the fall of 2022 on among others Data Science. In addition she holds a candidate in quantitative finance & econometrics, and 15+ certifications in data science, AI, ML, data engineering & BI.

Emilie has previously done research projects with MIT & DTU on ML for dynamic pricing, as well as Data & AI for sustainability.

In her career Emilie has been both head of BI, Data & AI Manager, speaker on Data & AI at Danish IT, as well as Microsoft, nominated for Berlingske’s talent 100 & Nordic women in Tech’s Diversity leader of the year.

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Alle foredrag med Emilie Lundblad

  • Program


  • 14:30  - 14:55
    Based on the guest lecture I will be doing this fall with MIT, I would like to present real world cases and frameworks for AI strategies with implementation roadmaps.

    AI is revolutionizing many industries, including energy, consumer products and services, automotive, financial services, national security, healthcare, and advertising. But too often, business and IT leaders take a limited view of AI, focusing almost exclusively on machine learning (ML) methods. But AI technologies are, in fact, key enablers to complex systems. They require not only ML technologies, but also trustworthy data sensors and sources, appropriate data conditioning processes, responsible governance frameworks, and a balance between human and machine interactions. In short, organizations must evolve into a systems engineering mindset to optimize their AI investments.

    In this talk I will give an overview of how to get started, from strategy, to framework and practical tools needed for successful implementation.
    #4 Data Architecture




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Emilie Lundblad