The Journey
I've been building data-centric and AI-powered products for over 10 years across enterprise organizations, SaaS platforms, and Fortune 50 environments.
AI-Powered Search Systems
Built systems aggregating data across 6+ disparate repositories using Azure Cognitive Search, serving thousands of users with intelligent content discovery.
Machine Learning Platforms
Developed ML-enhanced platforms serving Fortune 50 organizations with automated video, audio, and text analysis capabilities.
Cross-Platform Ecosystems
Created tightly integrated product lines serving 6,000+ users with nearly 100% usage growth and seamless user experiences.
Scalable Data Architectures
Migrated legacy systems to modern microservices, enabling real-time data aggregation and enterprise-grade compliance.
What Makes Me Different
Most AI product leaders come from one of two worlds: pure tech (engineers who don't understand users) or pure business (strategists who've never shipped anything).
Technical Leadership
- Led 32+ person technical teams building AI systems
- Managed $5M+ budgets and made critical trade-offs
- Worked with data scientists to architect ML pipelines
User-Centric Focus
- Sat with users to understand real needs vs. cool demos
- Bridge gap between "technically possible" and "actually matters"
- Speak language of both technical teams and executives
I know where AI projects go wrong because I've seen every failure pattern:
• Teams building impressive demos that never make it to production
• Organizations buying AI vendor promises without clear use cases
• Products with ML models nobody trusts because they can't explain the output
• Roadmaps paralyzed by "we need more data" or "let's add one more feature"
The AI Product Shipping Framework
Most companies approach AI backwards—they start with technology and hope to find a use case. Here's how I help teams ship:
Reality Check
- • Validate the use case (is this actually an AI problem?)
- • Assess data readiness (do you have what you need?)
- • Define success metrics (how will we know it works?)
- • Identify the MVP (what's the smallest shippable version?)
Foundation First
- • Get the data pipeline right before any ML work
- • Build trust mechanisms (how will users trust the output?)
- • Design the human-AI workflow (where does AI help vs. where do humans decide?)
- • Create the feedback loop (how does the product improve?)
Ship & Iterate
- • Launch the simplest version that delivers value
- • Measure what matters (not vanity metrics)
- • Build the improvement engine (make iteration systematic)
- • Scale based on proof (expand what works, kill what doesn't)
Sustain & Grow
- • Operationalize monitoring (catch issues before users do)
- • Build the roadmap from real usage (not theoretical features)
- • Evolve the team structure (as product matures)
- • Plan the next AI capability (based on proven foundations)