Articles & research
Ideas that shape how organizations capture value from AI.
Articles, series and frameworks on how organizations capture value from AI, grouped by topic. First published on LinkedIn; reproduced here in English, Spanish and Portuguese.
AI Leadership & Governance Research
How does AI actually become enterprise value?
A research program exploring why organizations with access to similar AI technologies produce radically different economic outcomes.
The working hypothesis is that AI does not create value directly: it creates intelligence potential, and value depends on an organizational conversion chain that can be observed, measured and governed. The frameworks below are components of that single agenda, not a catalogue of independent consulting tools.
The causal chain at the center of the program
One research agenda, eleven connected frameworks
Each framework addresses one point where the conversion chain breaks: the leadership that cannot yet govern AI, the architecture and governance debt that accumulates, the way pilots are built, the operating model that scales them, the measurement that makes value visible, and the capital allocation that reinvests it.
From research to business value
Three frameworks, each tied to a concrete business problem. Presented as a program in development, not a validated methodology.
| Framework | Business problem | Question it answers | How it is used in an engagement |
|---|---|---|---|
| AI Value Realization Theory (AVRT) | AI is adopted but the value does not show up in the income statement. | Where in the chain intelligence → decisions → actions → value is the leak? | Conversion funnel (DCR, ACR, VCR) measured by cohort of alerts; traceability from AI capabilities to economic results. |
| AI Leadership Debt | Technology arrives before the organization: initiatives with no owner, no indicator, no decision rights. | Why does an organization fail to capture value even when it has the technology? | Diagnosis against the five observable signals; ALDI as the upstream indicator that throttles every conversion rate. |
| AI Capital Allocationin development | AI budgets are spread across projects without a portfolio logic or a reinvestment rule. | What fraction of AI value should be reinvested, and into which capabilities? | Three Horizons and the AI Portfolio Decision Framework to allocate; AIRR to track reinvestment. |
Three research streams
Industrial AI
How do machine learning and agents change the control of complex physical processes?
Healthcare AI
Can machine learning support treatment decisions for older adults with cancer?
Where to start
Topic
AI Value Realization Theory (AVRT)
Why AI adoption does not automatically become value, and how to measure the conversion.
AVRT is a framework in development that explains the gap between AI potential and realized value. Its core is a conversion chain (Intelligence → Decisions → Actions → Value → Reinvestment) instrumented with five metrics: ALDI, DCR, ACR, VCR and AIRR.
Topic
AI Leadership & Executive Literacy
Leaders can delegate execution. They can no longer delegate understanding.
The next competitive gap will not be between companies that use AI and those that don't, but between leadership teams that understand how AI changes operating models and those that still treat it as a technology layer.
Topic
AI Portfolio & Strategy
From project to asset: managing AI as a capital allocation problem.
A series on why "which AI projects do we have?" is the wrong question: the seven components of an AI portfolio, the Three Horizons, the limits of two-axis matrices and a proposed AI Portfolio Decision Framework.
Topic
Operating Models & AI Governance
AI doesn't create value living in a notebook. It creates value inside the operating system of the company.
Why PoCs fail in the operating model rather than in the lab, what governing AI actually means beyond regulating access to tools, and why federated data ownership is the foundation for agentic AI.
Topic
Agentic & AI-native Engineering
The problems don't come from the AI. They come from how we design the system around it.
Field notes from building agents and AI-assisted software in industry and healthcare: agent harness engineering, and the Expert Validation Loop that validates the knowledge agents produce, not just the code.