Premium AI is included in Google Workspace, built into popular apps like Gmail, Docs, Sheets, Meet, and more. Here a prompt to get you started. 🤖PROMPT: I am overwhelmed with not knowing where to start with emails I need to answer. What looks to be the most urgent in the last 5 days? Focus on anything client related. FOLLOW-UP PROMPT: Let's prioritize the 10 emails I need to answer first. TBH it did great with the first prompt but only identified 2 emails and just okay with the follow-up. 👉Give it a try and let me know what you think!

Why connectors in ChatGPT are a game-changer (and how to set them up) 🤖⚡ TL;DR: Connectors let ChatGPT securely access your real tools (Drive, Slack, Notion, CRMs, etc.) so you can stop copy-pasting and start working with live business data. Why this matters for CPG teams: - ⏱️ Save time: No more exporting files or pasting context - 📊 More accurate outputs: Uses your actual numbers, SKUs, and docs - 🔁 Repeatable workflows: Weekly reports, summaries, follow-ups - 🤝 Team consistency: One source of truth across ops, sales, marketing How to connect (5 steps): 1. Open ChatGPT → Profile →Settings → Apps & Connectors 2. Choose a tool (Google Drive, Slack, Notion, etc.) 3. Authenticate + approve permissions 🔐 4. Confirm the connection (you can remove access anytime) 5. Reference it in plain English:“Summarize the doc called ‘Retail Pitch v3’” Pro tip: Start with read-only access and always review outputs before sharing externally. 🎥 Watch the walkthrough: https://www.loom.com/share/af605bbada124376bf9baaad1427098b?t=48 👉 Try connecting one tool this week and share what you automate in #wins 🚀

Using your current trend data and the details of your upcoming promotion, estimate projected sales velocity and model a range of possible outcomes. Inputs: - Recent trend data (last 4–8 weeks): [insert weekly units or summary] - Promo start date: [insert date] - Forecast time frame: [insert time frame] - Projected promo lift (%): [insert percent] Contextual factors to include: - Promo type and expected impact: [insert promo mechanics and rationale] - Historical promo performance: [insert comparison data, similar offer results, or benchmarks] - External influencing factors: [insert competitive activity, seasonality, retailer traffic, inventory position, marketing support, or distribution changes] - Channel nuances (if applicable): [insert online vs in-store differences, marketplace traffic patterns, or retailer-specific lift expectations] Please calculate the following: 1. Baseline average velocity from the trend data. 2. Anomaly adjustments for stockouts, unusual spikes, or distribution disruptions. 3. Adjusted baseline: baseline after anomaly correction. 4. Promo lift application: apply the projected lift percent to the adjusted baseline. 5. Scenario modeling: 6. Confidence range: provide a high-low band for the forecast. 7. Final forecast: return the projected velocity for the selected time frame (weekly or multi-week/month). Please present the results in a clear table with these rows: - Baseline average velocity - Adjustments for anomalies - Adjusted baseline velocity - Promo lift percent provided - Most likely forecast - High scenario forecast - Low scenario forecast - Confidence range (high and low) - Final projected forecast for the selected time frame After the table, provide a short written summary explaining how the scenarios differ and what operators should watch for during the promo period.

Using the last four weeks of sales data [or Attach file]: Week 1: [insert units] Week 2: [insert units] Week 3: [insert units] Week 4: [insert units] Notes on anomalies: [insert any stockouts, distribution issues, or unusual spikes] Expected percent change due to upcoming factor (promo, seasonality, traffic): [insert percent] Please calculate the following: Average of the last four weeks (raw) Adjusted average accounting for anomalies. If any week is impacted by an anomaly, replace that week with the average of the non-anomalous weeks. Expected percent change adjustment Final weekly velocity forecast Final monthly forecast (weekly forecast multiplied by 4) Please present the results in a clear table with these rows: Average of the last four weeks Adjustments for anomalies Adjustment for expected percent change Final weekly forecast Final monthly forecast Then compute the final forecast using the values above.

Just out today! The world of enterprise adoption is changing rapidly! Here’s the highlights: • 75% of workers say AI improves the speed or quality of their work, saving 40–60 minutes a day • Heavy users save 10+ hours per week • Coding-related messages from non-technical roles are up 36% • 75% say they can now complete tasks they previously couldn’t https://openai.com/index/the-state-of-enterprise-ai-2025-report/