How AI Transforms Outbound Marketing Strategies

Just closed a major enterprise deal against a competitor who pitched at 1/3 of our price. Here's the inside scoop on how we transformed what started as a "chatbot search" into a complete GTM automation win: Here's what most vendors miss: Chatbots alone are just the tip of the iceberg. The real magic happens when you connect visitor intelligence to autonomous GTM actions. Reframing the Conversation : The prospect (a well-funded services company with 400+ employees) initially came to us looking for an AI chatbot and had done their homework. During our first demo, we showed them something dramatically different: Their current flow: Visitor chats with bot Lead gets logged Sales team manually follows up (maybe) Data sits in silos What we demonstrated live: AI chatbot engages visitor Platform instantly identifies the company AI agents then automatically: Create enriched company profiles Launch tailored outbound sequences Book meetings via voice/email Update their CRM in real-time Alert relevant teams in Slack/Teams The game changer? When we were able to demonstrate to their team the ways our AI agents were already acting autonomously based on chatbot interactions from other companies in their industry - booking meetings while competitors are still manually working over chat leads. Inbound Intelligence: Knows which companies are engaging (or not engaging) with the chatbot Analyze conversation patterns for intent signals Triggers targeted workflows by interaction type Routes high-value prospects to live sales teams Outbound Automation: AI agents autonomously prospect similar companies Creates targeted account lists by patterns of engagement with the chatbot Launches multichannel outreach (voice, email, LinkedIn) Syncs all activity back to their CRM The "Aha" Moment Instead of configuring a chatbot for them, we walked them through building a complete workflow in the demo: Key Takeaway: When you can show how a "simple chatbot" can become an autonomous revenue engine--price becomes irrelevant. The discussion shifts from "Do we really need another chat tool?" to "How soon can we put this complete GTM automation out there?"

The Future of GTM is AI + Human Hybrid Outbound isn't becoming less effective. It's becoming more demanding and we need to adapt. *GTM Engineer enters the chat* 💡 What is a GTM Engineer? A hybrid operator who blends data, automation, and AI-driven execution with strategic go-to-market expertise. ❌ Not an Ops role. (Ops lacks the industry expertise.) ❌ Not a Sales role. (Sales lacks technical expertise.) ❌ Not a traditional Marketing role. A GTM Engineer builds, automates, and scales revenue workflows. 💥 Why Now? The GTM Tech Stack is evolving. FAST. AI agents and automated data workflows have changed the game. Key Trends For Successful GTM Workflows ✅ Data Enrichment → Finding and enriching data has never been easier. AI-driven tools now provide real-time, high-quality insights with minimal manual effort. 🛠 Clearbit, Apollo.io, ZoomInfo, Clay ✅ AI-Driven Personalization → Messaging at scale, making every touchpoint feel human. 🛠 Twain.ai, Lavender 💜🔮 www.ora.im, Anthropic ✅ Sales Automation → SDRs should focus on closing, not admin work. AI automates repetitive tasks, freeing up teams for high-impact conversations. 🛠 Attention.com, Attio, Dock.us, RevReply, Octave ✅ Advanced Data Analytics → Turning intent signals into revenue. Website visits, social engagement, help data-driven GTM decisions. 🛠 RB2B, Trigify.io, Teamfluence™ (use all x3) ✅ Generative AI → Automating hyper-personalized outbound at scale, from cold emails to social selling sequences. 🛠 AI (OpenAI, Claude, Llama) + n8n/Make, Claygent The New GTM Playbook 🏆 Forget high-volume, low-personalization outbound. The best GTM teams are focusing on: ✅ Real-time data workflows ✅ Multi-channel & multi-touch engagement ✅ Hyper-personalization at scale ✅ Automated micro-campaigns that drive demo calls This is just the beginning. My bet? In a year, we’ll see GTM Engineers in every B2B SaaS org. DM me if you're looking to build your GTM motion 🔥 I've got some cool tips for you #gtm #salesengineering #growth #salesautomation #marketing #sales #revenuegrowth #b2b #startups #gtmstrategy #ai #automation

Outbound Sales is Broken. Here’s How to Fix It With AI Most outbound strategies today look the same: - Mass emails with no personalization - Cold outreach to the wrong people - Ignoring high-intent prospects already visiting your site The result? Low response rates, wasted effort, and lost deals. A smarter approach uses AI to make outbound more effective: - Engage warm leads first by identifying real buyers, not just company names. Tools like RB2B and Maximise.ai provide person-level data on website visitors, including names, titles, and emails, enabling precise engagement. - Automate research to understand your prospect’s pain points before reaching out - Hyper-personalize at scale with messaging that gets real responses - Track and optimize outreach to double down on what works AI will not replace great salespeople, but it will amplify them. As Adam Robinson often highlights, AI-powered outbound is not about sending more—it’s about sending the right messages to the right people at the right time. AI won’t replace great salespeople—it will amplify them. The future of outbound is about precision, timing, and relevance. Are you adapting, or still hoping outdated playbooks will work? #Sales #AI #Outbound #B2BMarketing

After my last post, someone asked, “So what use cases are you actually seeing value from AI?” So here’s a peek into how we’ve been using AI across our Marketing team. Below is our actual team/org chart with AI Agents as part of the team 𝗢𝗻 𝘁𝗵𝗲 𝗢𝘂𝘁𝗯𝗼𝘂𝗻𝗱 𝘀𝗶𝗱𝗲 – 𝗔𝗴𝗲𝗻𝘁𝘀: 𝗚𝗲𝗺-𝗘𝘀 We’ve been fortunate to have Gem-E, our AI outbound agent, early on so this side of the team is more built out & measured Impact: Outbound capacity increased by 74% after 9 months Jobs to be done: - Research & Capture All Buying Signals & CRM Data - Account & Contact Scoring & Prioritization - Building Lists of Contacts - Writing Outbound Emails (& sometimes Sending on Autopilot) We apply Gem-E to specific Campaigns like: Past Champions, ABM Prioritization & Outreach, Revive Closed Lost, Event Outreach That makes it super easy to measure the pipeline impact. 𝗢𝗻 𝘁𝗵𝗲 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝘀𝗶𝗱𝗲 – 𝗔𝗴𝗲𝗻𝘁𝘀: 𝗚𝗮𝗶𝗮, 𝗚𝘂𝘀, 𝗚𝗶𝗻𝗻𝘆, 𝗚𝗶𝗻𝗮, … We’ve started creating internal custom GPTs for specific “jobs to be done.” Impact: The business value here is harder to measure besides time saved (I’m still working on quantifying this). Personally, these Agents help with easier context switching, faster output, and fewer repetitive tasks. A new competitive comparison used to take 45-60 minutes, now takes 15. Jobs to be done: - Market research - Video-to-Playbook - Battle Card - Paid Ads Performance Reporting - Email Nurture 𝗪𝗵𝘆 𝗜'𝗺 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗢𝗿𝗴 𝗰𝗵𝗮𝗿𝘁 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀? I noticed that all our team members already use AI to do their work — but their GPTs and training are "siloed" in their personal instances. The rest of the team cannot access it to get the output, like "a battle card for X" 👉 So we got everyone on the Corporate Team Plan (maybe Enterprise soon tbh) Many AI experiments tend to fizzle out because they don't align with clear business initiatives or tangible impacts. Combining with the silo-nature above, they tend not to last. 👉 So we have each team lead treat their Agents as direct reports — the lead is responsible for creating (~hiring) & improving (~managing & coaching), and let the company work with the Agents (~ visibility) My hypothesis is that if there are clear owners & visibility, the Managers will make sure the Agents add value to the team & the company. That’s the shift I think & hope will matter most long-term: Not just using AI, but operationalizing it. Treating Agents not as novelty tools, but as teammates that need direction, coaching, and integration into the way we work. Curious how others are doing this. How are you making AI a durable part of your workflows & teams, and not just an experiment that fizzles out?

There is a lot of chatter today about what isn’t working anymore in outbound sales. But what about what *is*? What are the channels and strategies that are bearing fruit that could be executed even better using AI? I’ll start with dialing. HubSpot’s recent Sales Trends Report shows phone calls to be the most effective channel for cold outreach. Yet, connect rates are below where they had been historically. It’s likely because buyers' BS filters are at all-time highs, while reps struggle with the disjointed calling workflow. So how do we take a strategy that is working, but improve the approach so it performs even better for our teams? Here are three core areas where the dialing experience and workflow can be improved thanks to genAI and AI voice: 1- Reaching the right prospects: connect with more phone-ready leads by having AI sort through intent and engagement signals to ensure outreach happens at the right time, to the right person. 2- Leveraging the stacking effect: use AI Agents to warm up lead lists through pre-call email and social engagement, so that way your dials are more likely to land with an audience who already has some familiarity with you and your brand. 3- Maintaining relevance at scale: use generative AI to create custom call scripts using unique data on each of your leads, while leveraging AI voice to make tailored voicemail drops in the voice of your reps. Both establish relevancy without sucking up bandwidth. What other channels and strategies are you seeing improved by introducing AI?

A lot of #AI talk in marketing today focuses on efficiency — helping marketers write copy, generate images, and automate reporting. That’s great, but it’s just the start. The real transformation comes with AI that orchestrates outcomes. Imagine an AI system that… …not only suggests audiences, but it also builds and refines them dynamically. ...does more than optimize an ad; it runs experiments across channels, learns what works, and scales success. …predicts churn and automates the right retention playbook in real time. This is what’s coming. AI agents, embedded in your #DataCloud, automating tasks AND results. Instead of a pile of disconnected martech tools, #marketing will run on composable, AI-powered workflows built directly on the data that already fuels your business. Are you building toward this future?

Last week, I heard from a super impressive customer who has cracked the code on how to give salespeople something they’ve always wanted: more selling time. Here’s how he transformed their process. This customer runs the full B2B sales motion at an awesome printing business based in the U.S. For years, his team divided their time across six key areas: 1. Task prioritization 2. Meeting prep 3. Customer responses 4. Prospecting 5. Closing deals 6. Sales strategy Like every sales leader I know, he wants his team to spend most of their time on #5 and #6 — closing deals and sales strategy. But together, those only made up about 30% of their week. (Hearing this gave me flashbacks to my time in sales…and all that admin tasks 😱) Now, his team uses AI across the sales process to compress the amount of time spent on #1-4: 1. Task prioritization → AI scores leads and organizes daily tasks 2. Meeting prep → AI surfaces insights from calls and contact records before meetings 3. Customer responses → Breeze Customer Agent instantly answers customer questions 4. Prospecting → Breeze Prospecting Agent automatically researches accounts and books meetings The result? Higher quantity of AI-powered work: More prospecting. More pipeline.  Higher quality of human-led work: More thoughtful conversations. Sharper strategy. This COO's story made my week. It's a reminder of just how big a shift we're going through – and why it’s such an exciting time to be in go-to-market right now.

Testing and piloting AI for sales and marketing can be frustrating. That’s why Jomar Ebalida and I came up with the practical AI roadmap for marketing and GTM ops pros. This roadmap helps you figure out where to start, what to focus on, and how to scale AI initiatives in a way that’s grounded in operational reality. It’s structured in 3 phases: PREP: Evaluate your organization’s current state across data, tools, team skills, and funnel performance. PILOT: Select and test AI use cases based on your actual readiness data. (Diagram shows samples) Avoid guessing by letting the assessment drive decisions. ACTIVATE: Scale the pilots that show promise and embed them into core processes. Here are select projects worth walking through: 🔹 AI Readiness Assessment This project includes evaluating data quality, the state of your CRM, the maturity of your tech stack, and your team’s readiness to work with AI tools. It also includes a bowtie funnel analysis to help identify where your customer journey is breaking down. The outcome is a clear picture of which AI use cases are both valuable and feasible for your team to pursue. 🔹 AI SDR Agent: Outreach and Prospecting This agent is designed to support outbound sales by identifying high-potential accounts, generating personalized outreach messages, and helping SDRs scale without sacrificing relevance. It can help teams boost pipeline without overloading headcount. 🔹 AI QA and Compliance: Brand, Legal, Regulatory This workstream ensures that every piece of AI-generated content or decision logic meets the necessary internal standards. It supports brand consistency, regulatory requirements, and risk mitigation. This process should run in parallel with pilots and activations to ensure safe implementation. 🔹 AI Agents for Ops: QA Checks, Routing, and Campaign Setup This includes AI agents built to handle operational tasks such as verifying UTM links, auto-routing requests, or creating campaign templates. These agents are ideal for improving workflow speed while reducing manual errors and team bottlenecks. At the foundation of all of this is change management. Each phase of the roadmap includes a focus on enablement, training, adoption, metrics, and governance. Tools don’t generate value unless people are set up to use them properly. Which parts resonate with you? What would you change or add? PS: To learn more & access templates, subscribe for free to The Marketing Operations Leader Newsletter on Substack https://lnkd.in/g_3YC7BZ and to Jomar's newsletter at bowtiefunnel(dot)com. #marketing #martech #marketingoperations #ai #gtm

Spray and pray REALLY is dead. But judging by my inbox… someone forgot to tell the sellers. Cold outreach isn’t broken. It’s just gotten lazy. We’ve got AI. We’ve got Clay, Apollo, Ocean, Lavender, all the sauce. But that toolset is just blasting MORE people who aren't EXACTLY right. If you want your outbound to actually work in 2025, here’s the dealio: Stop blasting. Start listening. Listening to signals is how you build a GTM system that feels like it was handcrafted without spending 30 minutes doing research for every message. Signals give you a sneak peak at "in-market" likelihood. Bad personalization makes you sound like AI or worse, someone who doesn't know WTF they are doing. Instead of AI inspired “Hey, love what you are doing at X...,” try: “Saw your team just rolled out X. That usually signals [insert problem]. Sound about right?” That’s not faux personalization. That’s real perspective & insight. And maybe most importantly, RELEVANCE. The good news? Signals aren’t hard to find. Most of your future clients are already raising their hand. You’re just not listening closely enough. The future of agency growth isn’t more volume. It’s laser-focused relevance - at targeted scale. That’s the whole game. #GTMOS #agencygrowth #outbound #leadgen #sales

Most AI sales emails suck. "Dear Sir or Madam" 🤖 (If you just cringed, I feel you) Why? Because most AI sales companies are 95% marketing. They're just GPT wrappers in disguise. They use basic IF > THEN statements. But this leads to a challenge... Lots of sales leaders are skeptical about AI. Rightfully so because there's a lot of 💩 on market. 📌 THE SOLUTION You need "Decision Nodes" Think of them like intelligent building blocks (That you can customize 1:1 to your sales workflow) - One node finds your ideal prospects - Another qualifies them deeply - Another crafts personalized messages - Another validates everything is accurate Each node is a hardened module that does ONE thing perfectly. We've built 50+ of these specialized nodes at Redcar. And chain them together into intelligent workflows. That's how we designed Redcar.io AI Sales Agents You get quota lift of up to 34% per rep 📈 When you bring them together. 🧵 EXAMPLE FOR YOU When you need to research a prospect? Multiple nodes spring into action: - First node crawls their website - If that fails, it tries different sources - Each step is validated for accuracy Your outreach feels natural because it IS natural. Based on 1. Real research on every prospect 2. Real qualification of their business 3. Real personalization in human language ❌ Not just "Hey (Name) at (Company)!" ✅ Real examples our AI wrote: "Saw the news break about Expedia Group's new data-driven marketing initiative launching this summer. Are you dealing with pressure to prove ROI on your marketing spend? We just solved this for (X company), worth a call?" You need REAL research to inform your outbound today. That's why you need precise research. Research you can count on with AI. (That won't be hallucinated). Standard LLMs: 58% overall accuracy on research tasks VS Redcar LLM Modules: 94% overall accuracy That's a 71.98% error rate reduction versus GPT4o. AKA... Your emails actually sound human. Because they're built on legit research. That's how you write great emails with AI. Customizable decision nodes. That's Redcar. -- 👋 P.S. Want to try Redcar for your sales team? DM me to get your free trial.