Hosted by Chandra Wobschall and Paul Houtkooper

Hey JDE Connection listeners! We’re back with a fresh episode—and this one’s all about a topic that’s making headlines, stirring curiosity, and fueling debates: artificial intelligence and what it could mean for systems and business analysts.

What Even Is AI?

With all the buzz out there, it’s easy to feel overwhelmed. So, we started with a high-level breakdown:

• AI (Artificial Intelligence): Technology that mimics human capabilities—learning, understanding, reasoning, problem-solving.
• ML (Machine Learning): Algorithms that learn from data to make predictions or decisions.
• Deep Learning: A more advanced form of ML that uses neural networks to analyze massive datasets.
• Generative AI: Uses deep learning to create content (text, images, audio) based on prompts. Think ChatGPT, Copilot, Llama 3.

We also talked about the difference between generative AI (which helps you summarize, author, and create content) and predictive AI (used for things like anomaly detection, forecasting, or root cause analysis).

What Customers are Saying

Paul just got back from a JD Edwards strategy meeting in Houston, where he had some in-depth conversations with customers about how they’re approaching AI. No surprise, many IT teams are under pressure to deliver “something with AI,” even though they may not know where to start. We talked about how important it is to:

• Manage executive expectations
• Avoid using AI just because it’s trendy
• Start small with clear use cases

Don’t Overthink It: Find Quick Wins

We shared advice we’ve heard from other customers too: look for obvious opportunities. Don’t chase the most novel, complex use case first. Instead:

• Reimagine existing processes: Where do you already create content, summarize reports, follow procedures?
• Leverage JD Edwards tools: Some requests framed as “AI automation” can be solved with tools like Orchestrator that have been around for years.
• Demonstrate value fast: Quick wins build credibility and unlock more innovation.

Data: The Fuel (and the Filter)

No AI strategy succeeds without good data. We talked about how data governance, metadata, and structured tagging play a massive role in model accuracy and usefulness. Garbage in, garbage out.

Has your company acquired other companies, reorganized departments, or just let data sprawl? Time to clean house before you feed that info into a model.

Explainability = Trust

Another critical point—people want to understand where the AI output comes from. Whether it’s MRP or machine learning, if users can’t trace the logic, they won’t trust the result. Transparency is key to adoption.

Final Thoughts

We’re not AI data scientists—but as business leaders and product developers, we’re learning alongside you. Our role is to help JD Edwards customers make sense of the tech, understand the capabilities, and apply AI thoughtfully.

Got AI ambitions? Start with these steps:

1. Pick a use case that matters.
2. Check your data quality.
3. Build trust by explaining results.
4. Celebrate small wins.
5. Stay curious, but realistic.

Join the Conversation

What’s your take on AI in the JD Edwards world? Share your questions, stories, or experiments with us at thejdeconnection@questoraclecommunity.org.

Until next time, let’s keep learning, sharing, and—most importantly—laughing together. Toodles!

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