AI in Sales Transformation

The anatomy of a sales call has changed dramatically. Last week, I shadowed some of HubSpot’s top reps and what struck me was how differently the best sellers work today. They’re using AI at every stage: before, during, and after the call. And the results are real. The brain: before the call. AI does the heavy research — scanning 10Ks, news, emails, and past calls to surface the insights that matter most. Tools like Breeze Assistant can prep a full company overview in seconds. According to our State of Sales Report, 74% of sellers say buyers are showing up to calls more informed than ever before. Salespeople need to be just as ready. The heart: during the call. AI notetakers capture everything: next steps, budget mentions, open questions, so reps can focus on listening, not typing or scribbling notes on the side.  Also, AI assistants surface the right case study or testimonial in real time, making every answer sharper and every example more relevant. That means as a sales rep you are more engaged and relevant. The muscle: after the call. AI follows through fast. It drafts personalized follow-up emails in your own voice, outlines next steps, and flags what needs attention. More time with customers and less time writing emails. The result: sellers who prepare better, connect deeper, and close faster. The anatomy of a great sales call used to be manual effort and hustle. Now, it’s human connection powered by intelligence.

Last quarter, we spent $1,404,619 on AI tokens - an all-time high - and the ROI wasn’t what we expected… Most of the ROI didn’t come from “flashy AI”, it came from boring AI doing boring work at scale. Here’s where our spend went and what actually moved the needle: 1. Telling reps who to call today (and why) We’re using AI to sift through millions of signals and tell reps who to talk to today and why. The signals that we’ve found matter: Job changes (new decision makers = new opportunities), buying committee changes and intent signals (active web research and pricing page visits). The big ROI driver is helping our customers with daily prioritization so they don’t have to go fishing for actionable info. At ZoomInfo, We’ve seen a 25-33% increase in meeting quality and opp creation when AEs are sourcing using our AI tools. Win rates also jump from 16-20% to 30%. 2. Writing outreach that doesn’t sound automated We’re moving from “20 segments of 1,000” to 20,000 segments of 1. Not “VP IT at enterprise insurance” messaging… but John at State Farm, who we talked to last year, who competes with three of our customers, with context pulled in automatically. Customer ROI here ultimately comes from better response rates and higher close rates by being more relevant. Buyers care when you show you care. 3. Turning sales calls into usable data Every sales call (ours and customers) is recorded using @Chorus and becomes structured data: objection patterns, competitor mentions, deal risk, coaching moments. We’ve found the benefits of this are huge - 25-30% faster ramp time for new reps, and 10-15% larger deal sizes through better discovery and value articulation. The average rep sells more like the best rep. 4. Speeding up low-value engineering work Every engineer at Zoominfo has Intellij and VS Code w/ Cline. AI handles the unglamorous stuff: Boilerplate code, refactors, test coverage. We’ve seen ~25–30% faster execution on these routine tasks, which frees senior engineers to focus on system design and real product innovation. Our biggest lesson so far has been that if your data foundation is garbage, AI just helps you move faster in the wrong direction. You won’t get AI “working” until you have contextual customer/prospect data centralized, and you can actually build on top of it. We’re still early and we’re trying a lot of things but these have been the highest ROI drivers by a mile. If you’re testing AI in your GTM stack, drop a comment with what’s actually working for you - I’m all ears.

54% of sellers miss quota. Not because of pipeline, but because of: 𝑇ℎ𝑒 𝑤𝑟𝑜𝑛𝑔 𝑤𝑜𝑟𝑘. And the data finally reflects what sellers have felt for years: • Only 46% of reps hit quota • Growth slowed to 16% in 2025 • Opportunities per AE dropped across every deal size Sales used to be a game you could win just by outworking everyone. That world is gone. Here’s the uncomfortable truth leaders are denying: Teams aren’t losing from a lack of effort. They’re losing from 𝑜𝑣𝑒𝑟𝑤ℎ𝑒𝑙𝑚. Sellers are drowning in: • Tool sprawl • CRMs stuck in the past • Constant context switching • Admin disguised as important sales activity • Hours of research that’s outdated by tomorrow Meanwhile, buyers show up already knowing: Your pricing. Your leadership changes. Your competitor’s roadmap. And you’re still trying to remember their CFO’s name. The fear is that AI will replace sellers. The reality? AI exposes who’s been coasting on activity vs. who’s building strategic muscle. Consider what high performers are already proving: 📈 Sellers who frequently use AI generate 77% more revenue per rep. 📈 Teams using AI with true revenue expertise see: • 13% higher revenue growth • 85% higher commercial impact AI isn’t a shortcut. It’s the new productivity layer. But not all AI creates leverage. The winners aren’t using generic chatbots. They’re using intelligent revenue agents that remove low-value work and surface insights humans miss. I’ve been testing something that feels like the future of selling. It’s called Rox, and it’s not another AI add-on. Rox delivers Revenue Agents built for the enterprise and Global 2000 accounts: ✔ Get meeting briefs that update themselves as conditions change ✔ Surface signals buried across Slack, email, product usage, & public data ✔ Centralize everything in one workflow (your calendar, not another dashboard) ✔ Walk into every call knowing the latest board deck, leadership moves, & competitive shifts (without opening 15 tabs) As someone who built a 7-figure-earning career through strategic focus, not more volume, this is the leverage I wish I had years ago. This isn’t for average reps. It’s for the ones committed to compounding their craft. The next era belongs to sellers who use intelligent systems—not more effort—to create asymmetric outcomes. If you’re a high performer trapped in low-leverage work, Rox creates the breathing room for strategy: https://bit.ly/4oHeB1R 🐝 P.S. I collaborated with Rox on this post. I only partner with platforms I would’ve personally used when I was closing $50M+ in enterprise deals.

𝗗𝗥𝗘𝗔𝗠𝗙𝗢𝗥𝗖𝗘 𝟮𝟬𝟮𝟱 - 𝗜‘𝗺 𝗶𝗻 𝗦𝗮𝗻 𝗙𝗿𝗮𝗻𝗰𝗶𝘀𝗰𝗼 𝘄𝗶𝘁𝗵 𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲!! ☁️💙 [𝗔𝗱/𝗔𝗻𝘇𝗲𝗶𝗴𝗲] Many companies like to talk about AI painting big pictures of what might come next. Salesforce takes a different approach: they build! For more than a year now, Salesforce has been rolling out AI agents that are already running inside companies around the world. In yesterday’s keynote, 𝗠𝗮𝗿𝗰 𝗕𝗲𝗻𝗶𝗼𝗳𝗳, 𝗖𝗘𝗢 𝗼𝗳 𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲, shared new use cases that made it easier than ever to understand how these agents really work in daily operations. 𝗕𝘂𝘁 𝗼𝗻𝗰𝗲 𝗮𝗴𝗮𝗶𝗻: 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁? Imagine opening your laptop and finding a small team of digital helpers already inside. Each one is an expert… one knows your customers very well, one your workflows, one is your data expert. They don’t just answer your questions or react to commands but fix things before you see them and make work feel more fluent, faster, personal & fun. Marc Benioff described this evolution clearly: “𝘈 𝘺𝘦𝘢𝘳 𝘢𝘨𝘰, 𝘈𝘨𝘦𝘯𝘵𝘧𝘰𝘳𝘤𝘦 𝘸𝘢𝘴 𝘢 𝘱𝘳𝘰𝘥𝘶𝘤𝘵. 𝘛𝘰𝘥𝘢𝘺, 𝘪𝘵’𝘴 𝘵𝘩𝘦 𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮 𝘣𝘦𝘩𝘪𝘯𝘥 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 𝘸𝘦 𝘥𝘰.” 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀: –> up to 30 % faster service resolution and 40 % lower response time in customer operations –> productivity gains between 20–35 % across early adopters –> now used by 12,000+ companies from retailers to logistics firms –> interoperability with AWS, Microsoft & OpenAI, so it fits into existing tech stacks –> built-in governance and transparency layers, critical for regulated industries 𝗟𝗲𝘁’𝘀 𝘁𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗺𝗲 𝗰𝗼𝗻𝗰𝗿𝗲𝘁𝗲 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀: 𝗪𝗶𝗹𝗹𝗶𝗮𝗺𝘀 𝗦𝗼𝗻𝗼𝗺𝗮 – An AI “shopping chef” that knows your taste. It connects recipes, products, and past purchases turning every visit into a personalized experience that feels more like a conversation than a normal shopping experience. 𝗙𝗲𝗱𝗘𝘅 – AI agents read and route thousands of logistics documents in seconds catching exceptions, rerouting shipments, and reducing manual work across global operations. 𝗣𝗮𝗻𝗱𝗼𝗿𝗮 – An AI assistant follows you from online to in-store. What you like in chat appears ready in the boutique creating one seamless, personalized customer journey. 𝗗𝗲𝗹𝗹 – AI agents automate supplier onboarding verifying documents, sending approvals, and cutting setup time from 60 days to under 20. Faster partnerships, faster production. Each example shows how AI can move beyond experimentation, into real outcomes & that‘s what we need more now: REAL IMPLEMENTATION! Tomorrow continues with more 𝗧𝗲𝗰𝗵 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗮 𝗚𝗟𝗢𝗕𝗔𝗟 𝗦𝗨𝗣𝗘𝗥𝗦𝗧𝗔𝗥 I was able to meet and we all probably know more for his music than for his tech… STAY TUNED!! 💙🦾 Do you already use AI Agents in YOUR business? –> If yes, what for? –> If not, which tasks would you 𝘭𝘰𝘷𝘦 to hand over to an agent friend?

Two forces are colliding in B2B go-to-market: the decline of the traditional playbook and the meteoric rise of AI. In 2006, we founded Marketo and I helped create that traditional playbook — the one built on MQLs, marketing automation, and tracking every click. For years, it worked brilliantly… until it didn't. Now, the “gum ball machine” approach to marketing (“budget in, MQLs out”) has become unsustainable. Buyers are burned out by relentless outreach, and trust is at an all-time low. It’s time to reframe marketing’s role in revenue and lean into brand-building as a long-term differentiator. At the same time, AI agents are reshaping how we work and buy. They’re handling repetitive tasks like qualifying leads and building campaigns, and helping us make purchases by filtering and summarizing information. In this world, experiences that can't be filtered or summarized will become marketing's new currency. These two trends are driving the most profound transformation in B2B marketing since the advent of marketing automation. And they work together. As AI finally delivers on the promise of “automation” in marketing automation, it will free us to focus on the strategic, creative work that truly moves the needle. Put another way, if AI can handle the "-ing" in marketing, then we can focus on the "market": understanding our buyers, crafting compelling narratives, and building memorable experiences. This shift is at the heart of my 11 predictions for how B2B will evolve in 2025 and beyond. Here's a sneak peek: 1. Companies will slowly break from their "gumball machine" MQL addiction 🍬 2. CMOs will work to reframe marketing's role in revenue 📈 3. Marketers will rebalance budgets toward brand 🌟 4. AI agents will gain early real traction in the enterprise 🤖 5. MOps teams will use AI to trade tactical tasks for strategic impact 👩💻 6. AI will start to replace junior sales roles but augment strategic sellers 🤝 7. Companies will adopt AI SDR agents — but automated cold prospecting will fall flat ❄️ 8. Seat-based pricing will give way to value-based models 💺 9. Agents will begin to transform how we buy — and how we go-to-market 🛍️ 10. Experiences, relationships, and original content will stand out as AI filters out traditional marketing 🎉 11. Marketing automation will be reimagined for the AI era 🚀 The full definitive article, shared in comments, dives into each prediction and what it means for you. Found this valuable? Please leave a comment or repost to let me know what you think and help drive visibility for these ideas! Do you agree or disagree? What are you seeing in your own business? 🙏 #B2BMarketing #MarketingAI #MarTech #CMO #MOps #SalesAI #MarketingAutomation #Predictions #Marketing2025

I've watched organizations rush to implement AI tools across their revenue functions, often with mixed results. Today, I'm sharing a crucial insight: the companies seeing transformative results are not those with the most advanced tech stacks. Instead, they deploy AI with surgical precision at the intersection of efficiency and trust. In my latest piece, I break down specific AI tools reshaping revenue operations and offer strategic guidance on implementing them without eroding the customer trust that underpins sustainable growth. Key takeaways: 🎯 Conversation Intelligence Platforms (Gong, Chorus): Not just for call analysis, but for scaling successful behaviors while maintaining authentic customer interactions 🎯 Predictive Lead Scoring (MadKudu, 6sense): Allowing targeted deployment of human capital against high-probability opportunities (with critical guardrails) 🎯 Personalization Engines (Mutiny, Optimizely): Creating tailored experiences without increasing operational complexity or crossing the "creepy line" 🎯 Content Generation (Jasper.AI, Copy.ai, Claude.ai): Achieving velocity without sacrificing quality (but still requires human oversight to be more, well, human). 🎯 Customer Journey Orchestration (Drift, a Salesloft company, Qualified): Creating guided buying experiences that feel personalized while operating at scale 🎯 AI Assistants (Grok, ChatGPT): Rapid iteration and testing of multiple approaches before committing resources The most successful revenue organizations aren't those using the most AI but those using AI most strategically. There is a competitive advantage in knowing where NOT to automate - in preserving human connection where it creates differentiating value. What AI tools are you implementing in your revenue operations? And more importantly, how are you measuring their impact beyond efficiency metrics? Read more here: https://lnkd.in/e4Ang6Nj __________ For more on growth and building trust, check out my previous posts. Join me on my journey, and let's build a more trustworthy world together. Christine Alemany #Strategy #Trust #Growth

If you’re learning AI automation without a roadmap, you’re guaranteed to get overwhelmed. People usually “learn AI automation” by jumping straight into tools… and then wonder why nothing works consistently. Real automation requires structure - thinking, logic, testing, and a gradual build-up of skills. This 18-day roadmap breaks down the exact sequence to go from zero → confidently building automations with AI, APIs, tools, and no-code platforms. Here’s the full breakdown, day by day: Day 1 - AI Automation Fundamentals Learn what automation really means, how it differs from AI and agents, and see real examples. Day 2 - Automation Thinking Break work into steps, triggers, and outcomes - the mindset behind every good automation. Day 3 - APIs & Webhooks Basics Understand how apps communicate and how events trigger workflows. Day 4 - No-Code Automation Platforms Explore Zapier, Make, n8n - and how no-code tools actually run workflows. Day 5 - Build Your First Automation Create a simple trigger-action workflow and connect two apps. Day 6 - Data Handling Pass data between steps, map fields, and work with text, numbers, and dates. Day 7 - Logic & Error Handling Add filters, conditional logic, retries, and fallbacks to keep automations reliable. Day 8 - AI Model Basics Learn prompts vs system instructions, tokens, limits, and LLM behavior. Day 9 - Using AI Inside Automations Insert AI steps into workflows and parse structured AI outputs. Day 10 - Prompt Design for Automation Write consistent prompts and reduce hallucinations with JSON outputs. Day 11 - Text-Based Task Automation Automate email replies, summaries, CRM updates, and document tasks. Day 12 - Knowledge Automation (RAG Basics) Connect AI to internal documents and fetch accurate answers from real data. Day 13 - AI Agents Basics Understand agent planning, tools, and identify use cases for agents. Day 14 - Business Use Case Automation Automate lead qualification, ticket routing, and internal processes. Day 15 - Sales & Marketing Automation Personalize outreach, repurpose content, and automate follow-ups. Day 16 - Operations Automation Manage approvals, notifications, and repetitive operational tasks. Day 17 - Monitoring & Optimization Track workflow success, cut costs, and improve performance. Day 18 - Build & Ship Your System Design, test, document, and finalize a complete end-to-end automation. You don’t master AI automation by learning tools, you master it by learning systems thinking, data flow, and structured execution. Follow this roadmap, and you’ll build automations that are reliable, scalable, and business-ready.

Most people think lead gen = buy a list. However the winners are the ones who can manufacture live intelligence on demand. That’s what this video is about. Not “how to use Clay” or “a neat Sales Navigator trick.” It’s about building a system where: Hiring signals become triggers. Company data gets de-duped and structured. Titles and org charts are mapped in minutes. Contacts are pulled and enriched across tools like Clay, Sales Nav, PhantomBuster, and LeadMagic. What used to take an agency days now takes 15 minutes if you think like an operator. That’s the bigger point here. Outbound isn’t about tools. It’s about stitching together SIGNALS to build LISTS of CONTACTS that you have RELEVANT MESSAGING for.... into a continuous cycle of intelligent outreach. Most teams are still playing checkers here. I've seen it. They’re buying static lists, maybe 1 data provider and clicking filters, maybe some burning of credits if they're lucky, and then blasting noise. Meanwhile, operators who work like this are running laps around them. Watch the walkthrough. More importantly, steal the philosophy. The future of outbound won’t be won by the flashiest tech stack. It’ll be won by whoever can use it to turn signals into conversations the fastest.

Today’s revenue teams will look nothing like the best-run revenue teams of the next decade. The CRO role is being redesigned. For decades, revenue leadership meant managing pipelines, arguing over forecast math, judgment calls, and carrying a number into a board meeting. The CRO was the overall quota owner and enforcer. AI agents change that entirely. When agents absorb the invisible work of selling, the Orchestrator role emerges: designing an intelligent revenue system where humans and machines co-own outcomes. In the agentic era, the CRO becomes the orchestrator of the revenue system and owns these 4 roles:  1. Chief Growth Systems Designer 2. Chief Forecast Intelligence Officer 3. Chief Agent Governor 4. Chief Revenue Connector I sat down with Abhijit Mitra, CEO of Outreach, to dig into where AI transformation is heading for CROs and sales teams. His framing was direct: the best-orchestrated revenue system wins. With agents, the shift moves sales from activity-heavy execution to decision-driven selling. The real shift is from point AI solutions to end-to-end revenue orchestration, where AI coordinates inbound, outbound, and deal execution as a unified system. AI restructures today’s B2B sales work around strategy, orchestration, and trust. The meta-pattern: AI handles sense-making and analysis. New roles are emerging: - Sales AI Operator / Sales Ops AI Lead  - Buyer Signal Analyst  - Deal Strategy Orchestrator  - Trust & Compliance Sales Specialist The shift from traditional SaaS sales software to intelligent revenue systems is a big company-building opportunity. Here is the advice I am sharing with founders building in this space: 1. Build for decisions, not activity 2. Design for systems, not features 3. Build for the Revenue Orchestrator and the organization around them. 4. Price to outcomes. 5. Design trust from day one. The winners are not the companies adding AI features to existing workflows. They are the ones reimagining SaaS in the AI era and building an intelligent revenue system that compounds. This is part 2 of my series on the Future of CXOs. Watch the highlights from my conversation with Abhijit Mitra on the future of sales, and read my newsletter on The Future CRO: The Orchestrator. 

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