ABOUT ME

17 years building institutional AI capability across banking, telco, retail, energy, and public sector. Started in strategy and design, grew into AI transformation leadership because that's where leverage compounds, not in individual solutions, but in how an organization measures, governs, and scales them.

What this adds up to so far:

• €59M in enterprise AI and transformation revenue closed
• 40% reduction in operating costs in core service environments
• 14% churn reduction in highly competitive telco markets
• 15% global price increase for a major hardware brand without volume loss

What matters more than the numbers is the discipline underneath them.

MEASUREMENT DISCIPLINE FROM DAY ONE

Most AI transformations fail because measurement is an afterthought. In my work, measurement starts in week one and never leaves the room.

At BCI Bank in Chile, I didn't start with a technology roadmap. I started with their CFO and roughly forty stakeholders from contact center operations, retail, compliance, and IT.

The questions were concrete and uncomfortable on purpose:

• How many fewer calls do you need in the contact center, in absolute numbers?
• Over what timeframe?
• What's that worth to you annually in local currency?
• What does "faster resolution" mean in actual minutes?
• What NPS level would force us to admit this hasn't worked?

From there, we documented baselines: call volumes, average handling time, NPS at 62, cost per interaction, adoption of existing tools. Together we set targets: 40% fewer calls, resolution time down by roughly 20%, NPS into the mid-seventies, visible ROI inside the first six months.

Only when the CFO signed off on the measurement approach did we move to design and implementation.

Throughout the 18-month program, we treated measurement as part of the product:

• Weekly dashboards instead of annual reviews
• Monthly business reviews with the CFO where technical progress and financial impact sat on the same slide
• Instrumentation at the level of teams and behaviors, not just systems

The outcome was a 40% reduction in call volume, 22% faster resolution times, a step change in NPS, and a contract that expanded to €10.8M.

More importantly, the framework became repeatable. The bank could use the same pattern on other technology decisions without needing me in the room.

That repeatable discipline, plus the audit trail that now maps to EU AI Act Article 6 requirements, is what I bring to Head of AI Strategy roles.

TRACK RECORD ACROSS SECTORS

BANKING
• BCI Bank (Chile): Rescued an at-risk pilot, then scaled to €10.8M engagement. 40% fewer calls, 22% faster resolution, NPS 62→75, €14.2M annual savings (CFO-verified). Audit trail anticipates EU AI Act documentation requirements.
• Flowe (Banca Mediolanum): Supported launch of digital bank that onboarded 18K accounts in the first week, shortened onboarding from 22 to 8 minutes, maintained 4.7/5 App Store rating.

TELCO & AIRLINES
• Fastweb NeXXt: Led strategy for Italy's first Alexa-enabled router. Over 18-month control-group study, customers using NeXXt showed 14% lower churn than control group on legacy hardware. Two million consumers touched, iF Design Award 2022.
• AeroMéxico: Turned around failing contract by understanding how staff actually behaved under pressure in airports. Adoption moved from 31% to 78% in eight weeks, €7.9M contract.

RETAIL & TECHNOLOGY
• Lenovo Spazio: Shifted Lenovo from spec-sheet comparison to experience-led retail. Milan flagship became global template (14 countries). Work supported 15% global price increase without losing volume, executive sponsor promoted to Head of Global Retail.

WHY MOVE FROM ADVISORY BACK INTO A PERMANENT ROLE

In 2024-25, I took a deliberate step back from permanent posts. At Board of Innovation and through research at Università Federico II, the focus was to document what actually works in AI transformation, especially for organizations without large internal data science teams.

That period confirmed something simple: depth beats breadth.

Fractional advisory is useful for early framing and pilots, but the real leverage sits in staying long enough to shape culture, incentives, and measurement habits. The work becomes truly interesting in years two and three, when difficult trade-offs appear and compounding effects start showing up in the P&L.

The next chapter is therefore clear: an embedded, multi-year Head of AI Strategy or Senior AI Strategy Director role in an organization that's serious about measurement, not theater.

WHAT I'M LOOKING FOR

• Role: Head of AI Strategy, Senior AI Strategy Director, or equivalent
• Context: €500M+ enterprise with board-level ambition for AI and data
• Sectors: Banking, telecom, technology, retail, or adjacent industries where there's already proof of traction
• Geography: EMEA, with focus on Italy, Spain, Netherlands, UK, Switzerland, and Germany
• Mode: Permanent, remote-first with up to 50% travel, open to relocation in 6-12 months

Based in Florence. Belgian-Italian national. Fluent in Italian, English, and Spanish; working proficiency in French and Portuguese.

MY APPROACH

How I Lead Measurement-Driven AI Transformation

I operate as embedded Head of AI Strategy, not external consultant, not fractional advisor. Multi-year commitment, not 3-month engagement. The difference is accountability. When you're embedded, your compensation moves with the outcomes. That changes everything about how you prioritize.

THE SELF-FUNDING AI METHODOLOGY

Most AI leaders drag EBITDA for 18 months before showing ROI. I use a different approach: audit the pilot in 48 hours, kill what's failing, and fund the forward roadmap through Q1 operational savings.

The framework is simple: measurement before technology.

MONTH 1: FOUNDATION
• Value Definition Workshop (CFO + department heads)
• Document baselines: current state in actual currency
• Define targets: specific, measurable, time-bound
• Get CFO sign-off on measurement framework before technology deployment
• Build audit trail that anticipates regulatory requirements (EU AI Act Article 6)

MONTHS 2-6: PILOT + PROOF
• Weekly dashboards: actual vs. projected outcomes
• Monthly CFO reviews: technical progress + business impact on same page
• Rapid iteration based on evidence (not opinion)
• First measurable outcomes visible by Month 3
• Control group methodology where applicable (like Fastweb's 1M treatment vs 1M control)

MONTHS 7-18: SCALE + INSTITUTIONALIZE
• Expand successful pilots to additional departments
• Train internal teams on measurement discipline
• Create repeatable playbooks for future initiatives
• Embed framework into organizational DNA
• Build "Human-in-the-Loop" control infrastructure for autonomous AI agents

MONTHS 19-36: OPTIMIZE + COMPOUND
• Measurement discipline becomes organizational habit
• AI value visible in quarterly board presentations
• Framework scales to adjacent use cases
• Leadership prepared to self-sustain post-departure
• Governance framework satisfies regulators without paralyzing teams

WHAT THIS MEANS PRACTICALLY

At BCI Bank, the framework I embedded in Year 1 became repeatable across their entire organization. They didn't need me after 18 months, they had the capability. The audit trail we built now positions the Board as a "Liability Shield", documentation that anticipates EU AI Act Article 6 requirements before regulators ask.

At Fastweb, the control group analysis methodology became standard practice for all major technology investments. That's institutional capability, not consultant dependency.

At Lenovo, the experience-driven positioning I created in Milan became the global template. That's leverage, not one-off magic.

THE LEADERSHIP PHILOSOPHY

Autonomy within constraints: I set clear outcomes and success criteria. Teams own how they get there. Example: "Your goal is 40% volume reduction in 9 months. How you do it is yours. I'll unblock obstacles."

Radical transparency: Truth over comfort. Quarterly retrospectives where teams share what they tried, failed, learned. Rule: don't present the same failure twice.

Praise in public, feedback in private: Builds psychological safety. Creates environment where people tell you when things aren't working.

AGENTIC AI ORCHESTRATION

As organizations deploy autonomous AI agents in supply chain, HR, and finance, someone needs to build the "Control Towers" that ensure these agents operate within P&L safety margins. That's the orchestration layer I design, Human-in-the-Loop governance that allows innovation without catastrophic operational errors.

WHAT I'M SEEKING