FEDERICO J. QUIJADA
AI GOVERNANCE ARCHITECTURE &
EXECUTIVE EDUCATION
Case Studies
Selected engagements, seen through today's AI governance work
Twenty years of technology programs in regulated, high-stakes settings taught me that what holds AI back is rarely capability. It is architecture. Each case below shaped how I approach AI governance and assurance today: controls designed in, accountability assigned early, and verification built into delivery rather than audited after the fact. Client names are withheld.
An AI/ML Platform for a Fortune 500 Airline
2019 · Aviation · AI/ML architecture and program leadership · $6M program
The challenge. Bring AI-driven, context-aware customer messaging to web and mobile channels at the scale of a major U.S. airline, drawing on customer data, flight information, and real-time operational events.
What I did. Led the design and deployment of the AI/ML system infrastructure: a scalable cloud architecture, a data pipeline that let models keep learning from customer and operational data, integration across web and mobile platforms, and security and compliance controls for sensitive customer information.
The outcome. Personalized, timely messaging across digital touchpoints, more efficient messaging operations, new upsell and cross-sell opportunities, and AI-driven insight for the management team.
Why it matters now. Production AI that touches customers succeeds or fails on its data pipeline and its controls. Designing compliance and monitoring into the architecture from day one, rather than bolting them on later, is the core of how I approach AI assurance today.
Cybersecurity Infrastructure for a Fortune 500 Financial Institution
2014 · Financial services · Security architecture and regulated deployment
The challenge. Strengthen backend defenses against emerging threats without disrupting operations, in a sector where downtime carries regulatory and financial consequences.
What I did. Led a cross-functional team of security, network, and compliance specialists to test and deploy a black-box intrusion detection and prevention capability: rigorous testing in a simulated environment, a phased rollout across data centers, real-time monitoring during cutover, staff training, and compliance checks against financial industry regulations.
The outcome. Delivered on time and within budget. Post-implementation analysis showed a 40% increase in threat detection and a 25% reduction in false positives.
Why it matters now. Putting an opaque system into a regulated environment is the same problem AI teams face today. Staged testing, measurable acceptance criteria, and compliance sign-off before scaling up are the habits I bring to AI governance work.
A Cloud and DevOps Curriculum for an R1 U.S. University
2021 · Higher education · Curriculum design for cloud and DevOps
The challenge. Close the gap between theory and practice for students heading into cloud engineering and DevOps roles.
What I did. Designed a hands-on course covering cloud architecture, infrastructure as code, containers and Kubernetes, CI/CD pipelines, cloud security, serverless computing, and microservices. Students worked in labs on AWS, Azure, and Google Cloud, studied enterprise case studies, heard from industry guest lecturers, and finished with a full-stack capstone built on cloud services and DevOps practices.
The outcome. A course that prepares graduates for cloud and DevOps roles with the tools and real-world scenarios they will meet on the job.
Why it matters now. The same practice-first design, grounded in enterprise cases, shapes the executive AI programs I teach at MIT Professional Education, Cornell, and Cambridge. Leaders govern AI better when they have worked through real decisions, not slides.
A Graduate Program for Working Professionals at a European University
2024 · Higher education · Graduate program design in technology leadership
The challenge. Build a master's program in project management that working professionals can complete alongside demanding jobs, and that keeps pace with digital transformation.
What I did. Designed the core curriculum (advanced methodologies, strategic technology leadership, financial management and KPIs, and cross-cultural leadership) and three specialization tracks: Digital Transformation, including AI and machine learning in project management; Agile and Lean; and Sustainability. Added a blended delivery model of weekend intensives, online modules, and an annual residency, plus industry-sponsored projects, a mentorship program, and a strategy capstone.
The outcome. A flexible graduate program that blends theory with practice, with AI and machine learning built into the digital transformation track.
Why it matters now. AI adoption is a leadership problem before it is a technical one. Building programs around real decisions, cases, and simulations is central to how I design executive AI education today.