supply-chain data product portfolio spanning 15 operational systems
AI product leader · Aerospace systems thinker · Operator
AI systems that move from strategy to measurable operations
I’m Ogün Kargın. I bring an aerospace engineer’s systems mindset to AI—turning complex operational problems into trusted data foundations, agentic workflows, and enterprise products that people actually adopt.
Experience acrossThe Home Depot·Rolls-Royce·Airbus·Georgia Tech
engineers coordinated across analytics, ML, and GenAI workflows
quarterly commitment delivery across three consecutive quarters
users adopted a governed analytics product in three months
Selected work
Built for the messy middle.
I work where ambitious AI ideas meet fragmented data, cross-functional teams, and the realities of enterprise adoption.
01Agentic AI
Turning inventory signals into better decisions
Took an optimization agent from concept to production, automating order-level adjustments around purchase-order arrivals.
$1M annual savings · 2% in-stock lift02Data products
Scaling trusted analytics across supply chain
Built a governed semantic layer that standardized enterprise metrics and gave teams reusable, AI-ready access to operational data.
250+ users · 40% faster insight03Product leadership
Scaling delivery across teams
Established roadmaps, planning rhythms, dependencies, and success benchmarks across a complex AI and data portfolio.
>95% commitment delivery · 25 engineersAn aerospace mindset for AI
Engineer the whole system, not just the model.
See the system of systems
Connect the model to its data, infrastructure, governance, user workflow, and downstream operating impact.
Make the tradeoffs explicit
Use evidence to balance performance, cost, latency, scalability, reliability, and speed rather than optimize one dimension in isolation.
Build, test, and learn
Challenge assumptions early, ship in measurable stages, and iterate with engineers, data scientists, and real users.
Consulting focus
From AI ambition to an operating product.
I help leaders find the right AI opportunities, shape an executable roadmap, and build the data and evaluation systems required to scale.
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