I had 105 recorded sales calls sitting in my business. Instead of trying to remember what people said, why they bought, or why they didn’t, I used AI to analyse the transcripts for patterns. That is where this got interesting. I expected AI to help me summarise the calls. What it actually gave me was business intelligence. It helped me spot patterns around: - Price and perceived value - Unclear transformation - Risk and commitment - The need for more relevant proof And some of the findings were uncomfortable. Some of the biggest were: • Sometimes what sounds like a price objection is really a clarity problem. • Sometimes “not now” is really about risk. • And sometimes the issue is not the prospect at all. It is the way the offer has been packaged or explained. That made me realise how much valuable first-party data many of us already have sitting inside our businesses. Sales calls. Discovery calls. Customer interviews. Support conversations. DMs. AI can help us interrogate that data at a speed we simply could not manage manually. I break down the four discoveries and what I’m changing as a result in this video. Check out the full video here and let me know what business data are you sitting on that AI could help you learn from? Chelena 🖤

AI disclosure is about to become a much bigger conversation for business owners. And I don’t think the most interesting question is: “Am I legally required to tell people?” For me, it’s: “At what point would my audience expect me to tell them?” Because there’s a lot of grey area now. You use ChatGPT to tidy up a post you wrote. Probably not controversial. It writes the entire post from a few bullet points. That starts to feel different... You publish an AI-generated image. What if it looks completely real? You use an AI version of yourself to record a video you never actually recorded. You use an AI agent to reply to a prospect who thinks they’re speaking to you. That’s where this gets interesting... For those of us building businesses, expert-led brands, communities, and offers with AI, we’re going to have to make some decisions not just about what’s technically allowed, but about the relationship we want to have with the people who trust us. For me, this is going to become a major part of how we think about using AI responsibly in business. Where do you draw your line? At what point do you think someone should disclose that AI was involved?

AI is making a LinkedIn problem worse right now; and most of us building AI offers are walking straight into it. A few weeks ago I onboarded a founder who'd been working with a strategist using AI tools to mass-produce his content. Daily posts. Beautiful carousels. Reels. The whole machine running at full tilt. Three months in: → 500,000+ impressions → Hundreds of comments → Zero sales calls → Zero inbound enquiries → Zero pipeline The AI wasn't broken. It was doing exactly what it was told. The strategy...now that was broken and AI just made it broken at scale. Here's the trap I'm watching midlife founders walk into right now: AI makes content cheap to produce. Which means everyone is producing more of it. Which means the bar for what actually converts on LinkedIn has gone UP, not down. More content ≠ more leads. More reach ≠ more revenue. If you're building, launching, or monetising AI offers and you want LinkedIn to actually move the needle for you, here's what's working across the client accounts I'm watching every week: 1. Use AI to research your audience, not just to write to them. Feed your call transcripts, sales objections, DM conversations, and customer feedback into a model. Ask it to surface the patterns you're missing. The questions your buyers are actually asking. The language they use that you're not using back. That's where the gold is; most people skip it and go straight to "write me a post." 2. Use AI to clone your voice, not replace it. The "sounds robotic" problem is real, but it's solvable. Build a voice model from your actual content — your past posts, voice notes, livestream transcripts. Then use it as a writing partner, not a writing replacement. The output should sound like you on your sharpest day, not like a LinkedIn template generator. 3. Build for depth, not just frequency. LinkedIn is rewarding learning content right now — articles, in-depth videos, newsletters that actually teach. Five posts a week of shallow AI-generated takes won't out-perform one piece of long-form content that genuinely helps your buyer think differently. AI is brilliant at helping you go deep faster. Use it that way.

🗓 June 15–17, 2026 We’ve got an incredible lineup of speakers coming in 🔥Here’s a sneak peek at who you’ll be learning from: Here’s your full speaker lineup: @Julie Butler — Get 100–300 Email Subscribers with One Simple Event + AI Calvin Hollywood — Skool + AI @Cristal Vancarson — Remote Headshot Photography @Bansari Panchal — How I Use AI to Run My Skool Community on Autopilot @Zokai Robinson — AI for Intuitive Builders @Berlyn Komar — Why Your AI Content Isn’t Converting @Olivia Radcliffe — A Day In The Life Of... @Martine Brown — Canva AI 2.0: 3 Touchpoints @Janay Trevillion — Vibe Coding & Stolen Time: Build a Business Backend That Runs While You Rest @Matthew Burns — Discoverability in the Age of AI @Ellyn Schinke — Stay Ready, Not Busy @Benjamin Ewert — YouTube + Claude @Lorri-Lynn Roche — Using AI Without Overwhelm @Destini Copp — Self-Selling Funnel AI System @Monna Tang — The Calm CEO’s Profit Plays @Valarie Smith — The Midjourney Style Anchor @Stacy Covitz — $5/Day Visibility With Meta Ads @Jen Hill Myers — AI Magic For Launch Challenges @Micki Kosman — Your Personal AI Support Team @Morag Kent — Make AI Sound Like You @Christina Rava — Sales Page in 7 Minututes @Nicky Williams — AI Can't Handle Your Energy @Jeanette Stein — Stop Rebuilding Your Business

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