Conative AI Founder on Connecting Marketing, Inventory, and Operations with AI Forecasting

How can companies stop losing revenue because marketing, inventory, and operations are working from disconnected data? In this podcast hosted by EY Chief Platform Officer Justin Leibow, Conative.ai Founder Mike Le discusses how AI forecasting helps organizations align teams, improve decision-making, and increase cash-flow efficiency. He also explores why clean, connected data matters more than models alone, and how AI agents are reshaping the way product, marketing, and inventory teams collaborate to act faster and smarter.

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Show Notes

1. Many companies lose significant revenue because marketing, inventory, and operations decisions are made in silos rather than from a shared forecasting foundation.
2. Demand forecasting improves dramatically when AI integrates multiple data streams instead of relying on spreadsheets, rules-based planning, or intuition.
3. Deep learning models outperform traditional forecasting approaches because they can incorporate many more contextual signals simultaneously.
4. The biggest obstacle to successful AI adoption is rarely the model itself—it is the quality, structure, and availability of data.
5. Building a working AI prototype can take months, but building a reliable production-grade data infrastructure can take a year or longer.
6. Brands often operate with conflicting or incomplete datasets across platforms like ecommerce backends, ad channels, analytics systems, and ERPs.
7. Forecast accuracy directly affects inventory health, which in turn shapes marketing performance and cash flow outcomes.
8. Many organizations unknowingly lose revenue because top-selling SKUs frequently go out of stock while lower-performing inventory accumulates.
9. Aligning marketing strategy with inventory availability can produce meaningful revenue increases without increasing marketing spend.
10. AI forecasting systems must include risk-control mechanisms so businesses can trust outputs even when operating at scale.
11. Inventory planners initially hesitate to rely on AI because their roles carry high financial responsibility and risk exposure.
12. Trust in AI increases quickly when teams compare their manual forecasts with machine-generated forecasts over a short evaluation period.
13. AI can replicate hours of expert forecasting work in minutes while maintaining comparable accuracy levels.
14. The most effective positioning for AI in operations is as a decision-support partner rather than a replacement for human judgment.
15. Specialized AI models remain necessary for forecasting tasks because large language models are not optimized for numerical prediction workflows.
16. Organizations that start their AI journey without first understanding their data landscape often struggle to achieve meaningful results.
17. Data pipelines that unify signals from marketing, ecommerce, and inventory systems create the foundation for reliable forecasting automation.
18. AI dashboards and conversational AI agents reduce the need for manual analysis by allowing teams to interact with insights directly.
19. Teams that adopt AI tools early gain a competitive advantage over organizations still relying primarily on spreadsheets and manual planning.
20. Over time, AI systems are evolving toward acting as digital teammates capable of executing structured analytical tasks across business workflows.