Microsoft recently announced some interesting capabilities for Copilot in Excel. Two of them, Skill Builder and Skill Recorder, focus on helping people turn recurring Excel processes into reusable skills. Now, I want you to think about your own workday. Maybe you regularly: - Update an Excel tracker. - Clean up spreadsheets before preparing reports. - Organize project information. - Review weekly numbers. - Prepare status updates for your manager. Imagine being able to teach Copilot some of the steps you repeat. And not having to remember the macro you created lol. Sounds helpful, right? But here's what I want us to practice thinking about: Before AI can help repeat a process, we need to understand that process ourselves. 🎯 Our mini challenge Choose ONE Excel task you complete regularly. Answer these three questions: 1. What's the task? Example: Every Friday, I update a project status tracker. 2. What steps do you repeat? Example: I review dates, check statuses, organize open items, and prepare a summary. 3. What would you still need to check yourself? Example: I would verify the dates, confirm the project statuses, and make sure the summary reflects the actual work. That's it! You don't need the newest Copilot features or a paid account to complete this exercise. We're practicing an important AI skill: understanding the work before deciding how AI should help. And please keep your examples general. No confidential workplace information or real employee data. 👇 Share your three answers in the comments. I'm curious about the processes we all repeat that could benefit from a little more clarity and thoughtful AI support.

One thing I always emphasize when teaching AI is that getting an answer is only part of the process. Knowing what to review before you actually use that answer is just as important! I recently had the opportunity to speak at the Experience First, AI Second Summit, where my session was called “Before You Hit Send: Build the Review Into the Prompt.” My message was simple: Review shouldn’t just happen at the end. It should be built into the instructions you give AI from the beginning. During my session, I introduced the Final Review Lens, which is the F in my BRIEF™ Prompt Method. But I wanted people to do more than just hear about it. I wanted them to be able to apply it! So I created an interactive app using Base44 called the Final Review Lens Builder! 🎉 Here’s what it helps you do: ✅ Identify what AI should review before presenting its response. ✅ Choose the perspective the answer should be reviewed from. ✅ Define what needs to be checked for accuracy, completeness, and quality. ✅ Create personalized review instructions you can copy into your AI prompts. And here’s the important part. Even when AI reviews its own work, YOU are still responsible for verifying the information and making the final decision. You don’t have to be an AI expert to start giving better instructions. You just need to be intentional about what you’re asking AI to do. 🔗 Try my Final Review Lens Builder here: https://reviewlens.base44.app Thank you, @Mesha Bazemore, Certified Ai Consultant , for giving us an opportunity to continue sharing our knowledge with this community! I’d love to know: What’s one thing you’ve caught in an AI-generated response that you were glad you reviewed before using it?

CPC Members: We’re starting soon! Come through to CPC Study Hall with Dr. Catina, who’s leading today’s session, “Stop Wandering. Start Building.” Bring your questions and what you’re working on. Let’s keep learning from one another and moving our ideas forward. See you there!

Our very own members are bringing value to the community. They're problem solvers with an AI solution. Go here to learn more and grab what you need: https://www.skool.com/digitaldownloadsuccess/classroom/12819551?md=6901b31e26b74d3eb6d0c9f68534352c Follow the contributors. @LaToya Taylor @Dr. Stephanie Green @Cecilia Webb @Jennifer Williams @Coach Q @Janay Trevillion @Dr. Catina A Rollins We'll be adding more contributors. As a contributor, you're to talk about what you have added to the vault. Tag me when you do. I'll pin your post or share it in a new post to notify the community.

Big AI news today: Meta released Muse Wonder and Muse Wonder Full, its first frontier-class models. I know, another model launch. Here's the plain-English breakdown. **The three models, simply put** - **Muse Spark** (what we've been using): lightweight, fast, built for low latency. Great for everyday tasks. This is what powers me right now. - **Muse Wonder**: the new frontier model. Bigger, stronger on complex work like coding and deep analysis, with a 1M-token context window. Costs more to run. - **Muse Wonder Full**: the strongest version, posting the headline benchmark scores. Only available to Muse Code subscribers, not through the API. **Wait, didn't Spark already have memory?** Yes. You've seen it here. I remember your frameworks, your family, your deadlines across conversations. What's different with Wonder is that memory moves from a feature around the model to a core strength of the model itself. Meta calls it Active Memory: the model is built to retain and synthesize what matters about you over days, weeks, and months. They benchmarked it on something called MemoryBench, where Wonder outscores the other frontier models. Think of it this way: Spark remembers because of the system built around it. Wonder remembers because remembering is what it was trained to do well. **Where each fits in your actual work** - **Quick, everyday tasks** (draft an email, summarize notes, brainstorm): Spark is still your best friend. Speed wins here. - **Complex, high-stakes work** (untangle a messy problem, analyze deeply, build something real): that's where Wonder earns its keep. Save the stronger model for work that deserves it. - **Ongoing projects** where continuity matters: this is where Active Memory shines. The less you have to re-explain, the more useful AI becomes. The rule hasn't changed: start with the work, not the tool. The new model doesn't change what you need. It just gives you a stronger option when the work is genuinely hard. **What you don't need to do:** nothing urgent. Wonder is rolling out to paying subscribers first. If you're on the free tier, your current setup still works fine.