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Rodrigo Krüger

Work

What I lead, build and deliver.

Twenty years across manufacturing, agribusiness, food and beverage, apparel and footwear, beauty and cosmetics, retail and e-commerce, logistics, utilities, financial services, life sciences and healthcare. The portfolios and programs below are what I am responsible for now: what each one is, what it covers, and which part of it is mine.

Corporate experience

Leadership

Enterprise AI portfolio

In a large company the model is the small part. Around it sit the data it is allowed to see, the systems it has to integrate with, the evidence that it is good enough to trust, and whoever keeps it working a year from now.

My responsibility

I lead the Data & AI practice at NTT DATA with a team of 15, accountable for an AI solution end to end: choosing the problem worth solving, agreeing what counts as working, designing how it integrates and what it may see, funding it, and running it once it is live.

Scope

  • Which problems are worth solving, and what counts as working before anything is built
  • Data access, quality and the legal basis for using it
  • Architecture, tooling and integration with the systems the process already runs on
  • Acceptance criteria and evaluation agreed with the business before go-live
  • Cost, ROI and total cost of ownership, including what inference costs at volume
  • Life after go-live: monitoring, drift, retraining, support and rollback
  • Schedule, dependencies and delivery risk across the programme
  • The commercial model for AI services: offerings, pricing and deal structure

Adoption

AI Center of Excellence

Making AI work across a whole operation is a different problem from building one solution. Teams need a common way of working, and clients need the same standard from one engagement to the next.

My responsibility

I own the framework for how AI is adopted across NTT DATA in Brazil, and I run the AI Center of Excellence: the reference architectures, delivery standards and enablement that let every team build AI the same way.

Scope

  • The AI Center of Excellence for the Brazilian operation
  • Reference architectures and delivery standards for AI work
  • Enablement of delivery teams across the operation
  • Reuse of what works: patterns, assets and lessons between engagements

Enterprise software

Guepardo AI

Guepardo is a suite of solutions built for CFOs of companies that run SAP — tax reporting, electronic fiscal documents, global trade, analytics and AI. More than 550 customers use it, and at least 9% of Brazil's GDP passes through it.

My responsibility

I lead Guepardo AI: what gets built, in which products, how it is packaged and priced, and how it reaches customers.

Scope

  • Predictive analytics for fiscal and financial operations
  • Deep learning applied to document and transaction-heavy processes
  • Generative AI embedded in existing business workflows
  • AI agents that operate against real system data and controls
  • The commercial model for AI: packaging, pricing and go-to-market

Group role

Global Innovation Team

NTT DATA runs its innovation agenda across more than 60 countries. The Global Innovation Team is where those operations decide together what is worth building, what is worth adopting, and what gets shared between markets.

My responsibility

I represent the Brazilian operation on that team, taking what works here into the group's agenda and bringing what the group builds back to the Brazilian market.

Scope

  • Brazil's voice in the group's innovation and AI agenda
  • Evaluation of emerging AI capability before it reaches client work
  • AI use cases and delivery patterns exchanged between countries
  • Perspective from a market with unusually complex tax and fiscal requirements

Execution

Delivery of large technical programs

AI reaches production through the same delivery machinery as any other change to a system a company depends on.

My responsibility

Before Data & AI, I ran enterprise delivery programs of up to 80 people, accountable for scope, budget, quality and operational continuity.

Scope

  • Program and delivery management in complex enterprise environments
  • Technical leadership across architecture, engineering and operations
  • Systems where downtime and error carry direct financial cost

Governance

Governance, privacy and responsible adoption

Much of what AI touches in a large organization is regulated, audited, or carries legal accountability.

My responsibility

I specify model risk, data sensitivity, access design and human oversight as implementation requirements, before the build starts. This is where my professional and academic work overlap most directly.

Scope

  • Privacy and data-protection constraints designed into AI systems
  • Traceability and auditability of AI-supported decisions
  • Public commentary on responsible AI adoption and its limits

Industries

Twenty years of enterprise work, across these sectors.

  • Agribusiness
  • Apparel and footwear
  • Automotive
  • Beauty and cosmetics
  • Chemicals
  • Financial services
  • Food and beverage
  • Life sciences and healthcare
  • Logistics
  • Manufacturing
  • Mining and metals
  • Public sector
  • Pulp and paper
  • Retail and e-commerce
  • Utilities