It’s the start of 2026, and a lot of teams expect outbound to feel different New tools New AI Fresh dashboards But for many, it already feels familiar Replies are inconsistent Pipelines spike then disappear Good weeks don’t repeat That usually points to one thing The system hasn’t changed AI makes sending faster but the work before sending is still messy -Lists still have bad data -Reps still guess who matters -Messages still go out at the wrong time Here are 3 AI workflows that quietly fix broken outbound 1. Clean → Score → Send Clean the list and remove dead emails, job changes, wrong roles and only reach out when someone actually looks ready Example Prompt: “Clean this list, remove invalid emails, detect job changes, and score each contact 1–10 based on intent signals. Return only the top 20% highest-signal prospects.” 2. Rewrite → Compare → Improve Generate a few angles instead of one “good” email and keep the version people reply to Example Prompt: “Rewrite this cold email in 5 variations: shorter, direct, conversational, trigger-based, and CTA-focused. Then explain which variation has the strongest clarity and relevance.” 3. Predict → Prioritize → Personalize Surface who’s most likely to respond, then personalize using real movement instead of fillers Example Prompt: “Identify which prospects show the strongest buying signals and create one personalized opening line for each based on their recent activity or role change.” New year energy only goes so far if the system stays the same P.S. What’s the first thing you’re cleaning up in 2026?

Your outbound campaigns are failing because of one thing you're ignoring. Most people obsess over messaging and timing. They spend hours perfecting their copy. Then wonder why their campaigns tank. The real problem? Your lead list is built on garbage data. Here's what actually happens: You buy 1,000 emails from a provider. You load them into your campaign. Then reality hits: - 50% are catch-alls - Bounce rate climbs to 15% - Domain reputation tanks - Inbox placement drops - Campaign dies You didn't fail at messaging. You failed at the foundation. Inside every lead list, there's a hierarchy: Lead List → Data Enrichment → Email Finding Most people stop at the top. The winners? They obsess over the bottom. Finding the right email is everything. Because quantity means nothing if the emails don't work. Here's how to fix it: 1. Stop chasing volume. One real email beats 10 catch-alls. 2. Test your provider's accuracy. Send to 100 emails. Track bounces. If it's over 5%, switch. 3. Look for enrichment stacks that do it all. APIs for scraping. Built-in databases. Profile finders. One platform beats juggling five tools. 4. Automate the flow. Manual enrichment kills your time. APIs handle it while you sleep. I've tested multiple providers. The ones that work don't brag about numbers. They deliver emails that reach real inboxes. Icypeas is one example that does this right. Not just email finding. Full enrichment stack in one place. I use it for my waterfall enrichment in Clay. Your outbound is only as strong as your weakest pillar. Fix the foundation first. Everything else gets easier. | 58 comments on LinkedIn

The tests are in and we are not allowing generic AI personalizations based on company descriptions as first lines anymore. I could prove this raised reply rates but recently, in most markets, it falls flat. We try to always lead with some kind of signal or pain point that we research but if we can't do that, we would fall back to a company description based personalization. Something like, "I saw you help fitness coaches retain clients and I wanted to get connected." We used to see increased reply rates when we personalized first lines with AI-generated content scraped from company websites. Now the data is pointing the opposite direction. Sophisticated buyers recognize generic AI personalization instantly which hurts your message: - "Hey, I saw how you helped fitness enthusiasts breathe better..." - "I noticed you're focused on helping local businesses..." - Generic references to their company description They've seen it a thousand times. Here's what we do instead: If we have a real signal: We use it. Something specific, custom-scraped, that ties directly to their problem and our value prop. **If we don't have a signal:** We treat cold email like a banner ad. Put the offer in the preview text. Make it punchy. No fake personalization. Think about it: When someone's scrolling on their phone or web browser, they see the subject line + preview text together. That's your click opportunity. Stop wasting time generating first lines from company descriptions. Unless you have custom research that goes well beyond what's publicly available on their homepage, foregoing AI personalization and writing punchy, split-tested first lines performs better. We've seen this deteriorate over the past six months across our customer base and as someone that would suggest it based on the data, I want to correct the record and say we're out of it now.

Here's how to use Clay to automate graphic design assets: This table is actually super simple to build, and only really requires: 1. Clay integrations to find the data you'd like to include in your asset 2. A Templated HTTP API call Using just these two things, you can create event invitations, webinar graphics, social assets, and more 🚀 Highly recommend this use case for anybody in MarOps looking to get started with Clay- big shoutout to Akansh Gupta for his help with this one. Here's a video walking through it below ⬇️

Top outbound sales tech in 2025 👇 Clay - orchestration layer for all your GTM workflows Smartlead - email sequencer for high volume outreach with optimal deliverability Apollo - still the goat when it comes to old school list building Prospeo.io - if you could only use one email finder, this is the one DiscoLike - build lookalike lists of your best customers BounceBan (Verify risky emails without sending messages) - validate an additional 20% of emails with catch-all validation Claude - the best LLM for writing your sales copy Any I missed?

Cold email and LinkedIn DMs serve the same goal - but they work in slightly different ways. We’ve run thousands of both everyday, and here’s what we’ve learned 👇 1️⃣ Copy Cold emails need to sound professional and stripped down. LinkedIn DMs work best when they sound like something you’d actually say. 2️⃣ Volume Cold email = scale. 20–30/inbox/day. LinkedIn = slower pace. Around 200 new connections/week/profile. 3️⃣ Rules Email success comes from relevance and proof - direct, simple, and credible. LinkedIn success comes from consistency - showing up, commenting, and sharing useful content. 4️⃣ Follow-up Email: 3-5 value-led touchpoints. No empty followups. LinkedIn: max 3 messages, spaced out, keep it social. 5️⃣ Tools & Testing Email: test multiple versions - value, case study, pain question. LinkedIn: test different DMs at scale, double down on what gets replies. 6️⃣ Results & Setup Cold email: higher volume, lower reply rate, easy to scale. LinkedIn: smaller volume, higher engagement, harder to scale. Both channels work - but for different reasons. Email is the engine. LinkedIn is the accelerator. The best outbound strategies use both — working together, not competing. Learn how to leverage both at scale in 2025: https://lnkd.in/eN7qinfB | 54 comments on LinkedIn

This cold email will never stop working. It’s an example email that an agency or freelancer selling ad creative services can send to DTC brands. Why I love it: 1. First line makes it sound like the rep was on their computer right then and there, even though it's of course just a dynamic variable. 2. The second line appears much more relevant and personal. You can either manually check this or set up some dynamic variable logic with scrapers to filter this. You basically just want to create a sense of FOMO in the prospect. 3. Extremely soft and valuable offer. Instead of asking for a call, we asked to make them sample creative. 4. Social proof as CTA. We conversationally mention a past result for a similar client. What I love most about this is it can easily be adapted to many service-based offerings.

I've spent well over 100hrs testing different AI models this year. For one purpose: Generating cold emails at scale that don’t all sound the same. The best AI model for cold outreach is all about who writes differently, not necessarily who writes the best. If every email sounds identical, deliverability suffers. To counter this, we force models to generate high-variance copy using this line in our prompt: “Maximise email variability by changing greetings, transitions, and phrasing. Ensure each generation is linguistically unique while preserving meaning, tone, offer, and call-to-action.” The problem: GPT & Claude try, but the outputs still converge. Gemini 2.5 Pro is the only model that truly understands and executes tthe variability requirement. If you’re using AI for outbound and your emails still have that “same template, new day” energy - it’s probably not your prompt. It’s your model.

Smartlead vs Instantly vs Lemlist vs Outreach vs Salesloft Which one actually gives you the most value in 2026? We broke down the top engagement platforms across: → Native features (warm-up, LinkedIn, lead DB) → Scalability → CRM sync depth → Pricing model → Value for money Here’s how it stacks up, depending on your team’s needs: → Smartlead: Ideal for lean, high-volume teams. Unlimited inboxes, native database, and scalable at a low cost. → Instantly.ai: Solid for email-only workflows that want volume without complexity. → Outreach & Salesloft: Built for enterprise, but pricey and bloated for smaller teams. → lemlist: Multi-channel, but limited on scalability and pricing flexibility. Choosing the right tool can save you thousands, and hours of frustration. Any platforms I missed? 👇