Leveraging AI/ML in Product Development

As product development evolves in the AI era, I've been actively implementing machine learning and artificial intelligence to transform how we understand users and make product decisions. My experience goes beyond theoretical knowledge—I've applied these technologies to solve real business problems and create measurable impact.

Strike Analytics: AI-Powered Insight Generation

At Strike, we implemented a sophisticated AI/ML framework that fundamentally changed how business insights are generated and applied.

Cross-Modal Data Integration

We developed a system that could seamlessly integrate and analyse diverse data types:

Qualitative Sources: User interview transcripts, unmoderated testing videos, support conversations, survey responses, and open-ended feedback
Quantitative Metrics: User analytics, business revenue figures, marketing performance data, conversion funnels, and engagement patterns
External Context: Market trends, competitor performance, and seasonal factors

Business Impact

The AI/ML systems we developed delivered concrete business value:

23% improvement in prediction accuracy for user conversion compared to traditional analytics
18% reduction in customer acquisition costs through more precise targeting
15% increase in average order value through better understanding of purchase motivations
96% reduction in time-to-insight (from 24 hours to 1 hour)

Beyond Implementation: Strategic Direction

Ethical AI Implementation

I've developed frameworks for ensuring AI systems are:

Transparent in how they use and analyse data
Free from harmful biases that could impact user experiences
Respectful of user privacy while still delivering value
Designed to augment rather than replace human decision-making

Cross-Functional AI Literacy

I've led initiatives to build AI literacy across product teams:

Created accessible training programs that demystify AI capabilities
Developed collaboration models between data scientists and product designers
Established shared vocabulary for discussing AI features and limitations

Future Direction

My ongoing development in Python and ML is focused on:

Building more sophisticated predictive models for user behavior
Creating design systems that can adapt to individual user needs
Developing frameworks for testing and validating AI-driven features
Exploring generative design approaches that expand creative possibilities