AI Leaders in Tech Night // Lisbon

​An AI-Native Workflow Gathering for Leaders in Tech

​A high-signal evening summit for Founders, Engineering Leaders, CTOs and product leaders shaping how engineering Teams become next.

​📍 Lisbon, Devoteam (Auditorium & Lobby)
🕙 17:00–22:00 | Monday 9 November
🤝 70 attendees (curated only)

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​AGENDA //

​17:00 Doors open: Networking & Drinks
18:00 Democratising AI Platform Engineering with Open Source: Own It, Run It, Trust It by Alfonso Sandoval from Cisco.

​AI platform engineering is usually sold as something only big teams can afford, or as a leap of faith in someone else’s black box. It shouldn’t need either. When every agent, tool and permission is open to read, run and change, a small team can start small, see how each decision is made, and decide how much freedom its agents get.

​18:30 Beyond AI Coding: How Engineering Leaders Are Rebuilding Their Teams and Workflows Panel

​🎤 Rijk van Zanten, CTO & Co-Founder - Directus 🎤 Pedro Afonso, VP of Engineering - Engine AI

​AI is changing far more than how we write code. It's reshaping engineering teams, workflows, leadership responsibilities, and the way software gets built and shipped. Expect candid insights, real-world experiences, and practical lessons on what's working, what's not, and what comes next.

​19:30 Mastermind Roundtables (topics below)
20:30 Networking, Drinks & Bites

​Masterminds Roundtables

​Confirmed Roundtable Leaders:

• ​Gina Schinkel, Founder @ Driftawave

• ​Tom Droste, CTO @ Secret View

​Area 1: What Actually Changes

​⚡ Junior Hiring Challenges: If AI does entry-level work, how do you hire, onboard and grow juniors, and where do your future seniors come from?
⚡ Proving the ROI: What are you actually measuring, and how do you explain AI productivity to your CEO, CFO or board without made-up numbers?
⚡ Reshaping the organisation: Team sizes, EM-to-engineer ratios, new roles (AI enablement, platform), and what the engineering manager's job looks like now.

​Area 2: How We Rebuilt Our Workflow ⚡ Quality and ownership: Code review, tech debt and security when much of the code was generated. Who is accountable for code nobody fully wrote? ⚡ Rollout and governance: Getting adoption past the enthusiasts, handling resistance, tool sprawl, cost control and usage policies. ⚡ Beyond autocomplete: Agents picking up tickets, writing tests, triaging incidents. What actually works in production, and where do humans stay in the loop?