AI Strategy & Value Realization
Defining where AI changes the business model and building the roadmap, business cases and measurement systems that turn ambition into EBITDA.
AI · Data · Strategy · Impact
Transforming
intelligence into
real enterprise value.
CIO · CDO · AI Executive · Researcher · Board & C-suite Advisor
I help organizations convert AI and data into decisions, actions and measurable business outcomes through leadership, governance and execution at scale.
- EBITDA impact generated
- USD 40M+
- EBITDA impact generated
- ROI on Industrial AI programs
- 300%+
- ROI on Industrial AI programs
- Multi-industry AI solutions deployed and in operation
- 600+
- Multi-industry AI solutions deployed and in operation
- Technology & data professionals led
- 170+
- Technology & data professionals led
- Driving strategy, innovation & value
- 20+ years
- Driving strategy, innovation & value
Career & industry experience
- Honeywell
- Agrosuper
- KPMG
- Oncoger.ai
- IBR LATAM
- Equifax
- Navigo Mining
- Universidad de Chile
- Harvard-Smithsonian CfA
- Chalmers University of Technology
- Universidad de los Andes
- Universidad Diego Portales
- CORFO
- Instituto Balseiro
Leadership, transformation and advisory experience across multiple industries, technology, healthcare and academia.
01About
An executive who understands AI from the boardroom to the algorithm.
From astrophysics and machine learning research to the boardroom.
Claudio Barrientos is a Data & AI executive with more than 20 years leading enterprise-scale transformation in C-level roles: Chief Information Officer, Chief Data Officer, Regional Head of Data Science & Innovation, and startup co-founder & CTO. He has served the mining, food & consumer, healthcare, telecommunications and consulting industries across Latin America.
His work sits at both ends of transformation. With boards and executive teams, he defines where AI and data change the business model, and where they do not. With the teams he leads, he builds the capabilities that turn that strategy into competitive advantage: operating models, Centers of Excellence, data and AI governance, cybersecurity, and agile ways of working, with Scrum, OKRs and other methodologies, that keep execution fast and accountable.
Trained as an Electrical Engineer with graduate studies in Electrical Engineering and Astronomy at the University of Chile, and doctoral research at the Harvard-Smithsonian Center for Astrophysics and Chalmers University of Technology, he is one of Chile's pioneers in translating AI and machine learning from academic research into industrial applications.
“Technology should stop being a support function and become a growth engine.”
Claudio Barrientos
From astrophysics and machine learning research to the boardroom.
An intellectual trajectory
- 01
Astrophysics & research
Terahertz photonics and instrumentation at Universidad de Chile, Chalmers and the Harvard-Smithsonian CfA.
- 02
Industrial AI & data science
Among the first machine learning applications in Chilean mining; analytics practices built at Equifax and KPMG.
- 03
CIO/CDO & enterprise transformation
Technology and data leadership at scale: operating models, governance, cybersecurity and measurable EBITDA impact.
- 04
AI leadership & value realization
Industrial AI agents, healthcare AI and a research program on how intelligence becomes enterprise value.
From the boardroom to the algorithm
Boardroom
Strategy · Governance · Capital allocation · Transformation · Leadership
Operating model
Organizations · Processes · Digital Factories · CoEs · Agile · Scrum · OKRs
Technology
AI · Data · Cloud · Architecture · Machine learning · Agentic AI
Research
Scientific method · AI research · ML research · 20+ peer-reviewed publications
20+ peer-reviewed publications
Executive leadership grounded in research
Research background spanning astronomy, engineering, artificial intelligence and applied machine learning. Boardroom experience combined with scientific and technical depth.
At a glance
- Roles
- CIO · CDO · CTO · Regional Head of Data Science & Innovation · Board & C-suite advisor
- Education
- M.Sc. Electrical Engineering · M.Sc. Astronomy (Universidad de Chile) · Ph.D. studies, Chalmers & Harvard-Smithsonian CfA
- Industries
- Mining · Food & consumer · Healthcare · Telecommunications · Consulting
- Research
- 20+ peer-reviewed publications · Journal of Geriatric Oncology · Hydrometallurgy · SPIE
- Recognition
- Fellow, Harvard-Smithsonian CfA · CONICYT Doctoral Scholarship · CORFO Crea y Valida · CORFO Precision-IA
- Languages
- Spanish (native) · English (advanced)
02Expertise
Where I create value.
Six areas where executive judgment, technical depth and research rigor converge.
AI & Data Governance
Responsible AI, DAMA-based data governance and operating models that give boards control without slowing execution.
Industrial AI & Digital Twins
ML-driven adaptive control, computer vision and simulation for complex non-linear processes in mining and manufacturing.
Generative & Agentic AI
Operational AI agents, RAG architectures and knowledge platforms that integrate thousands of real-time signals into decisions.
Technology Operating Models
Redesigning technology organizations, Digital Factories and Centers of Excellence to cut delivery time and cost while raising service quality.
Healthcare AI
Clinical decision-support platforms for oncology and oncogeriatrics, externally validated and deployed across hospitals in Latin America.
03Experience
Impact, not a chronology.
Selected cases, each framed as problem, intervention and result.
300%+ ROI
2024 – Present
Mining
Honeywell
Regional Head of Data Science & Innovation, Mining CoE LATAM
- Problem
- Complex, non-linear mining processes with high variability and long AI delivery cycles.
- Intervention
- Built a 35+ multidisciplinary AI team and deployed ML-enabled adaptive control, computer vision, digital twins and one of LATAM's first operational AI agent platforms.
- Result
- ROI above 300% with payback under one month at three of the world's largest mining companies; 600+ solutions across 10+ sites; delivery time cut from 8 to 3 months.
USD 40M EBITDA
2022 – 2023
Food & consumer
Agrosuper
Chief Information Officer — Head of Technology & Data
- Problem
- A USD 5B+ multinational needing an end-to-end technology, data and cybersecurity transformation.
- Intervention
- Redesigned the operating model into 8 specialized functions, launched DAMA-based data governance and modernized cybersecurity under CIS18.
- Result
- ~USD 40M EBITDA impact; long-term cost base optimized by 35%+; ~10% increase in customer NPS.
10+ hospitals
2022 – Present
Healthcare
Oncoger
Co-Founder, CTO & CDO
- Problem
- Physicians lacked evidence-based tools to decide treatment intensity for older adults with cancer.
- Intervention
- Architected a multivariable ML and agentic clinical decision-support platform, backed by CORFO.
- Result
- Deployed in 10+ hospitals across Latin America; ~0.90 ROC AUC in external validation; published in the Journal of Geriatric Oncology (2026).
+20% margin
2020 – 2022
Services / BPO
IBR LATAM
Head of Advanced Analytics & Data-Driven (CDO), Executive Committee
- Problem
- Declining operational performance across BPO operations.
- Intervention
- Analytics-driven contact strategy, segmentation model and an Integrated Operations Center uniting analytics, BI and operations.
- Result
- Operational margins improved by more than 20%.
30+ professionals
2016 – 2020
Consulting
KPMG Chile
Senior Manager — Head of Advanced Analytics & Big Data
- Problem
- No advanced analytics or data governance capability inside the firm.
- Intervention
- Founded the Advanced Analytics and Data Governance practices from scratch, plus the Insight Center technology showcase.
- Result
- 30+ professionals across 6 service lines; AI solutions delivered in 5+ industries, from fraud detection to mining optimization.
Earlier: Head of Analytics, Telco at Equifax; CTO & Head of Innovation at Navigo Mining, pioneering some of the first machine learning applications in Chilean industrial mining.
04AI 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
Two conceptual entry points
AI Value Realization Theory (AVRT)
How organizations convert AI potential into measurable and compounding enterprise value.
ReadAI Leadership Debt
The organizational gap created when AI capabilities evolve faster than leadership's ability to govern, integrate and convert them into value.
ReadThe relationship: AI Leadership Debt is the upstream constraint that throttles every link of the AVRT conversion chain. Measured as ALDI, it is the leading indicator the theory uses to explain persistent differences in AI ROI.
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.
Three research streams
AI Value Realization
Why do similar AI capabilities produce such different returns?
Organizational and economic research on how AI investment converts into decisions, actions and measurable value. Includes AVRT and its measurement system, the debt concepts (leadership, architecture, governance) and capital allocation across AI portfolios.
- AI Value Realization Theory (AVRT)
- AI Leadership Debt
- Architecture Leadership Debt
- Governance Debt
- AVRT Measurement System
- AI Capital Allocation Theory
- AI Portfolio Decision Framework
- Executive AI Literacy
Industrial AI
How do machine learning and agents change the control of complex physical processes?
Applied research at the frontier of process control and AI: ML-driven adaptive control (MPC/APC) for non-linear mining processes, digital twins and operator training simulators, computer vision at scale, and operational AI agents integrating thousands of real-time signals.
- Evolution-Ready PoC
- Expert Validation Loop
- Agent Harness Engineering
Healthcare AI
Can machine learning support treatment decisions for older adults with cancer?
Clinical decision support in oncology and geriatric oncology: multivariable ML models for chemotherapy eligibility and treatment intensity, external validation across hospitals, and agentic clinical platforms. Developed through Oncoger.ai and the CORFO-funded PRECISION-IA program.
- Expert Validation Loop
05Insights
Ideas that shape how organizations capture value from AI.
Articles, frameworks and research at the intersection of leadership, governance and technology.
AI Value Realization Theory (AVRT)
4 pieces
AI Leadership & Executive Literacy
3 pieces
AI Portfolio & Strategy
4 pieces
Operating Models & AI Governance
3 pieces
Agentic & AI-native Engineering
2 pieces
Latest articles
Coming next in the series
- How AI Leadership Debt Destroys AI ROI
- The Hidden Cost of AI Governance
- Industrial Agentic AI
- Why Short-Term AI Optimization Can Destroy Long-Term Enterprise Value
Selected research
20+ peer-reviewed publications in Hydrometallurgy (Elsevier), SPIE Astronomical Instrumentation and Space Terahertz Technology, 2005–2016.
- Navarrete G., Martínez G., Barrientos C., et al. “ML-Based Systemic Therapy Decision Support in Older Adults with Cancer.” Journal of Geriatric Oncology, 2026.
- Barrientos C., Neira N., Olivares J., et al. “Non-linear Adaptive APC: ML-Driven Optimization for Copper Mining.” APC Peru, 2025.
- Barrientos C., et al. “Adaptive APC Strategy using ML for SAG Grinding.” APC Peru, 2024.
06Speaking
Speaking, teaching and executive education.
Keynotes, board sessions and workshops that make AI understandable for a CEO and rigorous enough for a specialist.
Keynote topics
- 01
Translating AI into Enterprise Value
How boards and executive teams decide where AI changes the business model, and how to measure it.
Read - 02
AI Leadership Debt
The hidden liability that erodes AI ROI, and how to pay it down.
Read - 03
Governing AI in the Enterprise
Responsible AI, data governance and operating models that scale.
Read - 04
Industrial & Agentic AI
From adaptive control to operational agents: lessons from mining and manufacturing.
Read
Academia & institutions
- Professor, Electrical Engineering · Universidad de los Andes · 2009–2016
- Professor, Civil Engineering (CS & Telecom) · Universidad Diego Portales · 2016–2022
- Invited Evaluator, AI & Data Science · CORFO – Innova Chile · 2013–2022
- Doctoral Researcher, THz Photonics · U. de Chile / Chalmers / Harvard-Smithsonian CfA · 2004–2014
07Currently exploring
Open questions on the research agenda
Four questions currently shaping the work. Comments, counter-evidence and collaboration are welcome.
- 01
Can AI value realization be measured as a conversion system?
AVRT Measurement System
- 02
Can AI Leadership Debt explain persistent differences in AI ROI between organizations?
AI Leadership Debt
- 03
How should organizations allocate capital across AI initiatives under organizational uncertainty?
AI Capital Allocation
- 04
How should AI PoCs be architected to maximize learning without creating Architecture Leadership Debt?
Evolution-Ready PoC · Architecture Leadership Debt
08Contact
Let's create measurable value from AI.
For board advisory, executive AI strategy, speaking or research collaboration. Selective by design: a conversation first.
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Santiago, Chile · Serving Latin America and beyond