Overview
Hebe Susana is the founder and chief executive of ai-boddie, based in Brussels, where she also leads research and development and works as a data scientist. She founded the company in August 2021. She states expertise in data science, data engineering, project management and business development.
Before that she was external relations manager at Treedy's from April 2019 to June 2020, and executive assistant to the CEO at BEyond Belgium from August 2018 to April 2019. Between April 2017 and April 2019 she was co-founder and CEO of STARTUP EUROPE NEWS by THE EUROPEAN COMMISSION, and from April 2016 to March 2018 founder and CEO of SIP Mezcal & Coffee. She worked on project concept innovation and digital fundraising innovation at Oxfam New Zealand from October 2014 to February 2015, founded BabyBorn in 2013, co-founded SUBVENCIS Grant Consultants in March 2012 as a grant writer, and was a researcher at Procter & Gamble in 2011.
She studied data science and big data analytics at the Massachusetts Institute of Technology in 2021, global trends for business and society at The Lauder Institute of the University of Pennsylvania and The Wharton School, and biology at Universidad Veracruzana, Universidad Nacional Autónoma de México and Universidad Anáhuac Veracruz.
Career history
In the news
- We are getting closer to a world where AI will not only recommend what to buy — it may also decide how our money moves. But if an AI chooses the currency, stablecoin, issuer, or payment infrastructure on our behalf, who sets the rules? In this article, I explore why this is becoming a new question of monetary sovereignty, user control and AI governance — especially as the GENIUS Act, MiCA and the AI Act begin to intersect. Would be very interested to hear how others see this evolving.
- Workflow memory is the missing layer that makes GenAI workflows reconstructable, reusable, and transferable across models and providers.
- REGULATION-BACKED EVIDENCE MAY BECOME EUROPE'S AI MONETISATION ADVANTAGEIn my last post, I wrote about workflow memory as the missing layer between AI adoption and AI control. The idea was simple: If companies use external AI tools but cannot prove how the work happened, who controlled it, or whether the workflow can move elsewhere, they are not only adopting AI, but also. They are creating dependency. But there is a second part to this conversation. - What if EU regulation is not only a constraint on AI adoption? - What if it
- Can European companies scale AI intelligently without becoming dependent on external platforms? Yes, but only if they control one critical layer: workflow memory. Today, companies use AI across CRM, chatbots, voice agents, campaign tools, analytics, AI video, HR screening, and customer support. Each tool captures part of the workflow. But the strategic question is: Has the external platform become the only place where your workflow logic, customer intelligence, measurement, and evidence exist? If yes, the company is not just using a
- The AI Dependency Europe Cannot Ignore European SMEs adopt Big Tech AI platforms to move faster. But if they do not control the model, the data, or the workflow, they may slowly lose margin, customer ownership, negotiation power, auditability, and strategic independence. The blind spot is that Europe’s dependency on third-country AI tools is not only about using their software. The dangerous blind spot is a deeper dependency on AI tools that sit in the layers where European companies create work, measure customers, automate
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