Mehandi Islam

Mehandi IslamMay 19, 2026

Anthropic just launched Claude for Small Business. Native connectors into HubSpot, QuickBooks, Canva, Google Workspace, Microsoft 365 - with pre-built workflows for invoicing, payroll, marketing, and month-end close. Honest take from someone running an AI automation agency: This is going to kill the lower end of our industry. Anyone selling "we'll connect your HubSpot to your Google Sheets for $2,500" - that work is now a $20/month subscription. Where it doesn't compete: - Custom n8n workflows with conditional logic - Voice AI agents (VAPI, ElevenLabs) - Cold email systems with intent signal detection - Anything requiring 3+ tools that aren't in Anthropic's connector list (yet) If your agency's moat is "we connect SaaS tools to each other"... it's time to move up the stack. What are you building that Claude for Small Business can't? https://lnkd.in/gB9xYzF8

Mehandi IslamJun 17, 2026

A client's SDR was sending hundreds of LinkedIn messages a month. Reply rate barely moved. The problem wasn't volume. It was the absence of signal. No one knew which message worked, on which channel, for which target. So they kept scaling the same broken approach. We rebuilt the system on LaGrowthMachine. Reply rate landed around 20%. Here's the architecture: 1️⃣ A/B Testing layer Every campaign runs message variants against each other before scaling. We don't guess which hook works, we test it on a small batch first, then push budget behind the winner. 2️⃣ isActive? filter on LinkedIn LGM checks whether a profile is actually active before a message goes out. Sounds small. It's the difference between burning outreach quota on ghost accounts and spending it on people who'll actually see the message. 3️⃣ Real Chat Mode Messages route through a setup that reads like an actual conversation, not a sequence. This is what gets replies instead of silence or "unsubscribe." 4️⃣ Deal tracking by campaign We tied every campaign to revenue outcomes, not just reply counts. A campaign with high replies and zero deals isn't a win. This layer is what tells you which campaign to kill and which to double down on. What I'd do differently: build the deal-tracking layer first next time, not last. Knowing which campaign drives revenue should shape the A/B tests from day one, not just grade them after.

Mehandi IslamFeb 8, 2026

Last quarter, I worked with a B2B SaaS company doing 2.5M ARR. Their GTM was chaos: ↳ Sales blaming marketing for bad leads  ↳ Marketing blaming sales for not following up  ↳ RevOps caught in the middle with a broken forecasting model  ↳ Sales cycle averaging 65 days (should be 30-40) They needed an AI-GTM overhaul. Here's exactly what we did: Before (Month 1): Lead scoring: Manual (guesswork) Lead routing: Random to whoever was available Forecasting: Excel model, 50% accuracy Cadence: One-size-fits-all email/call sequence Result: 12% monthly sales growth, declining Implementation (Months 2-3): Step 1: Deploy predictive lead scoring ↳ Built AI model on their best-fit customers ↳ Trained it to spot buying signals (job changes, website behavior, funding announcements) ↳ Result: Top 20% of leads scored as "hot" were 3.2x more likely to convert Step 2: Create intent-based routing ↳ Hot leads → AE immediately (don't wait)  ↳ Warm leads → SDR nurture sequence (3-week warm-up)  ↳ Cold leads → Marketing automation (keep warm, revisit quarterly) Step 3: Automate GTM workflows ↳ LinkedIn + Email sequences triggered by buying signals  ↳ Used La Growth Machine for multichannel automation  ↳ Timing: LinkedIn message 2 hours after email open, phone call 24 hours after LinkedIn reply  ↳ Why: Meeting them on their preferred channel After (Month 4): Sales cycle: 32 days (was 65) Close rate: 23% (was 14%) Forecast accuracy: 78% (was 50%) Sales cycle length dropped by 49% Win rate on hot leads: 41% vs 9% on cold leads Monthly growth: 34% (was 12%) Key metrics: ↳ +60% revenue impact in 90 days  ↳ Sales team gained 8 hours/week back  ↳ RevOps had predictable data for the first time The real win? They stopped treating all prospects the same. Hot leads with buying signals got treated like hot leads. Cold prospects got evergreen nurture. Result: Higher conversion, lower wasted effort, predictable GTM. The lesson: AI-GTM isn't about more touches. It's about smarter touches to the right people at the right time. If your GTM feels scattered and your sales cycle is too long, we can run the same playbook for you: https://lnkd.in/gyjBPA44 #GTM #RevOps #CaseStudy #AI #SalesStrategy #B2BGrowth #DataDriven