Exemples de lead magnets LinkedIn en enterprise software

AI didn’t create more SaaS founders. It created more almost-launched founders: 271 SaaS founders are joining me today. See who: luma.com/fp17 I'm showing them how to build with AI and add 300K of revenue in 30 days. Sharing the workbook for the next 24 hours only.  (It's a free PDF, structured like a Harvard business case study review) These are not original ideas. I did the painstaking work of interviewing thousands of founders. Like a detective, I was hunting for the AI tactics working today. These are the top 6:   $30M revenue at 19 years old: How Cal AI built a 250 influencer army that prints downloads every single day $50M revenue data fortress: How Vantaca built a dataset no competitor can touch or replicate 600% revenue growth: How 1Mind cleared Fortune 500 legal when every other AI company got rejected $9M golf course software: How Club Caddy mapped every course in North America and sold to Constellation Software on $600k raised $15M revenue, zero dilution: How Flipsnack wired into enterprise IT stacks so deeply that removal would break compliance $3M revenue viral loop: How PodPlay built a network effect hiding inside a sports tech product RSVP here to join us today: luma.com/fp17 If you want the free "30 Days to add $300k with AI" Playbook: Like this post Comment "Build with AI" or "Give me the Workbook!" below I will DM you the guide via LinkedIn at 6pm EST tonight. Make sure we are connected so I can reach you.

Most ERP integrations stall for months. We integrated AI with SAP, Oracle, and Microsoft Dynamics in weeks using our Smart ERP Loop. Most companies move slowly because teams overplan, rely on manual workflows, and test too late. We changed the system. AI agents handled: Approvals & reporting  • Data syncs across ERP  • Repetitive workflows    Teams focused on decisions and strategy. What that unlocked:  • No more manual chaos  • Clear AI-driven workflow automation  • Early testing with bottlenecks flagged upfront Instead of “integrate and hope,” we followed a simple loop: 🟦 Define clear AI outcomes 🟦 Map workflows and orchestrate agents 🟦 Test, refine, and scale Weeks later, the AI-enabled ERP was live. Smooth. Efficient. Ready to scale. This is the same system we’re refining at EaseZen Solutions for enterprise teams in Law, Retail, and Automotive. Curious how the Smart ERP Loop can streamline your SAP/Oracle/Dynamics? Comment “Show me the Loop.”

I just built a fully working AI voice agent for my company. In 5 minutes. No code. No sales call. No 6-month enterprise deployment. Just pasted my website URL and watched it build itself. --- Here’s what happened: I got early access to PolyAI's agent wizard and tested it on my own company, Gerra (we build AI + robotics training data infrastructure). I entered our website. Agent Wizard scraped the entire site — FAQs, product info, company details — and auto-built a voice agent. Then I called the agent. And it was way more conversational than I expected: → Accurately explained what Gerra does and the types of data we provide → Answered technical questions about our multimodal datasets → Offered to book a demo when it didn’t have specific info → Pulled up actual available time slots and walked me through scheduling → Natural back-and-forth — no robotic pauses, no awkward delays 5-minute setup. Fully functional voice agent that knows my entire business. This is the same tech behind: • Marriott’s reservation lines • Caesars Palace and major Vegas casinos • Gordon Ramsay’s restaurant network • FedEx, PG&E, and 100+ enterprises PolyAI handles 2,000+ live deployments, millions of calls daily in 24+ languages. 391% ROI. $10.3M average savings per enterprise. And they just made this accessible to anyone with a website. I recorded the full walkthrough + live call — watch the video. Want the Complete Voice Agent Deployment Guide? 1️⃣ Connect with me 2️⃣ Comment "AGENT" below + Like ♻️ Repost for priority access

I just built a fully working AI voice agent for my company. In 5 minutes. No code. No sales call. No 6-month enterprise deployment. Just pasted my website URL and watched it build itself. --- Here’s what happened: I got early access to PolyAI's agent wizard and tested it on my own company, Gerra (we build AI + robotics training data infrastructure). I entered our website. Agent Wizard scraped the entire site — FAQs, product info, company details — and auto-built a voice agent. Then I called the agent. And it was way more conversational than I expected: → Accurately explained what Gerra does and the types of data we provide → Answered technical questions about our multimodal datasets → Offered to book a demo when it didn’t have specific info → Pulled up actual available time slots and walked me through scheduling → Natural back-and-forth — no robotic pauses, no awkward delays 5-minute setup. Fully functional voice agent that knows my entire business. This is the same tech behind: • Marriott’s reservation lines • Caesars Palace and major Vegas casinos • Gordon Ramsay’s restaurant network • FedEx, PG&E, and 100+ enterprises PolyAI handles 2,000+ live deployments, millions of calls daily in 24+ languages. 391% ROI. $10.3M average savings per enterprise. And they just made this accessible to anyone with a website. I recorded the full walkthrough + live call — watch the video. Want the Complete Voice Agent Deployment Guide? 1️⃣ Connect with me 2️⃣ Comment "AGENT" below + Like ♻️ Repost for priority access

I've spent the last 2 years of building the most sophisticated retrieval engine for company knowledge I shared it all in a webinar yesterday 1/ Why early RAG broke and got such bad press In 2023, teams were jamming entire documentation into GPT-3.5 and getting garbage back. The models were weak and got even weaker with large messy context. Now GPT-5 and Opus 4.6 have gotten 10x smarter and can handle much more complex retrievals and non-deterministic tasks. 2/ There are many R(etrievals), and you need them all → your good old keyword search (find "Project X" or close) → semantic (finds "churn" when you search "customers leaving") → structured (SQL queries) → API/MCP retrieval (live data from Salesforce) → Hybrid ("All tickets in Intercom in last 2 weeks mentioning pricing issues") We demoed all four running together in Super. One search bar, four systems working in parallel. 3/ There are 4 levels of RAG sophistication. Naive is embed, match, generate. That's where most teams stop. Multi-loop re-ranks results across multiple passes. Agentic means the AI picks which tools to query (we showed this live with MCP). Self-correcting retries when it fails and builds memory. 4/ The build vs buy question comes down to permissions and data size. → If your data fits in a context window, just paste it into Claude. → If you know where the data is, use API or MCPs and automations. But if the answer is "somewhere", scattered across 50 tools with different access rules, you're about to spend 8 months building permission-aware retrieval. 90% of our customers tried building it before coming to Super. 5/ Native AI connectors lack semantic + company understanding ChatGPT's Slack integration can't do semantic search. It doesn't know that your team uses "P0" to mean urgent or that sales decks live in a specific Drive folder structure. It just searches filenames. Same with Claude's Drive connector. They're built for personal use, not enterprise knowledge. 6/ The part that got the most questions was loose orchestration. Instead of hardcoding "search Slite, then check Slack, then query the CRM," you give the AI dynamic tools it can call and say "find out why this customer churned." The AI figures out the steps. One person asked if we had a flowchart. We don't. That's the point. Let me know if you'd like the recording and I'll send it! And of course, thanks for having me Alexandre Kantjas, Pierre-Yves Garcia and 9x!😉

I counted 9 collaboration tools running inside our team last quarter. Every one had a reason. None of them talked to each other.   Files lived in 4 places. Three versions of the same doc. Nobody knew which was real. A decision got made on stale data.   Your team isn't unproductive. Your toolstack is ungoverned.   𝗧𝗵𝗶𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗽𝗲𝗼𝗽𝗹𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗜𝘁'𝘀 𝗮𝗻 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺.   Tool sprawl is the symptom. The root cause is collaboration infrastructure with no control plane.   ↳ When files live in 4 places, decisions get made on stale data ↳ Version conflicts and duplicated work compound silently ↳ Scattered access means untracked permissions and real compliance exposure ↳ The governance debt only shows up when something breaks ↳ And when AI agents start querying ungoverned files — errors compound at machine speed   Nobody planned it this way. It grew. One tool at a time. One workaround at a time.   41% of enterprises are now actively consolidating their stacks. They're not adding tools. They're cutting them.   𝗬𝗼𝘂 𝗱𝗶𝗱𝗻'𝘁 𝗵𝗶𝗿𝗲 𝗯𝗮𝗱 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗼𝗿𝘀. 𝗬𝗼𝘂 𝗯𝘂𝗶𝗹𝘁 𝗮 𝘀𝘁𝗮𝗰𝗸 𝘄𝗶𝘁𝗵 𝗻𝗼 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗽𝗹𝗮𝗻𝗲.   𝗪𝗵𝗮𝘁 𝗮 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗽𝗹𝗮𝗻𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝘀   ↳ Single source of truth. One version, always current. ↳ Role-based access by design, not patched on after a breach ↳ Audit trails on every document interaction ↳ Governance built into the foundation — not bolted on after something breaks   Zoho WorkDrive reframed how I think about this.   Not because of features. Because of architecture. It's the content layer that survives consolidation — because governance is built in, not retrofitted.   You can't govern what you can't see. The teams winning in 3 years aren't adding more tools. They're fixing the foundation first.   #ZohoPartner   PS: How many tools is your team running just to stay aligned? Drop CIRCUS in the comments for the link.

Listen. AI can Read files. Push code. Connect to servers. Trigger actions in production. Access databases Expose API keys Read customer PII Modify cloud infrastructure Delete or overwrite critical files That’s powerful and risky. That’s why this conversation was important. I recently hosted Rajaram Srinivasan and Vignesh Subbiah – founders of Unbound Security (YC-backed) on my podcast. In short, Unbound Security helps enterprises use LLMs and AI agents safely – by adding a security layer that prevents data leaks and unsafe actions, without slowing employees down. 𝗪𝗲 𝘁𝗮𝗹𝗸𝗲𝗱 𝗮𝗯𝗼𝘂𝘁: - How AI coding agents actually work in real companies - How data leaks happen with LLMs (even on paid plans) - Private vs public LLMs (and the real cost behind them) - How non-technical teams are now building software using AI - Their journey getting into Y Combinator (after 3 attempts) - What founders should focus on when building AI startups 𝗜𝗳 𝘆𝗼𝘂 𝗮𝗿𝗲: - An aspiring founder - Building a career in AI / ML / data / software engineering - Or working on AI agents / LLM products This episode will be useful for you. 🎧 𝗖𝗵𝗲𝗰𝗸 𝗳𝘂𝗹𝗹 𝗽𝗼𝗱𝗰𝗮𝘀𝘁 𝗵𝗲𝗿𝗲👇 https://lnkd.in/g_mEtAb4 𝗕𝗢𝗡𝗨𝗦: $50 in Anthropic credits for the first 50 sign-ups. If you are interested, comment "UNBOUND" and I will share the sign up details with you. #podcast #ai #llm #security #unbound

The AI boom has created trillions in market value. But it has also created enormous economic exposure. Who actually makes money. Who carries the risk. And who quietly absorbs the downside when the AI story doesn’t play out. Over the last two years, trillions in market cap have been built on assumptions about AI. But most conversations still focus on: – models – benchmarks – demos – hypothetical futures That misses the real question: How does AI translate into durable economic value — today? So I stepped back from the hype and treated AI as what it really is: An economic system. I analysed 10 of the most AI-exposed companies in the world, using the same lens across each one — from infrastructure and cloud, to platforms, enterprise software, and AI-native players. Not to predict “winners” based on innovation. But to understand structure. Who captures value. Who bears risk. And what breaks first if expectations reset. The result is a short, evidence-based report: Understanding the AI Economy Inside, you’ll see: – The four real ways companies make durable money from AI – Why distribution beats model quality — again and again – How AI commoditisation shifts moats instead of destroying them – Where downside risk shows up first when the AI narrative weakens – Why AI concentrates power rather than flattening it It’s a framework for leaders, investors, and operators who need to make decisions before the narrative turns. Comment “AI ECONOMY” and I’ll send it over. If this resonates, feel free to share it. Someone in your network is probably still making AI decisions based on the wrong mental model. ♻️ If this resonated, share it. Someone in your network needs this reminder today. 🔔 Follow Alex Issakova for more clear-eyed takes on the untold stories of AI.

We’re going hard on NRF this year. Here’s what you can expect from us next week... ⏬ You’ll find the Versori team on booth 913, spending time understanding where integrations are causing pain and walking through, practically, how Versori helps remove that friction. We’re also running free integration audits on the stand, led by George Goodfellow. You’ll walk away with: - A spreadsheet directly comparing your integrations against competitors - Clear gaps identified in your current setup - Practical actions to strengthen your integration offering Finally, we're hosting an invite-only executive dinner with Spur, Fluent Commerce, AIONOS, and Marketplacer. The focus: how AI is actually being used across marketplace tech stacks: seller onboarding, listing quality, orders, and returns. You don’t need an NRF ticket to join. Oh, and I'm speaking on the panel too. DM me or Sophie Burgess to get an invite to the dinner.

𝗧𝗵𝗲 𝗣𝗮𝗹𝗮𝗻𝘁𝗶𝗿 𝗢𝗿𝗶𝗴𝗶𝗻 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 (2003–2013): How Software Met Consulting from Day One From day one, Palantir paired software with forward-deployed engineers on-site, wiring messy data, building workflows with users, and turning custom work into reusable product. High-friction, expensive, looked unscalable. Still became one of the stickiest enterprise businesses ever. That playbook matters more now than it did back then. AI is pushing startups back into services (implementation, workflows, change management). The winners won’t be the ones with the best demo. They’ll be the ones who can land inside a real org, deliver a win fast, and productize what they learn. I researched Palantir’s early model and wrote the full breakdown. It includes: 1. 𝗧𝗵𝗲 𝗢𝗿𝗶𝗴𝗶𝗻 𝗦𝘁𝗼𝗿𝘆: A Software Solution to Intelligence Failures 2. 𝗣𝗮𝗹𝗮𝗻𝘁𝗶𝗿 𝗚𝗼𝘁𝗵𝗮𝗺: Early Development and Deployment 3. 𝗛𝗼𝘄 𝗣𝗮𝗹𝗮𝗻𝘁𝗶𝗿 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝘁𝗵𝗲 𝗛𝘆𝗯𝗿𝗶𝗱 𝗠𝗼𝗱𝗲𝗹: Software + Consulting from Day One 4. 𝗣𝗮𝗹𝗮𝗻𝘁𝗶𝗿'𝘀 𝗘𝗮𝗿𝗹𝘆 𝗖𝗹𝗶𝗲𝗻𝘁 𝗔𝗰𝗾𝘂𝗶𝘀𝗶𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗢𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 5. 𝗠𝗲𝘁𝗿𝗼𝗽𝗼𝗹𝗶𝘀 𝗮𝗻𝗱 𝗙𝗼𝘂𝗻𝗱𝗿𝘆: Pivoting to the Enterprise 6. 𝗣𝗮𝗹𝗮𝗻𝘁𝗶𝗿'𝘀 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗮𝗻𝗱 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 𝗶𝗻 𝗘𝗮𝗿𝗹𝘆 𝗗𝗮𝘆s 7. 𝗟𝗲𝘀𝘀𝗼𝗻𝘀 𝗳𝗼𝗿 𝗙𝗼𝘂𝗻𝗱𝗲𝗿𝘀, 𝗜𝗻𝘃𝗲𝘀𝘁𝗼𝗿𝘀, 𝗮𝗻𝗱 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁𝘀 Comment "Palantir" and I’ll send you the link 👇

𝗧𝗵𝗲 𝗣𝗮𝗹𝗮𝗻𝘁𝗶𝗿 𝗢𝗿𝗶𝗴𝗶𝗻 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 (2003–2013): How Software Met Consulting from Day One From day one, Palantir paired software with forward-deployed engineers on-site, wiring messy data, building workflows with users, and turning custom work into reusable product. High-friction, expensive, looked unscalable. Still became one of the stickiest enterprise businesses ever. That playbook matters more now than it did back then. AI is pushing startups back into services (implementation, workflows, change management). The winners won’t be the ones with the best demo. They’ll be the ones who can land inside a real org, deliver a win fast, and productize what they learn. I researched Palantir’s early model and wrote the full breakdown. It includes: 1. 𝗧𝗵𝗲 𝗢𝗿𝗶𝗴𝗶𝗻 𝗦𝘁𝗼𝗿𝘆: A Software Solution to Intelligence Failures 2. 𝗣𝗮𝗹𝗮𝗻𝘁𝗶𝗿 𝗚𝗼𝘁𝗵𝗮𝗺: Early Development and Deployment 3. 𝗛𝗼𝘄 𝗣𝗮𝗹𝗮𝗻𝘁𝗶𝗿 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝘁𝗵𝗲 𝗛𝘆𝗯𝗿𝗶𝗱 𝗠𝗼𝗱𝗲𝗹: Software + Consulting from Day One 4. 𝗣𝗮𝗹𝗮𝗻𝘁𝗶𝗿'𝘀 𝗘𝗮𝗿𝗹𝘆 𝗖𝗹𝗶𝗲𝗻𝘁 𝗔𝗰𝗾𝘂𝗶𝘀𝗶𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗢𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 5. 𝗠𝗲𝘁𝗿𝗼𝗽𝗼𝗹𝗶𝘀 𝗮𝗻𝗱 𝗙𝗼𝘂𝗻𝗱𝗿𝘆: Pivoting to the Enterprise 6. 𝗣𝗮𝗹𝗮𝗻𝘁𝗶𝗿'𝘀 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗮𝗻𝗱 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 𝗶𝗻 𝗘𝗮𝗿𝗹𝘆 𝗗𝗮𝘆s 7. 𝗟𝗲𝘀𝘀𝗼𝗻𝘀 𝗳𝗼𝗿 𝗙𝗼𝘂𝗻𝗱𝗲𝗿𝘀, 𝗜𝗻𝘃𝗲𝘀𝘁𝗼𝗿𝘀, 𝗮𝗻𝗱 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁𝘀 Comment "Palantir" and I’ll send you the link 👇

I've been building AI agents for 18 months. The hardest problem wasn't the model. It was the files. Contracts buried in folders. Compliance reports nobody could locate. Meeting recordings that held critical decisions — watched by no one. Sales decks shared 6 versions ago. LLMs are only as intelligent as the content they can access. If your files are unstructured, siloed, and ungoverned — your agents are flying blind. Zoho 𝗪𝗼𝗿𝗸𝗗𝗿𝗶𝘃𝗲 𝟲.𝟬 𝗷𝘂𝘀𝘁 𝗮𝗱𝗱𝗿𝗲𝘀𝘀𝗲𝗱 𝘁𝗵𝗶𝘀 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆. 𝗬𝗼𝘂𝗿 𝗳𝗶𝗹𝗲𝘀 𝗮𝗿𝗲𝗻'𝘁 𝗮 𝘀𝘁𝗼𝗿𝗮𝗴𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘆'𝗿𝗲 𝗮 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗪𝗵𝗮𝘁'𝘀 𝗻𝗲𝘄 𝗶𝗻 𝟲.𝟬 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝘁𝗼 𝗔𝗜 𝗯𝘂𝗶𝗹𝗱𝗲𝗿𝘀 ↳ 𝗠𝗖𝗣 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻. Claude, OpenAI, Copilot, and Cursor now connect directly to WorkDrive. Your agents retrieve documents, update records, and trigger workflows from a single natural-language instruction across 950+ apps. Your file system is now part of your AI stack. ↳ 𝗔𝘀𝗸 𝘆𝗼𝘂𝗿 𝗳𝗶𝗹𝗲𝘀 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀. Zia AI lets you query PDFs, audio, and video in natural language. A 50-page contract answers you in seconds. ↳ 𝗔𝘂𝗱𝗶𝗼 𝗮𝗻𝗱 𝘃𝗶𝗱𝗲𝗼 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝘁𝗿𝗮𝗻𝘀𝗰𝗿𝗶𝗯𝗲𝗱. Meetings, interviews, training sessions. Searchable, summarized, structured. Knowledge locked in playback is now part of your content layer. ↳ 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗗𝗟𝗣. Context-aware data loss prevention that detects PHI and sensitive content before it leaves your control. Governance at the infrastructure level. Files stop being endpoints. They become active, queryable, governed intelligence. Most enterprises are still pointing AI at unstructured chaos. WorkDrive 6.0 fixes the foundation. #zohopartner PS: Are you connecting your AI agents to your file system yet? Drop 𝗙𝗜𝗟𝗘𝗦 in the comments.

Vos équipes vibe codent déjà. Vous ne le savez juste pas encore 3 calls de découverte en 2 semaines 3 fois le même constat Des apps vibe codées par les métiers. Déjà en prod. Sans que la DSI soit au courant CRM maison, outils de suivi, dashboards. Construits en quelques heures avec Base44, Bolt ou Lovable Et honnêtement ? On ne peut pas leur en vouloir. C'est rapide, c'est bluffant, ça débloque des sujets qui traînaient depuis des mois Sauf que derrière : • Données métier sur des serveurs dont personne ne connaît la localisation • Zéro contrôle d'accès • Zéro traçabilité • La DSI qui découvre le truc 3 mois plus tard Shadow IT, version 2026 Le bon réflexe c'est pas de bloquer. C'est d'encadrer Prompter une app c'est pas du software engineering. L'architecture, la sécurité, les droits d'accès, la scalabilité, un prompt ne résout pas ça C'est exactement ce qu'on construit chez YoungData Une plateforme qui centralise les apps métier vibe codées. Déployées dans le tenant Azure du client. Données chez vous, dans votre infra, en France. 200+ apps en 5 ans, on sait ce qui tient et ce qui casse Ce sujet va exploser dans les 12 prochains mois J'ai compilé un guide : comment bien vibe coder en entreprise et les risques à connaître avant de se lancer. Commentez "GUIDE" et je vous l'envoie👌 #Azure #IA #ShadowIT #Vibecoding #DSI