Translating AI into Enterprise Value

AI,
turned into
measurable enterprise value.

I help organizations convert AI and data into decisions, actions and measurable business outcomes through leadership, governance and execution at scale.

Executive profile Advisory & consulting

EBITDA impact delivered: ~USD 40M; EBITDA impact delivered
ROI on Industrial AI programs: 300%+; ROI on Industrial AI programs
AI solutions in operation in mining: 600+; AI solutions in operation in mining
in AI, data and technology: 20+ years; in AI, data and technology

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Career: industry and academia

CIO · CDO · AI Executive · Researcher · Board & C-level Advisor

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 industry, technology, healthcare and academia.

Results

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 multidisciplinary AI team of 35+ and deployed ML-enabled adaptive control, computer vision, digital twins and an operational AI agent platform.
Result
ROI above 300%, measured against baseline and validated by the client and its process team, with payback under one month at three of the world's largest mining companies; 600+ packaged AI solutions (computer vision, AI-based control, recommendation and optimization agents, among others), each running a specific process, 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.
7+ hospitals
2022 – Present
Healthcare
Oncoger.ai ↗
Co-Founder, CTO & Principal AI Investigator
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
PROTEGER, clinical decision support for systemic therapy in older adults with cancer: validating in 7+ hospitals in Chile, Peru and Brazil, over 1,000 patients included in the ML models, AUC 0.84 in external validation. The study was published in the Journal of Geriatric Oncology (2026).
+20% margin
2020 – 2022
Services / BPO
IBR LATAM
Chief Data Officer — Head of Advanced Analytics, 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, I was Head of Analytics, Telco at Equifax, and CTO & Head of Innovation at Navigo Mining, where I built some of the first machine learning applications in Chilean industrial mining.

Executive profile

More than 20 years taking AI from strategy to results.

Transforming businesses with applied AI and strategy.

I have spent more than 20 years working in technology, data and artificial intelligence. I am co-founder and CTO of Oncoger.ai, and before that I was Regional Head of Data Science & Innovation at Honeywell, Chief Information Officer at Agrosuper and Chief Data Officer at IBR LATAM. I have worked in mining, food & consumer, healthcare, telecommunications and consulting across Latin America.

My work sits at both ends of transformation. With boards and executive teams, I define where AI and data change the business model, and where they do not. With the teams I lead, I build the capabilities that turn that strategy into competitive advantage: operating models, Centers of Excellence, data and AI governance, cybersecurity, and agile practices (Scrum) and management by objectives (OKRs) that keep execution fast and accountable.

I trained as an Electrical Engineer, with graduate studies in Electrical Engineering and Astronomy at the University of Chile and doctoral research at Instituto Balseiro, the Harvard-Smithsonian Center for Astrophysics and Chalmers University of Technology. I was the first professional placed in industry through CONICYT's Advanced Human Capital Insertion in the Productive Sector program, applying machine learning to mining, and I have been taking AI from academic research into industrial operations ever since.

“Technology should stop being a support function and become a growth engine.”

Claudio Barrientos

Transforming businesses with applied AI and strategy.

Career trajectory

01
Astrophysics & research
Terahertz photonics and instrumentation at Universidad de Chile, Instituto Balseiro, 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.

At a glance

Roles: CIO · CDO · CTO · Regional Head of Data Science & Innovation · Board & C-level advisor
Education: M.Sc. Electrical Engineering · M.Sc. Astronomy (Universidad de Chile) · Doctoral research, Instituto Balseiro (Argentina), 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
Teaching and 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
Recent conference papers: “Non-linear Adaptive APC: ML-Driven Optimization for Copper Mining” · APC Peru, 2025 · “Adaptive APC Strategy using ML for SAG Grinding” · APC Peru, 2024
Specialties: AI Strategy & Value Realization · AI & Data Governance · Industrial AI & Digital Twins · Generative & Agentic AI · Technology Operating Models · Healthcare AI

Bio for events and press

Claudio Barrientos Z. is a Technology, Data and Artificial Intelligence executive focused on transforming businesses through applied AI. He is co-founder of Oncoger.ai and an advisor to boards and companies. With more than 20 years in AI, data and technology, he has led transformations in mining, food & consumer, healthcare and consulting across Latin America, and is developing AI Value Realization Theory (AVRT), a research program on how organizations turn AI into value.

How I work

Five ways to work together.

A conversation first; then a bounded scope with a defined outcome.

Boards, CEOs and executive committees

Board advisory and AI strategy

Where AI changes the business model, what to prioritize and how to measure the value. Sessions with the board or the executive committee, and support for the team that executes.

The problem: Nearly 9 in 10 companies use AI, yet only 37% see an impact on operating profit (McKinsey, 2026). The board approves AI investments with no financial indicator assigned and no way to know whether the value is being captured.
Scope: Working sessions with the board or the executive committee: where AI changes the business model, what to prioritize and how to measure it; support for the team that executes.
Deliverables: Diagnosis against the five signals of AI Leadership Debt, investment priorities with their business case, and a measurement system (conversion funnel and KPIs).
Expected outcome: AI investment decisions with an owner, an indicator and a measurement method defined.

Read: Beyond AI-First: Turning Artificial Intelligence into Business Performance · AI governance: the problem isn't that your teams use AI. It's that nobody knows who is accountable.

Discuss advisory

Companies, operations, data and technology teams

Consulting on AI monetization and applied AI

For companies on the path from AI pilots to measurable results: where the value is in the operation, the business case and its measurement, and applied AI in industry (adaptive control, agents, decision support) built with the teams that run the process.

The problem: Pilots that work in the lab and never reach the operation or the income statement.
Scope: The first full cycle in seven steps: objective and baseline, recurring decisions, diagnosis of the current flow, design of the AI-enabled process, bounded pilot, measurement and scale. In industry: adaptive control, agents and decision support built with the team that runs the process.
Deliverables: Map of the decisions that move the indicator, business case with sensitivity, a pilot in operation with success criteria, and measurement of value net of costs.
Expected outcome: A financial or operational indicator that moves in an attributable way, and the capability to repeat the cycle.

Read: Most AI PoCs don't fail in the lab. They fail in the operating model. · The AI Value Realization Gap: the gap your dashboard doesn't show

Discuss a consulting engagement

Companies, boards, data, legal and cybersecurity teams

Data governance and personal data protection

Data governance in federated and centralized models, its intersection with Chile's new Personal Data Protection Law (Law 21.719), cybersecurity for applied AI, and the conditions for monetizing AI without exposing the organization.

The problem: AI is deployed on data with no owner, no access rules and no clear legal basis; the new law requires accountable owners, consent and traceability, and the organization does not know where to start.
Scope: Diagnosis of the data governance model (federated or centralized), gaps against the law and DAMA, cybersecurity controls for AI systems (data, models, agents) and the design of the operating model that sustains them.
Deliverables: Data and ownership map, governance model with roles and decision rights, a risk-prioritized compliance plan for the law, and security controls for the AI cases in production.
Expected outcome: Governed data that AI can use with a legal basis and control, and a path to monetize it without exposing the organization.

Read: What is Data Mesh, and why it matters for AI?

Discuss data governance

Boards, executive teams, operations and technology teams

Speaking and board sessions

Keynotes, board sessions and executive workshops that make AI understandable for a CEO and rigorous enough for a specialist.

The problem: Leadership teams that delegate AI to technology and swing between overestimating and underestimating it.
Scope: A 45-minute keynote, a 90-minute board session or a half-day executive workshop, adapted to the industry and the organization's moment.
Deliverables: Board session: a diagnosis against the five signals and three priority decisions. Workshop: the roadmap for the first cycle in seven steps, with owners.
Expected outcome: A shared language about AI between board, management and technology, and concrete decisions on leaving the room.

Keynote · 45 min
Where is the value of AI in your industry, and how do you measure it?
What you take away: Where to look for value in your industry and how to measure it with the conversion funnel.
Board session · 90 min
What leadership debt is your organization accumulating with AI?
What you take away: A diagnosis against the five signals and three priority decisions.
Executive workshop · Half day
How do you govern AI without slowing execution?
What you take away: The roadmap for the first cycle in seven steps, with owners and decision rights.
Keynote · 45 min
What changes when AI agents enter operations?
What you take away: What changes in the operating model when agents enter the operation, and how to govern them.

Request a talk

Universities, research centers and companies that want to measure

Research collaboration

Validating AVRT and its frameworks with real organizational data, co-authorship and access to the working paper.

The problem: Value-realization frameworks need validation with real organizational data.
Scope: Measuring the five AVRT metrics in an organization or cohort, co-authorship and access to the AVRT working paper.
Deliverables: Measurement protocol, results report and, where appropriate, a joint publication.
Expected outcome: Evidence for the organization on where its AI value leaks, and one more validation case for the program.

Propose a collaboration

Selective by design: every engagement starts with a conversation and a clear question to answer.

Articles & 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.

Two entry points

AI Value Realization Theory (AVRT) — working paper under validation AI Leadership Debt

The causal chain at the center of the program

Where to start

Framework · Series AVRT SeriesAug 2026

AI Value Realization Theory: executive brief

The executive summary of the framework: the conversion chain, the five metrics (ALDI, DCR, ACR, VCR, AIRR), the weakest-link logic, the J-curve, the 10-question self-diagnosis, the positioning map, the critical metric by industry and the 3, 6, 12 and 18–24 month roadmap.

Read · 9 min read

ArticleOct 2026

Beyond AI-First: Turning Artificial Intelligence into Business Performance

You do not need to become an AI-first company to create value from artificial intelligence. But you do need to rethink how AI changes the way your business operates, decides and generates economic results: where the value is by industry, a worked case that takes gross margin from 30% to 31%, what Microsoft, McKinsey and BCG converge on, why value goes uncaptured (AI Leadership Debt) and how to know whether it is captured (the AVRT conversion funnel).

Read · 15 min read

ArticleJun 2026

The real problem with AI isn't the technology. It's that leadership doesn't understand it.

Three of the world's most rigorous research firms measured the same thing in 2025 and reached the same uncomfortable conclusion: adoption is massive, but value is rare. Leaders can still delegate execution, but they can no longer delegate understanding.

Read · 6 min read

Three research streams

AI Value Realization

Why do similar AI capabilities produce such different returns?

Read

Industrial AI

How do machine learning and agents change the control of complex physical processes?

Read

Healthcare AI

Can machine learning support treatment decisions for older adults with cancer?

Read Oncoger.ai ↗Paper (DOI) ↗

All articles and the research program

Contact

Let's turn your AI investment into measurable results.

Board advisory, consulting, data governance, speaking or research: write to me and let's start with a short conversation.

Executive opportunitiesBoard AdvisoryAI Monetization & Applied AI ConsultingData Governance & Personal Data ProtectionSpeakingResearch Collaboration

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Santiago, Chile · Serving Latin America and beyond

AI,turned intomeasurable enterprise value.