From inside the corporations
For more than 20 years, I led strategic transformations at some of the most demanding technology and consumer companies on the market: HTC, LG Electronics, Hoya Vision Care and Alain Afflelou. From building global categories and brands to digitalising channels and managing organisational change, I learned to make business decisions in complex, multicultural environments under real pressure on results. And in every cycle I saw the same pattern: the decisions with the greatest impact on company value — talent, culture, reputation, proprietary knowledge — were the ones least measured; and those that were measured weren't managed.
The problem no one was solving
When it came time to defend those investments before a Board, the answer was always the same: "there isn't enough data." The finance team had no tools to calculate it, and the business teams lacked what they needed to govern it. The CEO sensed the value but couldn't argue it with rigour. And meanwhile, most of the company's real value stayed invisible on the balance sheet — treated as a soft cost rather than a strategic asset. That was the problem. And no one in the market was solving it with the financial rigour a board of directors demands.
Why I built IVALOR
I founded IVALOR Consulting to solve exactly that. Not as one more consultancy talking about intangibles in the abstract, but with a methodology of my own — IVALOR™ — and a technology platform with a deterministic valuation engine and dual validation, with a machine-learning layer ready* — the third lens activates with real data: each real case with historical performance data becomes proprietary evidence that sharpens the next estimates, building over time a proprietary reference model that no external benchmark can replicate. All of it designed to speak the language in which senior-leadership decisions are made: impact on the income statement. Today I work with leadership teams who want to govern what genuinely creates value in their organisations — and turn it into a competitive advantage that's measurable, defensible and scalable.
*Validation today is dual: a deterministic formula + a language model (LLM). The adaptive machine-learning layer is ready and constitutes the third lens, which activates once the ML model is trained and logged with real historical data.
