Real LinkedIn Lead Magnet Examples

On October 5, Deloitte published "Frontier AI agents are testing enterprise control limits: 4 questions for leaders." Its answer is short. Give every agent "a verifiable identity, a defined owner, an approved purpose, and a bounded set of permissions." So here's a leadership team of 10 Claude agents, set up so each one has a seat, a single job and written limits 👇 Ten executive seats, one agent each: → AI CEO, the Strategic Operator: priorities and resource calls → AI CFO, the Numbers Machine: cash flow models and margin → AI COO, the Process Surgeon: workflows and bottlenecks → AI CRO, the Revenue Engine: the demo-to-close rate → AI CMO, the Growth Architect: the funnel → Five more: the CTO, Head of Product, Head of Growth, Customer Success and the CHRO Each agent is a full role prompt in five parts: who it is and how senior, your industry, the methods it uses, its constraints, and the shape of its answer. Put that next to Deloitte's line. The seat names who the agent is. The task is its purpose. The constraints block holds the limits you set: budget, timeline, headcount. And every agent ends with a written answer in a set format, so nothing moves until you read it. Prioritize. Forecast. Unblock. Close. The one item on Deloitte's list a prompt can't hold is the owner, and that's you. The agents give you each seat's reasoning. None of them signs anything or carries a number. The card has the word. Comment it below and I'll get you all 10 agents.

Then built the CRM I wish they all had. Now I'm giving it away for free. Most founders track their raise in their inbox. That's why follow-ups die and rounds drag on for months. What's inside: → Fit score for every investor (stage, sector, geography, cheque) → Tier A, B, C so you know who to contact first → Pipeline from researched to committed → Red alerts for overdue follow-ups → A dashboard with your reply and meeting rates → A 30-minute weekly review checklist Comment SYSTEM and follow. I'll send it to you. P.S. This will probably blow up, so give me some time to reply.

Are you targeting investors who can actually write your first cheque? I sent 340 cold emails to VCs before I realised most of them were never going to invest. Wrong stage. Wrong cheque size. Wrong region. I was spending hours pitching investors who were never the right fit. Fundraising isn't a numbers game. It's a targeting game. You don't need 340 random investors. You need 50 who invest at your stage, write cheques within your range, and back companies in your region. That's why we built this into Raise. A database of 1,003 investors who write first cheques at pre-seed and seed. With Raise, you can: → Filter by cheque size: $25K–$2M or $2M+ → Filter by stage: pre-seed or seed → Explore 6 regions, from Africa to North America → Search by fund, city, or sector → Build a targeted list of 50 investors in minutes No more spending weeks piecing together investor lists from LinkedIn and Crunchbase. No more chasing famous VCs just because you've heard of them. Find investors whose stage, cheque size, and geography fit your round. Want access to the investor database? 1. Connect with me. 2. Comment RAISE. I'll send you the link. P.S. The link is also in my Featured section. Get your Raise Score and build your investor target list today. #Fundraising #PitchDeck #Startups #PreSeed

One lead research and DM used to cost us about $20 👇 1/ Sorts your engager lists Code pulls the people who commented on or reacted to posts in your niche. Jev judges each row, for example whether a commenter is a buyer, a peer or a competitor. 2/ Checks what each lead is signaling Code collects job changes, hiring posts, funding news and recent posts for every profile. Jev goes through those signals one by one, so you see why someone is warm right now. 3/ Matches buyer pain to your offer Jev takes each problem a prospect posts about and picks your best-fit use case for it. You see the leads you have no angle for. 4/ Scores who's worth a DM Jev judges fit and intent on every lead. Code turns that into a 0-100 score. Only 80+ moves on. 5/ Writes the opener across 500-1,000 profiles a day Jev picks which signal each DM should open with. An LLM then writes only the DMs that passed, under 75 words each. That's the DIY version: code + Jev + a sheet, run by hand every day. We turned the same loop into Beeze AI: Capture → Enrich → Score → Message → Auto-send 20+ signals in 8 categories. 20-50 leads/day. 4 hrs/day → 10 min/week. Access below👇

100 videos featuring the top experts on AI for finance First 100 comments only so don't wait. Most finance professionals learn AI from random YouTube videos. No structure, no clear starting point, and hours lost searching. I watched hundreds of AI for Finance videos and handpicked the 100 best ones across 10 categories. Then I put them all in one place. Here's what's inside the 10-collection box set: 1. ChatGPT for Finance - models, analysis, presentations 2. Claude for Finance - Excel, Cowork, financial modeling 3. Gemini for Finance - automation, dashboards, forecasting 4. AI in Excel - agent mode, formulas, data cleaning 5. Claude in Excel - FP&A, dashboards, scenario planning 6. AI PowerPoint - board-ready slides from raw data 7. Claude Cowork - automation, reporting, reconciliation 8. AI Dashboards - CFO-ready visuals in minutes 9. AI Comparisons - ChatGPT vs Claude vs Copilot vs Gemini 10. Finance YouTube Videos - the best finance fundamentals Each collection is curated. Each video is there for a reason. This is the structured learning path I wish existed when I started using AI for finance 3 years ago. I've trained 70,000+ finance professionals on AI. Companies like Mercedes-Benz, KPMG, and Rakuten pay me $10,000+ for workshops. This collection is free. ♻️ Repost so your finance team gets access too

I’ve prepared a practical UAE Accounting Interview Questions & Answers guide for accounting and finance professionals who want to revise the areas that commonly come up in interviews. The guide covers: UAE VAT Corporate Tax Practical Journal Entries Bank & GL Reconciliations Month-End Closing WPS & Payroll QuickBooks, Zoho, Tally & SAP FICO Excel for Accountants Technical & HR Interview Questions The goal is not just to memorize answers. It is to understand the accounting treatment, the reasoning behind it, and how to explain it confidently in a real interview. Want the complete PDF guide? Comment “UAE” below or email me at: smohsinj444@gmail.com I’ll share the complete document with you. Syed Mohsin Accounting | Finance | Business Support WhatsApp: +92 302 4139163 #UAEJobs #Accounting #FinanceJobs #UAEAccounting #CareerDevelopment

After the pitch, investors start pulling your company apart: market, numbers, competition, growth, team, assumptions. In private equity, firms pay McKinsey hundreds of thousands of dollars to do this work. In venture, the process is leaner, but many of the questions are the same. The problem: investors get to write that report on your company. You usually don’t get to read it. So I wrote a playbook to help founders run that diligence on themselves first, using Claude and McKinsey-style problem solving. Inside you’ll learn how to: 1. Map your entire risk surface with MECE 2. Break every risk into questions a data point can answer 3. Attack your own strongest answers 4. Score your market and position with the GE-McKinsey Nine-Box 5. Test whether your growth survives past the core Comment DILIGENCE and I’ll send you the link!

Here's the 10 workflows every media buyer should have. OpenAI released it last week: a fast model that only answers yes/no, pick-one or 1-to-5 questions. For marketers, that means every comment, lead and search term can finally get a decision. Pasting 20 comments into ChatGPT already works. But your account has thousands of rows, and nobody pastes row 2,000. So each workflow asks one question of every row, and a written rule turns the answer into an action. It charges $0.10 per million input tokens, and almost no marketers are using it yet. Your ads: → creative-angles: reads every past ad's image, then shows CPA per angle → creative-format: tags UGC, founder or product shot, then shows CPA per format → negatives: finds search terms to cut, with the cost behind each → page-match: scores each ad hook against its landing page → creator-brief: checks UGC scripts against your brief, line by line Your customers: → comments: sorts buyers, questions, complaints and spam → leads: scores every form hot, warm or cold → reviews: pulls the lines you can run as hooks → survey: tags "how did you hear about us" answers to a channel → replies: sorts outreach replies into the next step I'm giving away all 10 workflows right now.

12 minutes of oversight a day. That’s what happened when I connected Claude to our acquisition workflow. I used to think AI outreach meant better emails, faster personalization, and more volume. Then I started using it to handle the work around the message. Claude doesn’t do this on its own. It needs the right tool connected to it. Once connected, the system: → Finds ICP-fit profiles based on role, company revenue, and hiring velocity → Analyzes recent posts, comments, and company news → Spots buying triggers across 10+ intent signals → Identifies a relevant reason to reach out → Writes context-aware openers → Adjusts follow-ups based on reply sentiment → Flags high-intent leads → Books qualified prospects directly into the calendar Last week, that translated into: → 742 conversations started → 41 qualified meetings booked → 14 prospects moved to proposal I didn’t open Sales Navigator once. What used to take 3–4 hours of manual outreach each day now takes 12 minutes of oversight. The biggest shift was connecting Claude to the entire workflow—from finding the right prospect to booking the conversation. The same system is generating 2–6 qualified calls a day for B2B operators. Want the tool? I’ll share it.

I ran a 3 week vibe coding programme with the ivee team and you should’ve seen their faces when they all opened VSC 😭 For a lot of them, Claude Code was new territory and I could see the fear in a few of their faces BUT to make it easier for them to get going, this is exactly what I told them on week 1 1: Write a PRD first. You might be thinking what the heck is that but it’s really just a document that says what you’re building and why. My best piece of advice here - especially if it's your first build - keep the scope small! you do not need more than your actual use case calls for, no matter how many features your brain wants to add. 2: Build it as an internal tool first. Nothing client-facing or public just yet. Keep it locally so nobody else can go near it while you’re playing around and figuring things out. 2: Building a Claude Artifact first is a good call. Some of the team didn’t like the fact that they couldn’t visually see what was being built so this way you can see what you’re building instead of being faced with loads of lines of code. If you’re on a team account, you can share your Artifact with someone to get their opinion before you go off and build it. If you want my epic vibe coding 101 starter pack, drop 'VIBE' in the comments & I'll send you the link 😎 + If you’ve built anything cool with Claude Code recently, let me knowwww because this is my JAM!

Start with read-only access and one number you can verify. A Skill reading last week’s CSV starts its analysis with last week’s numbers. The export may also use the wrong account, date range or columns. A cleaner prompt cannot repair those inputs. I published a four-step guide for connecting Claude Skills to Google Ads, Search Console and GA4. MCP connects Claude to the data source. The Skill sets the checks, severity rules and output format. Here is the setup: 1. Connect only the data sources the job needs. Google publishes official MCP servers for Ads and Analytics. For Search Console, use a reviewed community connector or an export fallback. Start with read-only access. 2. Check one number by hand. Ask Claude for a known spend, click or session figure. Compare it with the platform’s own report for the same dates. If it differs, check the account, reporting window and time zone before continuing. 3. Put the judgment in SKILL.md. Define conversions, reporting windows, severity rules and the expected output. Every recommendation should include its evidence, proposed action and owner. Use complete reporting days, allowing for GA4’s processing delay. 4. Choose one recurring job. A weekly paid-spend review can combine Google Ads Weekly Audit, Search Terms Analyzer and Negative Keyword Miner. A monthly SEO review can use GSC Data Interpreter and SEO Monthly Report Generator. Keep approval with a person. The official Google Ads MCP server cannot change bids or pause campaigns. If a connector fails, use a dated export and label the output as export-based. Write “not available” when a field is missing.The guide includes the MCP links, starter Skills, example output and checks for common failures. Comment LIVE and I’ll send you the full guide.

I'm sharing the playbook for free 👇 Writing the first message is only one part of outbound. The rest is choosing the right accounts, finding context, keeping the follow-up coherent and knowing when a reply deserves a meeting. This playbook connects five stages: → Target: define the ideal customer, accounts and buying personas. → Research: read profiles, brief the company and find the relevant signal. → Write: prepare the opener, connection note and follow-up in a consistent voice. → Send: plan the cadence and review the replies. → Convert: prepare the meeting request, call brief and handover. Each stage uses the context gathered before it. That's the part I find useful: keeping the buyer's situation visible from the first message to the first call. Comment OUTBOUND for the free playbook.

we've been playing with JEV for awhile and honestly think it unlocks a completely new paradigm in LinkedIn prospecting all prospecting tools in the market work like this: ICP input -> translate to search filters -> retrieve a few hundred profiles via the filters -> LLM to rank and write the problem? the most important decision in the entire process becomes the initial list and the filter translation, as you cannot message someone you never retrieved with JEV, you can flip it around you can take the entire accessible pool of people engaging in your niche on LinkedIn (post commenters, competitor engagers, industry leaders' followers) and ask tiny yes/no questions about every profile and its activity: 1. role → do they own the problem you solve? 2. company → inside your ICP on size and industry? 3. career change → new role or promotion recently? 4. hiring → posting roles your product helps with? 5. funding → just raised or got acquired? 6. engagement → commenting on posts about your problem? 7. tech stack → using tools you plug into or replace? 8. title match → would a title filter have missed them? 9. fit → a 0-100 score, only 80+ gets a DM in other words, you can now boil the ocean and evaluate the entire lead pool with your taste a much wider pool than ANY filter-based list in the market imagine an intelligent SDR having infinite time looking at EVERY profile in your market before sending a single DM feels like a fundamental shift in outbound design the DIY version: code collects the profiles, JEV answers the yes/no questions, Claude writes DMs under 75 words for the ones that pass the automated version: Beeze AI reads 500-1,000 profiles/day against 20+ signals in 8 categories and only messages the 80+ leads Access below👇

(we've booked 500+ calls in the past 6 months) Most people send LinkedIn messages to lists pulled from the same databases everyone else is using. So the prospect gets your message the same week as 20 others, and none of them is about anything that relates to them. But companies about to buy usually show it first. They post jobs, raise money, hire someone new into a role, speak at a conference or start running ads. So I built 7 Claude agents. Each one watches one of those places and tells you who to message this week, and why: → Funding watcher: finds companies that just raised money, often before the raise goes public → Hiring signal finder: counts open roles in the team that owns your problem, so you know who has budget and pressure right now → Job change tracker: finds decision makers in their first 90 days in a new role, while theyre still working out what to change → Speaker list reader: finds execs speaking on your topic at upcoming conferences, months before the event → Earnings report reader: pulls what public company execs told investors theyre focused on this year, in their own words → Post commenter collector: sorts the people commenting on LinkedIn posts about your problem and keeps the ones who could buy → Ad library checker: finds companies already paying for ads, so you know they have budget to spend Every agent gives you the company, the person, the date it happened and the first line to start your LinkedIn message with. Here are some of our best results using Linkedin outreach: → took our own agency to six figures a month in 10 months → $100K+ in the first 60 days for a freight brokerage → $60K+ in pipeline in 45 days for an AI automation company selling into healthcare Sending more LinkedIn messages was never the answer. Messaging the right person the week something changed for them is. Whether you build your own lists or someone on your team does it, theres an agen

I’ve seen several posts saying “Claude Pro is free for 1 year in India.” That’s not exactly true. Anthropic currently has a Claude for Startups program where eligible startups can get: ✅ 1 𝐲𝐞𝐚𝐫 𝐨𝐟 𝐂𝐥𝐚𝐮𝐝𝐞 𝐓𝐞𝐚𝐦 𝐅𝐑𝐄𝐄 ✅ 𝐔𝐩 𝐭𝐨 5 𝐏𝐫𝐞𝐦𝐢𝐮𝐦 𝐬𝐞𝐚𝐭𝐬 ✅ $1,000 𝐢𝐧 𝐂𝐥𝐚𝐮𝐝𝐞 𝐀𝐏𝐈 𝐜𝐫𝐞𝐝𝐢𝐭𝐬 ✅ 𝐔𝐩 𝐭𝐨 $45,000 𝐢𝐧 𝐩𝐚𝐫𝐭𝐧𝐞𝐫 𝐛𝐞𝐧𝐞𝐟𝐢𝐭𝐬 ✅ 𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐭𝐨 𝐞𝐥𝐢𝐠𝐢𝐛𝐥𝐞 𝐬𝐭𝐚𝐫𝐭𝐮𝐩𝐬, 𝐢𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠 𝐈𝐧𝐝𝐢𝐚 ⚠️ Important: This is NOT a general free 1-year Claude Pro offer for every individual user. I’ve put together a step-by-step guide explaining: → Who is actually eligible → How the startup program works → How to apply → What information/documents you may need → How the free Team seats work 📌 Want the step-by-step guide? Comment “CLAUDE” below and follow me. I'll drop the link in your comment's reply. #anthropic #claude #ai

This week it's free. For the next 72 hours only. It's the Dynamic Positioning Playbook: the 9-step system we use to keep positioning dynamic and true in the field. What's inside (10 tabs, ready to fill): → Map your starting point: see which decks and assets no longer match your strategy → Spot the drift: a 9-question check that points to your riskiest gaps → Build your core strategy: every claim gets a source and a date → Teach the signals: what to listen for in calls, CRM, and Slack → Run the loop: AI flags the drift, a human decides what changes → Pitch for sales: .md files to ground one pitch reps pull from, not a story AI rebuilds → Listen back (VOC): today's customer words become next quarter's positioning → Track amp; fix: a scorecard that tells you which step to fix first → Runbook amp; launch: a checklist to go live Plus a full worked example and the exact Claude setup we run it on. Why it matters: Every rep has AI now, and every AI run invents a slightly different version of your story. Your positioning isn't wrong. It gets scattered across reps, decks, and AI runs. This sheet is years of positioning work for startups and enterprise teams, packed into one file. It's the same process we run when a pitch stops landing in the field. Now your team can run it without us. Want it? 1. Comment "PLAYBOOK" 2. Connect with me so the DM reaches you Free until Sunday. After that, it's back to client-only.

One lead research and DM used to cost us about $20 👇 1/ Sorts your engager lists Code pulls the people who commented on or reacted to posts in your niche. Jev judges each row, for example whether a commenter is a buyer, a peer or a competitor. 2/ Checks what each lead is signaling Code collects job changes, hiring posts, funding news and recent posts for every profile. Jev goes through those signals one by one, so you see why someone is warm right now. 3/ Matches buyer pain to your offer Jev takes each problem a prospect posts about and picks your best-fit use case for it. You see the leads you have no angle for. 4/ Scores who's worth a DM Jev judges fit and intent on every lead. Code turns that into a 0-100 score. Only 80+ moves on. 5/ Writes the opener across 500-1,000 profiles a day Jev picks which signal each DM should open with. An LLM then writes only the DMs that passed, under 75 words each. That's the DIY version: code + Jev + a sheet, run by hand every day. We turned the same loop into Beeze AI: Capture → Enrich → Score → Message → Auto-send 20+ signals in 8 categories. 20-50 leads/day. 4 hrs/day → 10 min/week. Access below👇

They find people already showing buying signals, score them, write the message from that signal, and book the call. On a live campaign: 46% accepted. 35% replied. About 1 in 4 of those booked a call. Before the crew, my outbound looked like this: - Scrolling job titles, hoping someone needed us - Messaging people who never asked for anything - The same opener, copy-pasted 40 times a day - Follow-ups living in a spreadsheet nobody opened - Good replies going cold over the weekend - The messaging wasn't the problem. - The list was. And I was the bottleneck. So I turned the whole workflow into Wingmen. Four AI teammates. One job each. → The Scout finds people already talking about the problem you solve → The Scorer ranks them, and writes down why → The Messenger writes from their signal, not your pitch → The Closer runs the follow-ups and books the call → You get pinged the moment a call lands → Runs from your own Claude too, if that's your thing The math that made me build it: → ~10 booked calls a month per account, at a pace that stays safe → ~$7 per booked call → An SDR costs you $150+ for the same call I'm opening it to early testers. Comment WINGMEN and I'll send you a full month FREE. (Connect with me too, so I can actually DM you.) Built for agency founders and consultants who sell B2B. PS. Yes, they're birds. With goggles. Wingmen. I couldn't help myself. PS2. Repost and you jump the queue.

i’ve NEVER seen a successful cold emailer that only uses Apollo. that's how you wreck a brand new tenant in 3 weeks. Apollo gives you raw volume without ANY intent signal behind it. if you blast 5,000 contacts who have zero reason to care, you torch every inbox you spent 18 days warming up. i speak to ALL of the agencies winning in 2026. they’re running a specific 7-method intent stack right now. • Google Maps surfaces local intent. • Apify scrapes comment-level engagement signals. • Find Email cleans up the 40% of leads Apollo couldn't enrich. • Sales Navigator pulls headcount-growth triggers. when you pull from 7 angles instead of 1, the same list outperforms by 3-4x. so i packaged the entire stack. inside: • 15 copy-paste search queries across 7 intent-based methods • The exact tool stack for each (Apify, Triggery, Crunchbase, RB2B, BuiltWith, Clay) • Before/after personalization examples for every method • A Clay enrichment workflow that cleans and verifies before sending • A quick-reference table showing intent level and volume per method PS, find people who actually want what you sell. steal this.

Heres exactly whats inside. A plugin is a bundle: commands you type, skills it follows, and connectors it can reach. The HR one ships with six commands: → /draft-offer → /onboarding → /performance-review → /policy-lookup → /comp-analysis → /people-report It comes set up for Slack, Gmail, Google Calendar, Notion and Atlassian. Your HR system is not connected until your team connects it. Treat it as a first-draft engine. Before anyone runs it, write the rules down once: → remove names before you paste anything → anything touching pay or legal goes to a person → check with your admin which plugins your plan allows First drafts. A person signs. Save this for the day your team asks to switch it on.👇 Comment PLUGIN and Ill send you the Claude for HR Starter Kit.

This is the best way to give your team access to your company's brain. It's free below 👇 Most of what your company knows isn't written down. It's in four people's heads, and everyone else interrupts them to get at it. What did we quote this customer last time? What's our policy on rush orders? Has this supplier missed a deadline before? Every one of those is a message to someone who's busy. So we load it all into Claude, and put it in the channel your team already sits in. What your team can do with it: → Anyone can ask the company a question. They ask in the channel, it answers in the thread, in front of everyone, so nobody has to ask it again. → Catch up on what you missed. Back from three days off, ask it what happened in the channel and what's waiting on you. → Drafts that sound like you. A customer reply, a quote, a process doc. It has your terms and your past emails, so what comes back is close enough to send after a read. → New hires get useful faster. They ask the company its own questions for two weeks instead of tapping the person next to them. → What only lives in threads gets written down. A decision made in a channel becomes a document, so the next person doesn't have to scroll back six months to find out why. → It reaches the systems you already run. Connect it up and "has this shipped" gets answered without anyone opening the system. The playbook covers the parts that go wrong: what to load and in what order, who's allowed to ask what, and how the context stays current so it isn't quoting last year's prices next March. This is the easy half. It answers questions. It doesn't do the work yet. But every agent we build after it runs on that same context, which is why it goes first. Free access in the comments.

we've been playing with JEV for awhile and honestly think it unlocks a completely new paradigm in LinkedIn prospecting all prospecting tools in the market work like this: ICP input -> translate to search filters -> retrieve a few hundred profiles via the filters -> LLM to rank and write the problem? the most important decision in the entire process becomes the initial list and the filter translation, as you cannot message someone you never retrieved with JEV, you can flip it around you can take the entire accessible pool of people engaging in your niche on LinkedIn (post commenters, competitor engagers, industry leaders' followers) and ask tiny yes/no questions about every profile and its activity: 1. role → do they own the problem you solve? 2. company → inside your ICP on size and industry? 3. career change → new role or promotion recently? 4. hiring → posting roles your product helps with? 5. funding → just raised or got acquired? 6. engagement → commenting on posts about your problem? 7. tech stack → using tools you plug into or replace? 8. title match → would a title filter have missed them? 9. fit → a 0-100 score, only 80+ gets a DM in other words, you can now boil the ocean and evaluate the entire lead pool with your taste a much wider pool than ANY filter-based list in the market imagine an intelligent SDR having infinite time looking at EVERY profile in your market before sending a single DM feels like a fundamental shift in outbound design the DIY version: code collects the profiles, JEV answers the yes/no questions, Claude writes DMs under 75 words for the ones that pass the automated version: Beeze AI reads 500-1,000 profiles/day against 20+ signals in 8 categories and only messages the 80+ leads Access below👇

I'm giving it away for free. Every COO I work with in the 50-500 employee range is buried in the same eight workflows. They're the work that eats 15-20 hours a week and shows up on no one's job description. What I built: * 8 Claude COO workflows * Each one a production-ready prompt * Built for mid-market ops reality, not enterprise theory Covers: * Process audits (find the 20% of work that should be automated) * SOP generation (turn tribal knowledge into documentation) * KPI dashboard design (what to measure, how to report it) * Vendor scorecards (evaluate and renegotiate performance) * Capacity planning (model team load before you over-hire) * Exception triage (handle the messy 20% without breaking the workflow) * Executive reporting (board memos in 30 minutes, not 3 hours) * Quarterly team reviews (calibrated, evidence-based, defensible) Each prompt interviews you before producing output. No vague answer syndrome. You get the full toolkit. All 8 workflows. The prompts. The setup instructions. The use case examples. Plug it into your ops stack this week. Want it? Connect + Comment "COO" I'll send it over. P.S. Repost for priority access ♻️

Almost none can justify one yet. The hire is a senior salary for a role most boards can't define. The Big Four alternative is a $75K to $100K readiness assessment that ends in a deck. And either way you still need people underneath who know where AI actually pays in your industry, which is a different list for a distributor than for a law firm. So build one instead. A Head of AI Implementation agent inside Claude, with 10 specialist agents underneath it, searching for where AI fits in your business while you keep every decision about what gets built. For CEOs, COOs and GMs at companies of 50 to 500 people whose board keeps asking what the AI plan is. Here's what it runs: → Scouts: one each for manufacturing, professional services, financial services, healthcare, insurance, distribution and construction, and the ones that match your business go looking for the processes where AI pays in your industry → Classification: every find gets tagged customer-facing, internal or decision support, then split into the steps a model can run alone, the steps it drafts for a person, and the steps that stay human → Scoring: impact times feasibility, plus 4 disqualifiers that knock a process out whatever it scored → Data check: 30 random records and 5 checks on the top 3, before anyone scopes a pilot → Business case: an ROI model for the winner, downside case first, with every assumption sourced → Register: one ranked list of every opportunity in the company, with an owner and a status on each → Reporting: every Monday, one page with what the scouts found, what moved up the list, and the one decision that needs you The agents do the searching and the scoring, but none of them can contact a vendor or touch a live system, however strong the business case looks. What changes: → The $75K readiness assessment becomes a Claude setup → Nobody spends on an opportunity until it's been sorted and scored → Pilots get checked