Leveraging AI for Remote Collaboration

I've received a few questions on this, so thought I'd share 5 ways I'm using AI in my day-to-day at work to boost productivity, insight, and strategic clarity: Benchmarking: I use AI daily to quickly validate metrics and performance benchmarks. For instance, when reviewing email open rates, I ask ChatGPT (and other LLMs) for industry benchmarks segmented by email types, industries and content. This provides immediate clarity on performance against the rest, and I can see if we're good, great, or have work to do. This information was hard to find or non-existent before and instantly helps builld context. Thought Partner: LLMs elevate my strategic thinking. Whether analyzing competitors or drafting new strategies, I leverage AI to rapidly identify gaps, assess my thoughts against frameworks like "Seven Powers," and run game theory on them with competitive response and market players. It uplevels my thinking and leads to more comprhensive considerations. Deepening Customer Insights: By processing sales call transcripts and meeting notes through AI, I can surface customer pain points and uncover new insights, which improves my understanding of customer needs, sales blockers and messaging that otherwise would be hard to come by. Writing Partner: I use AI to power my writing process—from refining documents to constructing logical, concise, and compelling arguments. It helps draft outlines, provides examples and proof-points to reinforce my assertions, and streamlines my writing. All-in it makes my writing better and faster. Automating Daily Tasks: I use AI-powered tools daily to track competitors, monitor market trends, and check-in on things I care about. It never stops working and so I always have this information available as needed. Today, AI is integral to about half of my workday. And this is just the beginning—there's even more potential to unlock with automations such as reviewing and drafting replies for my emails, prioritizing which documents to review next, and automated meeting prep. How are you integrating AI into your workflow? I'd love to hear what's worked for you.

Here are 5 ways I use AI in my day-to-day job. Key insight: Post-AI, I’ve stopped thinking in algorithms. Heuristics are enough to get the job done. → Presentations: Last month, I had to prepare a presentation about a past project for the larger team. AI handled 80% of it including storyline, draft, deck building, narrative, and even the speaking points (with jokes included). After every meeting, I get feedback on pacing, filler words, and tone, along with suggestions for how to improve. → Communication: Once, I was deep in focused work and had to relay information between two senior folks, simultaneously. I used AI to handle the back-and-forth. It picked up on their tone, hierarchy, and content fast enough to draft near-perfect responses. I stayed focused, no bandwidth lost. → Meetings: Minutes, transcripts, translations; AI makes them painless. → Coding: Tools like Github Copilot and Cursor AI are great assistance when I'm working on codebases, helping with thinking, research, alternate approaches and minor rewrites of the functions. → Research: In-house tools are great way to fetch internal information, perform research and action tasks. For personal work, Perplexity replaced Google search long ago. Pro tip: I intentionally drop keywords in chat and documents while working. Later, I ask AI to find those and create a to-do list, sometimes even prioritizing tasks or suggesting which meetings I can multitask in. It also makes retrospection and info retrieval much easier. Still exploring agent orchestration. Your turn; how are you integrating AI into your daily grind?

My Daily AI Workflow as a QA / SDET (Saves 2–3 Hours Daily) A lot of people comment “AI is powerful.” Few know how to actually use it daily. Here’s my practical AI workflow as a QA/SDET 👇 🧠 Step 1 — Requirement Breakdown (15–20 mins saved) Tool: ChatGPT I paste the user story and ask: ✅ Generate positive scenarios ✅ Generate negative scenarios ✅ Suggest edge cases ✅ Identify missing validations Result: Instead of manually brainstorming 25 test cases, I refine AI output. ⚡ Faster thinking, not blind copy-paste. 🧪 Step 2 — Automation Skeleton (30 mins saved) Using: GitHub Copilot Playwright I generate: ✅ Page Object structure ✅ Assertion patterns ✅ Mock test data ✅ Basic reusable methods ✅ Then I clean & optimize it. AI drafts. I architect. 🐞 Step 3 — Debugging Failures (Huge Time Saver) When a test fails: Instead of scanning 400-line logs manually: I paste: ✅ Error stack trace ✅ Relevant code snippet Ask: “What is the likely root cause?” Often it detects: ✅ Timeout misconfiguration ✅ Locator mismatch ✅ Missing await ✅ Wrong test data ⏳ 30–40 mins saved per failure. 🔁 Step 4 — CI/CD Optimization For pipeline improvements: ✅ YAML improvements ✅ Parallel config suggestions ✅ Docker tweaks ✅ AI gives structure → I validate feasibility. This improves release stability. 📊 Step 5 — Documentation & Communication Instead of spending 1 hour writing: ✅ Test summary ✅ Bug explanation ✅ PR comments Technical documentation I generate a structured draft and refine it. Clean. Professional. Fast. ⚠️ Important: What I DO NOT Do ❌ Blind copy-paste AI code ❌ Trust AI without validation ❌ Skip fundamentals ❌ Replace thinking with prompting AI is a multiplier. Not a replacement. 💡 The Real Difference Average QA uses AI occasionally. High-performing QA builds a repeatable AI workflow. That’s where productivity jumps.

Everyone says AI saves 𝗧𝗜𝗠𝗘. So why are most people spending more of it? Because they're stuck in the same loop. Write a prompt. Explain the context. Start from scratch. Repeat again tomorrow. It feels productive. But in reality, you're resetting the same machine every single day. The people getting the biggest results aren't using better prompts. They're building better 𝗦𝗬𝗦𝗧𝗘𝗠𝗦. Instead of teaching AI the same things over and over, they create an environment where AI remembers how they work. Here's what that looks like in just 60 minutes. First 10 minutes Create one workspace. • About Me • Projects • Templates • Outputs Now Claude has context instead of starting with a blank page. Next 15 minutes Write two simple files. How you think. How you write. This removes hours of repeated instructions. Next 10 minutes Set global rules. What to read. What to check. How to respond. What to avoid. Now Claude stops making unnecessary assumptions. Next 10 minutes Start one real project. Choose something you do every week. The more real work it sees, the more useful it becomes. Next 10 minutes Run an actual task. Have it ask for approval before taking action. Now it starts working the way you work. Final minutes Automate the tasks you repeat every week. Now AI becomes part of your workflow instead of another tool you have to manage. This is the real shift. More prompting = more manual work. More structure = more 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲. Most people will keep chatting with AI. A few will build systems that get smarter every time they use them. That gap gets bigger every week. The future isn't about writing better prompts. It's about building AI that remembers, improves, and works alongside you. So be honest. Are you using AI every day... Or are you building a system that replaces your daily work? ---- 👉 Love my content? ☑ Follow me on LinkedIn: Tamara Storm 👉 Found this helpful? Share it! ♻️ Don't miss out! For exclusive AI and tech insights trusted by 570,000+ professionals at Microsoft, Google, Amazon, and more—join my free newsletter for cutting-edge strategies to keep you ahead in AI. 🔗 Subscribe now: https://lnkd.in/eFNvmcYa 🎧 𝗟𝗶𝘀𝘁𝗲𝗻 𝘁𝗼 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗲𝗽𝗶𝘀𝗼𝗱𝗲 𝗵𝗲𝗿𝗲 🔗 YouTube: https://lnkd.in/eGJPiQ4y  🔗 Amazon Music: https://lnkd.in/daTPRypn 🔗 Apple Podcasts: https://lnkd.in/dphbS89P 🔗 Spotify: https://lnkd.in/dQ9gjG2E 🔗 RSS: https://lnkd.in/d4tCXB7n

How to make building with AI a daily mindset for you and your teams. I shared these tips in a keynote this week and got feedback it was helpful. Other tips? Please share in comments! *1. Prompt 10 messages a day Set a message goal for yourself. It sounds small, but it builds the habit and gives you a way to measure your AI usage. One pro tip: make it a task in ChatGPT. I have task set so at 5pm every day I get a notification telling me how many messages I've sent. Keeps myself accountable. *2. Try something new each week Push yourself to try something new with AI every week. I actually kick off my team meeting with everyone having to share something new they tried with AI. Two things happen: First, people come prepared because they know they will be asked :) Second, we learn a lot from the examples. One week two people shared some amazing marketing + Codex examples. That pushed me to spend time with Codex that weekend and get better at using the tool. *3. Scale the wins Create a place where people can share AI wins. We have a Slack channel for this. It lets everyone see what is working across the org and get inspired with ideas to try. And when someone new joins, they can use Codex to create an onboarding journey based on that Slack channel. It reads the last 30 days and builds a syllabus of demos and examples. Dynamic onboarding! *4. Reporting and accountability Look at the data. For my team we track daily and weekly active use of advanced features. It helps me understand adoption of things like Deep Research and Connectors. I recently talked with someone that took this further. They publish daily active usage for a 1000+ employees in a live dash. Anyone can see how they or their peers are doing. Two things happened. Their daily active use is above 95%!, and it made it easy to identify AI power users... *5. Recognize your champions Every organization has people who get energy from building with AI. Find them. Empower them. Let them teach others and share examples. They should be posting in your Slack channels and driving ideas for how to best use AI in your company. These people are the glue. *6. Leadership shares top down examples Leadership examples are equally important. Greg (our president and a former CTO) recently posted on X about asking OpenAI developers to default writing all code with Codex. That moment mattered as it was from leader deeply familiar with the code. It gave clear permission for a new way of working. 7. Make time for build hours Create time to actually play with AI. Our team runs “build hours” where we get together and test new product and model capabilities. It's fun, but it is also one of the best ways people learn from each and become comfortable building new skills. What I've noticed is when you make AI a daily habit, your mindset changes. You start to feel more like a builder and treat AI less like a tool. A fun way to operate. Would love to your hear other tips in the comments!