Edith Heroux

Three years ago, I watched our first AI-powered feature fail spectacularly in production. We had brilliant data scientists, cutting-edge algorithms, and enthusiastic stakeholders. What we didn't have was a systematic approach to building, testing, and deploying AI capabilities. That painful experience became the catalyst for developing robust frameworks that transformed how we approach machine learning integration. The journey from ad-hoc experimentation to disciplined execution taught me that success in artificial intelligence isn't just about algorithmic sophistication—it's about the infrastructure, processes, and culture that surround those algorithms. The turning point came when we realized that AI Product Development Pipelines require fundamentally different thinking than traditional software development. Unlike conventional applications where requirements are relatively stable and outputs predictable, AI systems exhibit probabilistic behavior that demands continuous ...

Three years ago, I watched our IT operations team struggle under the weight of managing infrastructure for a rapidly growing organization. Alert fatigue was real, incident response times were climbing, and our best engineers were spending weekends manually troubleshooting issues that seemed to multiply overnight. That experience became the catalyst for our journey into artificial intelligence-powered operations management, and the lessons we learned along the way fundamentally changed how we approach technology infrastructure today. The transformation began when we committed to exploring AI in IT Operations as more than just a buzzword. We needed genuine solutions to concrete problems: reducing mean time to resolution, predicting failures before they impacted users, and freeing our teams from repetitive tasks that consumed their creative energy. What followed was a two-year implementation journey filled with unexpected challenges, surprising victories, and insights that only come from...

When our e-commerce platform first began exploring artificial intelligence solutions three years ago, we had no idea how transformative the journey would become. What started as a modest experiment with product recommendation engines evolved into a comprehensive transformation powered by generative AI technologies. The lessons we learned along the way—through both spectacular successes and humbling failures—offer valuable insights for any retailer considering this powerful technology. The landscape of online shopping has fundamentally shifted, and Generative AI in E-commerce stands at the center of this revolution. Our company's experience implementing these systems revealed that success requires more than just deploying new technology—it demands a fundamental rethinking of how we understand and serve customers. This article shares the real stories, mistakes, and breakthroughs from our three-year journey into AI-powered retail. The Journey Begins: Our First Hesitant Steps with Gen...

The retail industry faces unique pricing challenges that distinguish it from other commercial sectors. Retailers manage thousands to hundreds of thousands of SKUs simultaneously, each with distinct demand patterns, competitive dynamics, and margin profiles. Customer expectations for value vary dramatically across shopping occasions, channels, and even times of day. Traditional pricing approaches based on cost-plus formulas or periodic competitive surveys fail to capture this complexity, resulting in significant value leakage through suboptimal prices. The emergence of artificial intelligence capabilities specifically designed for retail pricing contexts is fundamentally reshaping how leading retailers approach this critical profit driver, creating new paradigms for balancing customer value perception with financial performance. Modern AI-Powered Dynamic Pricing systems in retail environments process diverse data streams to inform pricing decisions. Point-of-sale transaction data provi...