Previously, he held senior leadership roles at Philips and Nokia, where he built digital innovation engines, shaped AI strategy, and defined software platform roadmaps at global scale.
Leading AI Transformation at Global Enterprises
Alberto Prado (former Unilever exec), shares insights from his extensive experience in leading AI and digital transformations at major companies like Unilever, Philips, and Nokia. Vincent & Alberto discuss the evolving landscape of technology disruption, the unique challenges of implementing AI in R&D, and the importance of building a robust data foundation.
Listen and discover
- Technology breakthroughs that drive the need for transformation
- The pace of disruption
- R&D requirements for AI
- The importance of data quality and richness
- Why AI transformation in R&D needs a different approach than other functions
- How partnerships are essential for successful AI transformation
- How to measure ROI
- The future of organizations
What does it take to drive AI transformation in the enterprise? Tune in to find out.
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The insiders
Vincent is a senior leader in AI strategy and business transformation, helping Fortune 500 organizations unlock the full potential of artificial intelligence. As a Global Partner at Board of Innovation, he specializes in shaping AI-driven growth strategies for consumer goods, retail, and technology leaders across the USA and Europe.
His expertise lies at the intersection of AI, business strategy, and enterprise transformation, helping senior executives navigate AI adoption and scale AI-driven decision-making. He’s focussed on helping organizations build future-ready AI strategies that deliver real business outcomes.
Alberto Prado is a veteran technology leader and digital transformation strategist who most recently served as the Global Head of R&D Digital & Partnerships and Vice President at Unilever.
During his tenure (ending in late 2025/early 2026), he was the primary architect behind the company’s digital R&D transformation, championing “in silico” innovation to move product discovery from physical labs to AI-driven computational models.
