Practical AI: Introduction to Artificial Intelligence
AI is no longer a buzzword; it’s embedded in the tools Dynamics users work with every day. In Part 1 of the “Practical AI series,” Kylie Kiser lays the groundwork for everything to come: from understanding what AI actually is to the types of AI appearing in Dynamics to the responsible principles Microsoft expects us to uphold as we build and use these tools.
Key Takeaways
- AI is only as good as your data: Machine learning systems are trained on your data — garbage in, garbage out. Don’t expect strong AI performance without clean, quality data behind it.
- Four types of AI to know: Predictive AI (forecasting, opportunity scoring, next-best-action), Generative AI (text, images, email drafting), Conversational AI (Copilots, chat interfaces), and Embedded AI (insights built into apps and workflows).
- Copilot, not autopilot: Users must review AI-generated outputs. As the platform moves toward agents and automation, human oversight remains critical — AI assists, not replaces judgment.
- Microsoft’s Responsible AI principles apply to you: Fairness, reliability, privacy, inclusiveness, transparency, and accountability aren’t just Microsoft’s responsibility — they’re yours too when building or deploying AI solutions.
- Accountability is non-negotiable: You are responsible for the performance of any AI model you use or build. Understanding what it does and why — and training your users accordingly — is part of the job.
