How to Use AI for Automated Deliverable Creation

Most people use AI like this at work: • “Summarize this doc” • “Write this email” • “Give me ideas” • “Explain this topic” That’s fine. But that’s level 1. If you want to get ahead, you need to move from using AI for tasks → using AI to design how your work gets done. Here are 10 specific, actionable ways to do that…with real examples: 1/ Build a reusable update generator ↳ Prompt: “Act as a program manager. Turn this input into: 1. What changed 2. Why it matters 3. Risks 4. Next steps with owners” ↳ Example: Paste messy notes → get a clean exec update in 30 seconds No more rewriting updates every week. 2/ Turn every meeting into a system ↳ Workflow: Transcript → summary → action items → follow-up email ↳ Example: Zoom call ends → paste transcript → instantly get: • 5 bullet summary • action items • draft email Meetings become outputs. 3/ Create a decision brief generator ↳ Prompt: “Summarize this into: problem, 2 options, tradeoffs, recommendation” ↳ Example: Instead of a long Slack message, you send: • Option A vs B • Clear recommendation Now leadership can decide fast. 4/ Build a “thinking partner” loop ↳ Prompt: “What’s weak in this plan? What would leadership challenge?” ↳ Example: Paste your plan → AI flags missing risks + gaps You fix it before review. 5/ Generate stakeholder-specific comms ↳ Prompt: “Rewrite this for: exec, team, and Slack” ↳ Example: Same content → • Exec = 3 bullets • Team = detail • Slack = 1 line No rewriting needed. 6/ Turn notes into structured artifacts ↳ Prompt: “Convert this into decisions, risks, owners, next steps” ↳ Example: Messy notes → • Decision • Risk • Owner Clarity in seconds. 7/ Run a weekly risk detector ↳ Prompt: “What risks are hidden here?” ↳ Example: Paste your update → AI flags dependencies or timeline gaps You catch issues early. 8/ Build a mini-agent workflow ↳ Chain: Notes → summary → tasks → email ↳ Example: Paste notes → everything generated That’s an agent. 9/ Simulate stakeholder pushback ↳ Prompt: “Act as a skeptical VP. What’s wrong?” ↳ Example: Paste your plan → AI surfaces objections You tighten before the meeting. 10/ Use AI to cut low-value work ↳ Prompt: “Which tasks can be automated or removed?” ↳ Example: Paste your to-do list → AI suggests what to drop You reclaim hours. Here’s the shift: Most people use AI to go faster. The people who win use AI to eliminate, restructure, and redesign work. 📬 I write weekly about AI, execution, and operating at a higher level in The Weekly Sync: 👉 https://lnkd.in/e6qAwEFc Which one are you trying first?

We’ve all seen variations of this comic on LinkedIn. They’re “funny” — but they also show a problem: we’re using AI with an old, document-centric mindset. Five bullets → AI inflates to 12 pages → AI compresses back to five bullets. That’s not intelligence; it’s content ping-pong. We’re optimizing for length, not for decisions. A better way: in a case like this, AI should act as a decision co-pilot, not a text generator. Instead of “write 12 pages,” ask AI to: 1. Clarify intent & audience. “What decision must be made, by whom, and by when?” 2. Build a 1-page Decision Brief: recommendation, three supporting reasons, risks/mitigations, options considered, next steps. 3. Link evidence, don’t paste it: connect to the data and surface the few charts or numbers that matter. 4. Generate fit-for-purpose outputs: • exec email (≤200 words with clear ask) • one-slide visual for the meeting • optional appendix with traceable sources 5. Push back when inputs are weak: ask for gaps, assumptions, and thresholds that would change the recommendation. 6. Automate the loop: monitor the underlying data and update the brief if something material changes. Try this prompt: “Turn these 5 bullets into a 1-page Decision Brief for [audience]. State the recommended action, key reasons, risks, alternatives, and next steps. Produce: (a) a 200-word exec email with a clear decision request, (b) a single summary slide, and (c) links to supporting data. Ask me any clarifying questions first.” Write less. Decide faster. Deliver clarity. #AI #AgenticAI #DecisionIntelligence #Productivity #FutureOfWork #Leadership #Communication

Most coaches & consultants don’t have a time problem. They have a systems problem. AI doesn’t fix chaos. It scales whatever system you already have. Here are 5 AI tools that actually plug into your daily workflow (with real use-cases): 1. ChatGPT: Use it to think, not just write. Daily integration: Pre-call: Generate 5 sharp questions based on client background Post-call: Convert notes into insights and next steps Sales: Practice objection handling before discovery calls Example: “Here are my client notes → identify blind spots and suggest 3 tough questions for next session.” 2. Notion AI :Your second brain for client delivery. How to use: Create client dashboards with auto summaries Maintain SOPs for your programs Turn session transcripts into insights + next steps Example: Upload session notes → “Summarize key breakthroughs + assign action items” Your client gets clarity instantly. 3. Descript: Content creation without the headache. How to use: Edit podcasts/videos by editing text Remove filler words automatically Repurpose long-form content into shorts Example: Record a 20-min coaching insight → Cut it into 5 LinkedIn videos + 10 reels in under an hour. 4. Otter.ai.: Never miss what your client actually said. Daily integration: Record and transcribe coaching calls Highlight key patterns across sessions Build a repository of client insights over time Example: Spot recurring phrases like “I feel stuck” and use that language in your next session to go deeper. 5. Make: Where everything connects. Daily integration: Auto-send session summaries after calls Connect forms to CRM, email, and task managers Build end-to-end onboarding flows Example: Client fills a form, gets a calendar link, books a call, receives a prep doc, and you get a summary. All automated. Here’s the shift most people miss: Don’t ask, “Which AI tool should I use?” Ask, “Which part of my workflow is still manual?” That’s where AI fits. Because the goal isn’t to use more tools. It’s to free up more thinking time. What’s one task in your workflow you’d love to automate right now?

A few days before the deadline, I’d find myself in that familiar pool of anxiety : staring at a blank digital canvas, clock ticking, knowing the next 20 hours would dissolve into a blur of bullet points, chart creation, & late-night pixel-alignment. Time to make a presentation. With AI, I’ve slashed the time it takes to build a presentation from a full day down to just a few hours. I start with the essence - three key ideas or stories that will resonate with my audience. With these seeds planted, I’ll dictate to the AI to architect a 12-slide framework, calibrated for its audience (recently, sharing predictions for data in 2025 to a group of data engineers & analysts which I’ll publish next week). They are The Great Consolidation, Scale Up Architectures & Agentic Data. I suggest a few deeper points like new query engines, virtual developer environments, separation of compute & storage, & collaborative BI. Then I ask the AI to draft the speaker’s notes in a framework that my management coach at Google taught me - the “Clearing-Content-Transition” framework: Clearing: Describe what’s on the slide Content: Detail 3-4 key points & supporting stories Transition: Set up the next slide naturally Each iteration refines the narrative. “Move slide 5 to after slide 10.” “Shorten the section on AI & engineering teams fusing.” “In scale up architectures, there’s a generational transition where many of the new engineers prefer Python. Weave that in.” The outline evolves as AI & I collaborate. We craft a surprising hook (no more “Hi, my name is…”), from Matthew Dick’s storytelling wisdom to give audiences a reason to care by introducing the Stakes. With the outline nearly complete, the next step is to add the visual flair. “Please create a prompt for an image in for each slide.” This part isn’t yet automated, but I flipped back and forth between the prompts and an image generation AI. Put it all together, deliver it online, read from the script a few times to practice so the delivery is a bit more spontaneous and it’s done and dusted. Not every image or every slide can be fully automated, but many of them can. There’s something about starting a blank canvas with an AI that solves or at least bevels the edges of writer’s block. I’ll publish the presentation on Jan 24. I’ll also publish the script alongside it & would love to hear your reactions.

I reverse engineered Claude Claude to figure out what makes it so damn good. Here’s the secret sauce behind the best AI tool in the world & how you can steal it for your own AI workflows: 1. Show Up Briefed Idea: The model works best when it already knows who it’s working for and what success looks like. Steal: Paste a reusable header at the top of every chat — your audience, offer, tone, KPI, and source links. Tell it: “Treat this as context. Ask before writing anything.” 2. Give It Tools, Not Just Prompts Idea: Don’t just chat — connect it to real data. Steal: Let it read from and write to a Google Sheet, Notion page, or API. Define exactly when it should pull info, update it, or ask permission. 3. Plan → Do → Track Idea: Claude manages itself with checklists. Steal: Make it outline a plan before acting, give quick status updates after each step, and add a “REMINDER:” line if it drifts off-track. 4. Split the Work, Then Combine It Idea: Multiple focused chats beat one messy one. Steal: Run 2–3 side chats (market, product, channels). Then use an “Aggregator” prompt to score each idea on impact, confidence, cost, time, and risk — and return one ranked decision with next steps. 5. Remember Rules, Not Rambles Idea: Keep your decisions consistent. Steal: Create a simple `Decisions & Rubrics.md` file. When you change direction, have the model propose a short “diff” — what changed and why. --- Copy-Paste Starters --- 1. Strategy Workshop - use this when you need to design or prioritize your company’s AI roadmap. ``` You are my AI strategy partner. Treat this chat as a working session to design an internal AI roadmap. Context: [Company Name], [Industry], [Team Size], [Core KPI]. Deliverables: (1) 3–5 high-impact AI initiatives ranked by ROI and feasibility, (2) draft 90-day rollout plan. Ask clarifying questions before outputting anything. ``` 2. Content Engine: Use this when you want to turn raw ideas into ready-to-post LinkedIn content. ``` You are my editorial co-pilot. Treat this chat as an always-on content system for LinkedIn. Context: [ICP], [Offer], [Tone], [KPI]. Task: Turn my raw notes into 5 post drafts using the Project OS format (Hook → Insight → Takeaway). After each, ask: “Publish, refine, or queue?” ``` 3. Product Discovery: Use this when you need to extract insights or opportunities from customer research. ``` You are my product research analyst. Goal: Find, cluster, and rank user pain points for [target persona or segment]. Inputs: I’ll paste raw notes or transcript text. Output: A table with columns (Pain Point, Frequency, Impact, Root Cause, Example Quote). After summarizing, suggest 3 potential AI-powered solutions. ``` Most people use ~5% of what AI can do. Don’t just chat with it, run it like an operating system.

How We’re Using AI Tools Like V0 to Turn Weeks of Work into Hours Let me share how we’re supercharging various workflows with AI tools like V0—not just to save time but to build leverage that makes every step smarter and faster. Manoj DM was working on improving the search and filtering feature on a page listing "Sexologists in a locality." Normally, this would mean hours of design, back-and-forth iterations, coding, and testing—a process that easily takes several days, sometimes even weeks. But instead of doing it the old-fashioned way, Manoj DM tried a tool called V0. It’s an AI-powered platform that helps you generate multiple design options quickly. In one hour, v0 from Vercel helped us generate three responsive, production-ready designs—clean, functional, and ready to go live. Next, we’re using Keak, an AI-driven A/B testing tool, to identify the best-performing design for our users. This would let us learn what’s effective much faster than usual. If you’re on the sidelines wondering about AI, tools like V0 can help: - Save time: Automate repetitive tasks like coding and designing. - Experiment faster: Test ideas quickly to identify what works. - Work smarter: Focus on strategy and creativity while AI handles the groundwork. - Amplify outcomes: Achieve efficiency without sacrificing quality. Will AI Do Everything? Maybe someday, but right now, it needs you to make it work effectively. Here’s how to put AI to use properly: - Start with the right problem: Focus on areas where AI can save time or amplify results, not where it overcomplicates things. - Keep humans in the loop: Use AI as a collaborator, refining its outputs with creativity, intuition, and context. - Bridge the gaps: AI can’t do everything yet—it needs human oversight to ensure alignment with goals and values. - Think of AI as leverage: It’s a tool to work smarter and faster, not a magic wand to replace effort. Just to summarise: AI isn’t magic—it’s a tool. When used wisely, it helps you supercharge workflows, build leverage, and deliver real impact.  The best outcomes come from combining AI’s strengths—speed and scale—with human expertise to create real impact. #ArtificialIntelligence #AIInAction #FutureOfWork

I’ve spent the last couple years exploring ways that AI can be used to automate parts of the software development process. In this video, you’ll see a demo of me taking a JIRA user story and automatically generating Cucumber tests and a basic implementation plan combining GitHub Actions with Gemini 1.5 Pro. With just a few lines of code, we were able to create an AI pipeline that hints at the ability to drastically speed up development time and generate artifacts to be used in other parts of other pipelines. There’s already best practices forming around using Cursor’s agentic flows that include a step of having it generate an implementation plan. Why not automate that as well? All the code for this is available at https://lnkd.in/eFfxtHWa . The particularly interesting parts where we’re using AI are under the .github/workflows directory (https://lnkd.in/exkKMZaJ). To use it you’ll also need to set up a few API keys and environment variables. If you’re looking for help you can pop in to the Sublayer Discord (https://lnkd.in/eiaJV7JX) and I should be around to guide you through it. Want to see more explorations like this and get notified as soon as we release anything? Subscribe to my new newsletter - Works on My Machine - https://lnkd.in/eMe2NdjZ where I'll be sharing demos like this, longer form posts about the implications of AI like I've written in the past, and product announcements based on the ideas I share in the posts.

We rolled out AI across our team in 60 days. No chaos. No confusion. Just clear wins and real results. I've seen marketing departments jump into tools like ChatGPT and Claude without a plan, only to end up with inconsistent usage, security risks, and wasted time. So here’s a reality check: Giving your team access to AI tools is not the same as making them AI-ready. What works? A clear, structured rollout that builds confidence, protects your brand, and drives performance. Here’s the 7-step sequence I recommend getting your marketing team fully ready to use AI: 🔹 1. Leadership Alignment Before anyone writes a prompt, you need to answer this: → What are we actually trying to improve with AI? → Clarify your goals: content speed? campaign performance? lead quality? 💡Assign an internal AI Champion to lead adoption and make this someone’s job, not everyone’s maybe. 🔹 2. Create Your AI Usage Policy Yes, before the first prompt. Set ground rules: → No client data or credentials in tools → Human review before anything goes public → Approved tools only → A go-to person for AI questions 💡Keep it simple. A 1-page doc is better than a 20-page one no one reads. 🔹 3. Train the Team Don’t assume “digital native” means “AI fluent.” Run a short onboarding: → Demo real-world prompts for their roles → Share a centralized prompt library → Walk through how to use your company’s Custom GPT (if you have one) 💡Make it practical. Confidence creates momentum. 🔹 4. Start With Small Pilots Want to build trust in AI fast? Deliver small wins early. Assign 1–2 people per function to test real use cases: → AI for email writing → Content repurposing → Campaign briefs 💡Document results. Share what worked and build internal buy-in. 🔹 5. Bake AI Into Daily Workflows AI should enhance what already works. → Add AI to your content creation SOPs → Use it for meeting note summaries → Integrate it into campaign planning templates 💡The more friction you remove, the faster usage scales. 🔹 6. Build a Feedback Loop Set a bi-weekly or monthly check-in: → What’s saving time? → What’s confusing? → What should we expand next? 💡Refine as you go. This isn't a one-and-done rollout. It's a capability you're building. 🔹 7. Enable Long-Term Growth This isn’t just about productivity. It’s about transformation. → Encourage ongoing experimentation → Recognize team AI wins → Offer certifications or incentives to deepen adoption 💡You’re not just introducing a tool. You’re building a smarter, faster, more strategic team. ✅ Final Thought If you're leading a marketing team, you don’t need to rush into every AI trend. But you do need a clear path for AI readiness. Because the biggest risk today isn’t overusing AI. It’s being the last team in your category that doesn’t know how to use it well. ____________ ♻️ Repost if your network needs to see this. DM me if you need help creating an AI rollout plan for your team.

This is my first time building a services company. In the past, my co-founder and I only built product companies. Product companies are infinitely scalable theoretically and VC fund-able, while services companies are not. And being honest, it's more fun to work on a product than a service. However, with AI, this has completely changed. There is a huge demand for services at the moment, so the real challenge is, how do you build an effective IT services company in the age of AI? Delivering services is messy and a lot of unscalable hard work. Think about it like this: You need to bring founder level energy to every project to be effective and deep dive into the details for every new client (and do this over and over and over again). So how do you solve this? The answer is, leveraging AI and LLMs to streamline the service delivery process. What do I mean by this? Implementing LLMs and Agents internally to help go from AI automation idea to SOW all the way to technical tickets being scoped out for engineers to work on. Here is what our current requirements gathering process looks like, using LLMs and AI Agents (this would’ve taken weeks and hours of meetings): 1. Clients interact with our conversational agent to explain their problem, workflow, and goals (Think of this as your technical project manager deep dive) 2. The agent translates this into a draft proposal with potential AI automation plans & options 3. Our team reviews, adjusts for budget + timeline, and turns it into a polished SOW 4. Client reviews the plans → approves the plans → we move to finalized SOW 5. From there, we auto-generate tickets in our project management system so engineers and PMs can execute immediately Clients go from idea to signed proposal + scoped plan in days. We still keep humans in the loop for quality and nuance. To me, this is a small but important breakthrough. One of the best applications of AI / LLMs is improving how IT projects & services are delivered, where the AI does the heavy lifting around requirements gathering, scoping and finalized planning. What scared me away from services before (i.e. the upfront grind) is now the part I actually look forward to. I can see this pattern applying to any services-like business, not just IT services delivery (think Legal / Accounting etc.).

Your team just spent 120 hours on an RFP response. You submitted it 2 days late. The client went with someone else. And your best people are burned out from doing this three times a month. This is the reality for most companies responding to RFPs. According to the Wharton GKB AI Executive Report, RFP teams are still spending 100–150 labor hours per response, missing 24% of deadlines, and seeing low win rates on new business. And 62% of companies still aren’t using AI for RFP management at all. That's your competitive advantage right there. Manual RFP management drains time, introduces errors, and costs millions in missed opportunities. But most companies are still doing it the old way because they don't know where to start with AI. There's a progressive path to automating your RFP process, and you don't have to go all-in on day one. You can start small and scale as you see results. Level 1: Dip Your Toe (2-4 weeks to implement) Start with AI-generated timelines and task assignments. Use it to review for clarity, fix typos, and adjust tone. Simple automations that eliminate busywork. You'll see 10-15% time savings immediately with minimal investment. Level 2: Knee Deep (2-3 months to implement) Train a private LLM on your historic RFP responses. Use conversational AI for pricing and identifying client patterns, and auto-populate company information. Your team collaborates faster because AI is working behind the scenes. Time savings increase by 30-40%, and you'll begin to see higher win rates. Level 3: All In (4-6 months to implement) Agentic calendar and milestone management. Auto-generation of customer questions. Cascading supplier RFP creation. Profitability and capacity-driven pricing. AI becomes your RFP operations team. Time savings hit 60-70%, and you gain a strategic advantage in how you respond. For companies receiving 100 or more RFPs per year, the difference between Level 1 and Level 3 could result in 15-20 additional wins annually. Think about what that means for your revenue. If your average contract is worth $500K, that's $7.5M to $10M in additional revenue you're leaving on the table by managing RFPs manually. Most companies get stuck because they think they need to jump straight to Level 3. You don't. Start at Level 1, prove the ROI, then move to the next level when you're ready.