A real example of how non-AI B2B founders fall into hype and lose momentum. I’m betting you’ve been here. Last week, ClawdBot launched. Within 48 hours, it was everywhere. TikTok. X. LinkedIn. Instagram. “Replaces entire teams.” “Solo founder, $M scale.” “Fully autonomous.” I tried it myself. It’s powerful. Power ≠ priority. Shiny tools don’t automatically reflect on your revenue, team speed, or delivery quality. The AI space moves faster than any industry I’ve seen. Even Elena Verna called this out on Lenny’s Podcast. What actually works is boring clarity. Ask this instead: Where do deals get stuck in your pipeline? Onboarding? Delivery? Where is your team bleeding hours every week? Admin? Support? Follow-ups? What task survives only because “this is how we’ve always done it”? Copy pasting data. Sheets to CRM. Manual checks. Answer those first. Then pick ONE. While fixing it: you might use AI a few times you might use basic automation Most of the leverage comes from decision redesign. Fixing how work flows. This pattern shows up in almost every one of the 30+ systems I’ve implemented. P.S. ClawdBot is strong. Adoption takes time. Set it up once, then go fix your real growth constraint. Over time, tools adapt. Bottlenecks kill momentum fast.

B2B owners think they lack AI talent. Most of the time, they don’t. After building 30+ internal AI systems and talking to dozens of execs, I noticed a pattern. Teams spend months and serious money hiring AI roles they never needed. The cost stacks up fast: expensive salaries long hiring cycles recruiter fees Yet the teams actually moving forward with AI are doing something simpler. They change how work is structured. I call this DECISION REDESIGN. The wrong assumption Being AI-first means hiring AI specialists. That belief slows teams down. What works instead 1) Redesign roles around human strengths Most roles are bloated with repetitive work. AI should remove that. Free people to focus on: decisions context communication If AI adds work, you designed it wrong. 2) Teach teams how to work with AI Teams should be comfortable using modern tools. More important than tools: They must know when to use automation and when to stop it. Weekly internal sessions work well. Short. Practical. Role-specific. 3) Assign clear ownership One person owns AI adoption. Not strategy slides. Not tools. Adoption. Someone who understands how work actually happens and has authority to change it. This is the most critical part. We once shipped a lead qualification system that worked perfectly. Technically flawless. And then nothing happened. Slowly, the team stopped using it. Why? No owner. No one responsible for making the change stick. TL;DR Decision redesign comes before AI transformation Making your existing team AI-native beats hiring an AI-native team Tag the person responsible for AI adoption in your team, if you have one.

“We’re ready to transform our business with AI.” That’s a lie most B2B teams tell themselves. I know because I’ve heard it from 20+ execs in the last few months. Most B2Bs define “AI readiness” as: hiring AI engineers buying more tools waiting for exec buy-in running prompt workshops None of that makes you AI-ready. AI readiness has nothing to do with: best tools big headcount high budgets training everyone on prompting Real AI readiness looks boring. It looks like: your data is not scattered across sheets, inboxes, and Slack threads your workflows actually exist and can be explained end to end there’s one painful, repetitive process everyone agrees to fix seniors are willing to adapt fully, not “experiment” on the side AI mirrors what you feed it. Clean data in → strategic outputs Messy inputs → automated chaos Quick self-check for B2B leaders thinking about “introducing AI”: → If I asked for last quarter’s customer data, could you give me one source of truth without reconciling five files? → If a new hire joined tomorrow, could they understand how work flows without asking three people? → Is there one traditional task your team keeps doing just because it’s always been done that way, even though everyone hates it? If these questions feel uncomfortable, notice that. That discomfort is the signal. The move right now is not adding AI. The move is redesigning decisions, data, and workflows. Drop what broke when you ran this self-check. I’ll help you think through the fix in the comments.

1) Translate everything into dollars Nobody buys “AI automation for ops” “I do X for Y” They buy outcomes. Example “You’re leaking ~$80k per month because X causes Y.” Your job is translation. Problem → consequence → money. If the problem exists, what is it costing them in: revenue time headcount risk People love numbers. On marksheets. On dashboards. On offers. 2) Be hyper-specific “Founders at B2B SaaS with 100–500 employees” is still vague. Strong offers sound narrow: who it’s for what exact pain it removes how much value it adds When someone reads it, the reaction should be: “This was clearly built for me.” If it feels generic, it is. 3) Increase perceived value without changing scope Value isn’t just the core deliverable. It’s everything around it: Slack or WhatsApp access weekly strategy calls shared dashboards audit docs SOPs they keep forever extra assets that compound results Yes, slightly more work. Also higher intent. Higher price. People pay more when they feel looked after. 4) Kill risk Most buying hesitation comes from risk: money time effort data Guarantees flip risk back to you. Example “I’ll deliver X within Y days or you pay $0.” You might refund one client. You’ll close five more. Math works. 5) Productise early Once you know: minimum viable deliverables exact outcomes common objections You lock the scope. That’s how you scale without burning time or quality.

Step 1 — LASER TARGETING “Founders at tech startups” isn’t an ICP. Go deeper: what’s their biggest pain right now does it feel urgent, or just interesting what’s the actual cost of doing nothing Then go narrower. Who needs this today, not “someday”. Step 2 — TRACK SIGNALS Outbound lives and dies on timing. Signals look like: hiring for X — scaling pressure recently raised — expectation pressure new role / promotion — performance pressure operational changes — volatility public pain — visible cracks No signal = low intent. Step 3 — PICK THE CHANNEL (MIX) Email. LinkedIn. Or both. Different context — different mindset. Multichannel wins. It also costs more. Choose intentionally. Step 4 — HYPER-PERSONALIZE Not “Saw you went to X school”. Real personalization = you understand their problem better than they do. Examples. Appreciation-based Hope you don’t mind — I took a closer look at your [asset] around [focus area]. The way you handle [specific decision] is rare. Pain-based Saw you’re doing [work to fix problem]. Before you do [high-cost mistake], I wanted to share something. I recently solved this exact issue for [case]. Step 5 — HUMAN COPY Never say “write me better copy”. Use structure. Hey {{firstName}}, {{real personalization}} Who I am / what I do Why I’m reaching out Proof or de-risk Soft CTA — {{Name}} ≤ 80 words. Always. Step 6 — INFRASTRUCTURE Deliverability > copy. Set up: DKIM DMARC proper warm-up Bad infra kills good messaging. Step 7 — FOLLOW-UPS Not “just bumping this”. Every follow-up adds new value: another case insight guarantee 5–7 minimum. Step 8 — VOLUME × ITERATION Change one variable at a time: opener offer CTA follow-up Track everything. Step 9 — CONSISTENCY This is where people quit. This is also where patterns show up. Stay long enough to see what actually works. Outbound = volume + precision + iteration.