Welcome to the AI Agent & Copilot Podcast, analyzing the latest AI Copilot and agent developments from Microsoft and its partners, delving into customer use cases, and exploring how AI plus the Cloud helps customers reimagine their business.
In this episode, Tom Smith talks with two speakers about their upcoming sessions at AI Agent & Copilot Summit: “Fireside Chat: Data Innovation Profile” and “Image Tagging AI Case Study: A Customer Story.“
- Michael Simms, VP, Data & AI, Columbus Global
- Miyoshi Tokuno, Director of Data Control at Mad Engine
Highlights
Overview of Mad Engine and Columbus (01:15)
Tokuno explains that Mad Engine is a retail and print-on-demand graphic T-shirt company, focusing on e-commerce. She mentions her role in managing business data within the e-commerce system, including ERP systems like Dynamics systems. Simms provides an overview of Columbus, a global consultancy headquartered in Copenhagen, Denmark, with a strong presence in Dynamics products, data, and AI. Columbus has 24/7 global reach with development centers in various countries.
GenAI Project at Mad Engine (02:44)
Tokuno summarizes the GenAI work done with Columbus consulting. Mad Engine has over 60 customers, each with different requirements for upload sheets, which are essentially Excel sheets, and there’s a need for speed to market. Columbus used AI to automate the process of filling out 85% of the data, achieving a 96% accuracy rating. The AI project significantly increased the volume of designs Mad Engine can handle, from 300 designs initially to almost 2,000.
AI Agent & Copilot Summit NA is an AI-first event to define opportunities, impact, and outcomes with Microsoft Copilot and agents for mid-market and enterprise companies. It takes place March 17-19 in San Diego, CA. Register now to attend.
Key Objectives and Takeaways for the Summit (04:16)
Simms discusses the key objectives for the summit. He discusses the need for a proof of concept (POC) to minimize changes during the project. The project involved melding various technologies in the AI space, including data engineering and prompt engineering, to create a custom solution. The focus was not just on GenAI but also on creating a comprehensive technical solution for Mad Engine’s specific needs.
Key Learnings from Mad Engine’s Perspective (05:40)
Tokuno shares key learnings from Mad Engine’s perspective, emphasizing the importance of going through the POC process. Columbus helped Mad Engine identify aspects that were often overlooked, especially those with a human element. The primary focus shifted from financial savings to speed to market, which is crucial in the fast-paced e-commerce market. Columbus opened Mad Engine’s eyes to the capabilities of GenAI, leading to a reevaluation of their objectives.
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