Jun 27, 2025
How to use AI everyday at work
Discover how to use AI in your daily work across sales, marketing, support, and operations. Learn practical prompts, business use cases, and how connected AI tools like Calk AI boost productivity and decision-making.
Quentin Fournier
AI is no longer a futuristic dream—it's your new work assistant. Whether you're in sales, HR, marketing, or operations, AI can boost productivity, reduce busywork, and drive real business outcomes. But to use AI effectively, especially at scale, it must be connected to your internal tools and data.
It is when truly getting in the mud of real use cases that tools like Calk AI truly stand out.
Why This Matters: AI Is Here to Stay
The average knowledge worker spends 58% of their time on work coordination (McKinsey). AI helps you reclaim that time, but only if it’s embedded in your actual workflow.
Disclaimer: These examples assume you're using a tool like Calk AI, which connects to your tools (Slack, Notion, CRM, Docs, etc.), allowing agents to take contextual action without coding.
Superpower #1: Summarize your data : cut through the noise and act on what matters
Information overload is the silent killer of productivity. Between emails, meetings, reports, and Slack messages, the average employee processes thousands of words a day—yet struggles to extract what’s actually useful.
AI-powered summarization is changing that. When connected to your internal data sources, AI can automatically condense complex or lengthy content into key takeaways, action items, and strategic insights—tailored to your business context.
Why AI summarization drives real business value
Save hours per week: Skip repetitive reading and manual note-taking.
Accelerate decisions: Quickly align on what happened and what’s next.
Improve collaboration: Share clean, clear summaries across teams.
This isn’t generic GPT output. With tools like Calk AI, your summaries are driven by your tone, your knowledge, and your tools.
How teams use AI to summarize their business data
Let’s break down some real-world, daily use cases—by department in a company.
Department | Use Case | Prompt Example | Outcome / ROI |
|---|---|---|---|
Sales & Customer Success | Post-call summaries | “Summarize this client call and highlight next steps and objections.” | Less time on follow-ups, more time closing deals |
Sales & Customer Success | Support ticket overviews | “Summarize this Intercom thread. What was the customer issue and how was it resolved?” | Faster onboarding, clearer escalations |
Marketing & Content | Meeting recaps | “Summarize our campaign planning call and extract tasks for each team.” | Faster execution, less misalignment |
Marketing & Content | Long-form content repurposing | “Summarize this 20-page whitepaper for social media and email.” | Save hours repackaging content |
Product & Engineering | Daily standup summaries | “What blockers did the team mention in today’s standup?” | Spot risks early, improve coordination |
Product & Engineering | Bug report summaries | “Summarize the Jira ticket conversation and what has been tried so far.” | Scale engineering support efficiently |
Superpower #2: Search and get instant answers from your business stack
What it does:
AI-powered search goes far beyond keyword lookup. Instead of “Ctrl+F” in one tool, AI search lets you ask real questions—across every app your team uses. Whether your data lives in Notion, Slack, Google Drive, HubSpot, or Jira, connected AI pulls the most relevant answer, summarized and contextualized.
It’s like having a colleague who knows everything, remembers every version of a document, and instantly understands what you’re trying to find—even if you don't phrase it perfectly. You can ask, “What are the top customer complaints from Q2?” or “Where is the updated sales deck?” and get the exact answer, sourced from across tools.
Why it matters:
Eliminates time wasted searching: Employees spend up to 2.5 hours a day looking for the right information. AI cuts this down to seconds.
Improves decisions: Faster access to past insights means fewer mistakes and smarter, data-informed decisions. No more asking five teammates or digging through five tools.
Reduces tool fatigue: You don't need to remember where a file lives. Just ask in plain language. The AI will search Slack, Notion, Docs, and more—all from one place.
Examples of Using AI for Business Search
Department | Use Case | Prompt Example | Outcome / ROI |
|---|---|---|---|
Sales Teams | Search past emails or CRM notes | “What did the prospect at Acme Corp say about pricing?” | Faster responses, improved sales conversations |
Sales Teams | Retrieve key documents | “Show me the last proposal sent to Company X.” | Time saved searching, smoother follow-ups |
Marketing Teams | Locate brand or tone guidelines | “Where is our 2024 messaging guide?” | Consistent content, quicker onboarding for new marketers |
Marketing Teams | Find campaign-specific assets | “What testimonials are about onboarding experience?” | Faster campaign creation, better targeted messaging |
Product & Ops Teams | Surface internal processes | “What is the enterprise onboarding workflow?” | Avoid repeated questions, smoother cross-team collaboration |
Product & Ops Teams | Retrieve project history | “Find all notes related to the Q2 platform update.” | Better project tracking, informed product decisions |
Superpower #3: Generate with your data as context
What it does:
AI generation enables teams to produce high-quality content, communication, or analysis at scale. With your internal data as context, AI can draft personalized emails, social posts, reports, outlines, proposals, and even code. It doesn’t just write—it writes like it knows your business.
It turns one idea into ten. One dataset into a narrative. One client brief into a full strategy. Whether you're stuck, under pressure, or launching something new, AI removes the blank page problem and accelerates execution.
Why it matters:
Boosts creativity and consistency: Marketers and content creators can rapidly generate ideas, variations, or headlines—without burning out.
Speeds up execution: Sales reps don’t have time to write 30 follow-ups. With AI, they don't have to. Just feed it the meeting notes—done.
Tailored to your brand: Generic ChatGPT output doesn’t cut it. But when generation is connected to your tools, tone, and data, you get content that’s accurate, contextual, and on-brand.
Examples of Using AI for Smart Generation
Department | Use Case | Prompt Example | Outcome / ROI |
|---|---|---|---|
Sales Teams | Personalized outreach | “Draft a follow-up email to this client who raised pricing concerns.” | Saves time, increases email relevance and response rates |
Sales Teams | Proposal assistance | “Generate a custom pitch for a SaaS company in the healthcare space.” | Faster proposal creation, more tailored sales assets |
Marketing Teams | Multi-format content | “Turn this blog post into a LinkedIn carousel and a newsletter summary.” | Repurpose content at scale, improve campaign reach |
Marketing Teams | Persona adaptation | “Rewrite this page for CFOs at mid-market tech companies.” | Improves conversion rates by tailoring messaging |
Superpower #4: Analyze
What it does:
Analysis is where AI shines brightest. By connecting to your business data—CRMs, spreadsheets, feedback tools, or support platforms—AI can identify trends, outliers, and opportunities. It’s not just stats; it’s interpretation. Ask it to benchmark sales performance, highlight churn risks, or explore customer satisfaction themes—and it will do it in plain language.
What used to take an analyst hours of spreadsheet work can now be done in moments. And it’s accessible to everyone, not just the data team.
Why it matters:
Uncovers what matters most: AI can surface patterns that humans miss—like rising complaints about a product feature or declining engagement on a key channel.
Puts insights in your hands: Non-technical users can ask questions and get data-backed answers—no SQL, no dashboards required.
Enables real-time action: Spot problems before they scale. See what’s working now, not just at month-end. It’s proactive, not reactive.
Examples of AI-Driven Analysis in Daily Work
Department | Use Case | Prompt Example | Outcome / ROI |
|---|---|---|---|
Leadership and Strategy Teams | Customer sentiment tracking | “Analyze all support conversations from this month and highlight churn risks.” | Identifies early churn signals, supports retention strategies |
Leadership and Strategy Teams | Team productivity trends | “Compare response times across the support team over the last quarter.” | Uncovers performance patterns, helps set team benchmarks |
Operations and Support Teams | Performance optimization | “Which onboarding processes cause the most delays or support tickets?” | Improves workflows, reduces time-to-value for customers |
Operations and Support Teams | Theme identification | “Analyze NPS responses and summarize key improvement areas.” | Surfaces high-impact feedback, drives product/service improvements |