Elli Peterson's TechCrunch

Three years ago, our customer retention rates were stagnating at 24%, and our recommendation engine was delivering generic suggestions that customers ignored 78% of the time. We knew something had to change, but we didn't anticipate that the answer would fundamentally reshape how we approach every aspect of our digital storefront. The introduction of artificial intelligence technologies into our platform didn't just improve metrics—it taught us lessons that transformed our understanding of what's possible in modern retail. The journey into Generative AI in E-commerce began with a failed experiment. We launched our first AI-powered product description generator in Q2 of 2023, expecting immediate improvements in conversion rates. Instead, we saw a 12% drop in add-to-cart actions within the first week. The descriptions were technically accurate but emotionally flat, missing the nuanced language that resonated with our customer segments. This early failure taught us our first ...

Three years ago, our category management team at a major FMCG company faced a recurring nightmare: we were consistently overpaying for packaging materials while simultaneously experiencing stock-outs that delayed new product introductions by weeks. Our procurement process relied heavily on spreadsheets, manual vendor negotiations, and gut instinct. Despite having talented buyers, we simply couldn't process supplier data fast enough to make optimal decisions. That's when we decided to pilot artificial intelligence in our procurement operations—a decision that would fundamentally transform how we approached everything from trade spend analysis to supplier relationship management. The journey into AI in Procurement wasn't smooth, but the lessons we learned along the way proved invaluable—not just for our procurement function, but across our entire supply chain collaboration framework. What started as a narrow experiment in automating purchase order generation evolved into a c...

After spending over a decade working across data visualization teams at enterprise BI platforms, I've witnessed firsthand how AI in Data Analytics has evolved from experimental add-ons to mission-critical infrastructure. What started as small-scale pilot projects testing predictive models has transformed into comprehensive augmented analytics platforms that fundamentally reshape how organizations extract value from their data lakes. This transformation hasn't been smooth or straightforward—it's been marked by valuable failures, unexpected breakthroughs, and hard-won insights that only emerge when theory meets operational reality. The journey toward mature AI in Data Analytics implementations has taught me that success depends less on having the most sophisticated algorithms and more on understanding the human dynamics of data storytelling, the organizational challenges of data governance, and the practical constraints of real-time analytics deployment. These lessons, learn...

Over the past decade, I've witnessed firsthand how organizations struggle and ultimately succeed with demand forecasting transformation. The journey from spreadsheet-based predictions to sophisticated machine learning models isn't just a technical upgrade—it's a fundamental shift in how businesses understand their customers, manage inventory, and compete in volatile markets. Through consulting engagements across retail, manufacturing, and distribution sectors, I've gathered stories that illuminate both the pitfalls and breakthroughs that define successful AI adoption in this critical domain. The most common misconception I encounter is that AI Demand Forecasting is primarily a technology challenge. In reality, the technical implementation represents perhaps thirty percent of the total effort. The remaining seventy percent involves organizational change, data culture transformation, and the delicate work of building trust between human experts and algorithmic recommenda...

When I first encountered the reality of supply chain disruptions five years ago, I witnessed firsthand how a single delayed shipment could cascade into millions in lost revenue. Traditional forecasting models failed spectacularly during unexpected market shifts, and manual interventions only compounded the chaos. That experience became my catalyst for exploring how artificial intelligence could fundamentally transform the way we anticipate, respond to, and prevent supply chain failures. The journey taught me that technology alone never solves operational problems—it's the strategic implementation combined with human expertise that creates lasting change. The transformation began when our organization committed to integrating AI Supply Chain Management systems across our entire distribution network. What started as a pilot program in a single warehouse evolved into a comprehensive ecosystem that now processes over 50,000 data points per minute, predicting demand fluctuations with 9...