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.

The more I spend time building HUMAIN, the more convinced I become that the age of traditional enterprise sales is coming to an end. Relationship selling alone is no longer enough. In the AI era, value realization and solution selling matter far more than simply pushing products, licenses, or features. Most enterprises today are struggling with one fundamental challenge: they know AI is important, but they do not know how to operationalize it or realize measurable business value from it. Many organizations are still trying to apply AI on top of broken workflows, fragmented data, outdated operating models, and heavy bureaucracy. The future sales organization must look very different. The next generation of enterprise sellers must become: - deeply technical, - operationally aware, - capable of workflow redesign, - capable of discovering hidden inefficiencies, - and able to connect AI to real business outcomes. The conversation can no longer start with technology. It must start with: - What business problem are we solving? - What operational friction exists? - What workflow should disappear? - What can become autonomous? - How do we redesign the enterprise around intelligence and AI agents? In many cases, customers themselves may not even fully understand the root cause of their inefficiencies. This is why the future seller is evolving into something entirely different: part technologist, part operator, part strategist, part transformation architect. At HUMAIN, this transformation has honestly been one of the hardest challenges for me personally. Building AI products is difficult. Building AI infrastructure is difficult. But transforming the mindset of enterprise go-to-market teams may be even harder. I spend a surprising amount of time reading messages that come to me on LinkedIn because I am constantly searching for people who think differently: builders, systems thinkers, operators, problem discoverers, AI-native minds, people obsessed with solving hard problems rather than simply closing deals. The future AI field organization will not look like the traditional sales teams of the past. And I believe the companies that figure this out first will define the next decade of enterprise AI.

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.

Hey Salespeople: Here is a collection of current use cases for AI in sales & CS: ** GenAI in Sales ** --> Draft messaging for personalized email outreach --> Generate post-call summaries with action items; draft call follow ups --> Provide real-time, in-call guidance (case studies; objection handling; technical answers; competitive response) --> Auto-populate and clean up CRM --> Generate & update competitive battlecards --> Draft RFP responses --> Draft proposals & contracts --> Accelerate legal review & red-lining (incl. risk identification) --> Research accounts --> Research market trends --> Generate engagement triggers (press releases; job postings; industry news; social listening; etc.) --> Conduct role-play --> Enable continuous, customized learning --> Generate customized sales collateral --> Conduct win-loss analysis --> Automate outbound prospecting -->Automate inbound response --> Run product demos --> Coordinate & schedule meetings --> Handle initial customer inquiries (chatbot; voice-bot / avatar) --> Generate questions for deal reviews --> Draft account plans ** Predictive AI in Sales ** --> Score leads & contacts --> Score /segment accounts (new logo) --> Automate cross-sell & upsell recommendations --> Optimize pricing & discounting --> Surface deal gaps / identify at-risk prospects --> Optimize sales engagement cadences (touch type; frequency) --> Optimize territory building (account assignment) --> Streamline forecasting (incl. opportunity probabilities; stage; close date) --> Analyze AE performance --> Optimize sales process --> Optimize resource allocation (incl. capacity planning) --> Automate lead assignment --> A/B test sales messaging --> Priortize sales activities ** GenAI in CS ** --> Analyze customer sentiment --> Provide customer support (chatbot; voice-bot / avatar; email-bot) --> Draft proactive success messaging --> Update & expand knowledge base (incl. tutorials, guides, FAQs, etc.) --> Provide multilingual support --> Analyze customer feedback to inform product development, support, and success strategies --> Summarize customer meetings; draft follow-ups --> Develop customer training content and orchestrate customized training --> Provide real-time, in-call guidance to CSMs and support agents --> Create, distribute, and analyze customer surveys --> Update CRM with customer insights --> Generate personalized onboarding --> Automate customer success touch-points --> Generate customer QBR presentations --> Summarize lengthy or complex support tickets --> Create customer success plans --> Generate interactive troubleshooting guides --> Automate renewal reminders --> Analyze and action CSAT & NPS ** Predictive AI in CS ** --> Predict churn; score customer health; detect usage anomalies, decision maker turnover, etc. --> Analyze CSM and support agent performance --> Optimize CS and support resource allocation --> Prioritize support tickets --> Automate & optimize support ticket routing --> Monitor SLA compliance

In customer experience (CX), the closed-loop feedback (CLF) model has been a cornerstone for over two decades, originally designed to ensure responsiveness and adaptation. It's time for a change. With the advent of artificial intelligence, it's clear that merely adapting this model isn't enough. It's old tapes. It needs to evolve. Here's what's next: Real-time Interaction Management: Traditional CLF reacts to feedback after the fact. And, traditionally, closing the "inner loop" requires a human to follow up. AI turns this on its head. Imagine a system that adjusts the customer journey in real-time based on predictive analytics, reducing friction points before they affect the customer experience. Large Action Models: We all know that AI can dive deep into data lakes to instantly identify patterns and root causes of customer dissatisfaction. This rapid analysis allows companies to not only close the feedback loop faster, but also implement more effective solutions. This will come in the evolution of Large Language Models, or LLMs, to LAMs, or Large Action Models. Continuous Learning Systems: AI transforms CLF from a loop that ends into continuous cycle of improvement. These systems learn from each interaction, constantly updating and refining strategies to enhance the customer experience. This means that the feedback loop is ever-evolving, driven by AI's ability to adapt to new information and complex variables, seamlessly. CX leaders have to embrace AI's potential to redefine our foundational practices. It's time to innovate beyond the traditional CLF and leverage AI to deliver personalized experiences, and at scale. How are you thinking about adaptive, predictive, and personalized CX strategies? Your answer can't be to hire more people to close more loops. #customerexperience #ai #journeymanagement #survey #CLF

𝗦𝗮𝗹𝗲𝘀 𝗰𝘆𝗰𝗹𝗲𝘀 𝗮𝗿𝗲𝗻’𝘁 𝗹𝗼𝗻𝗴. 𝗧𝗵𝗲𝘆’𝗿𝗲 𝗷𝘂𝘀𝘁 𝗹𝗼𝘀𝘁 𝗶𝗻 𝗻𝗼𝗶𝘀𝗲. Many sales leaders believe they can improve performance by adding more training, tools, or dashboards. But the reality doesn’t change. Deals continue to stall. Forecasts remain inaccurate. The problem isn’t a lack of effort or even talent. It’s the absence of a real-time execution layer that turns data into action. 𝟭. 𝗡𝗲𝘅𝘁-𝗦𝘁𝗲𝗽 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Reps no longer guess their next move. AI reads deal patterns and gives them precise, timely actions that move the pipeline forward. 𝟮. 𝗗𝗲𝗮𝗹 𝗥𝗶𝘀𝗸 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗥𝗲𝗰𝗼𝘃𝗲𝗿𝘆 Most lost deals show early warning signs. AI detects when momentum drops and triggers recovery actions before the deal slips away. 𝟯. 𝗦𝗮𝗹𝗲𝘀 𝗖𝗮𝗹𝗹 𝗣𝗿𝗲𝗽 𝘄𝗶𝘁𝗵 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 Reps walk into calls fully prepared. AI surfaces the key insights, talking points, and questions tailored to each persona and stage. 𝟰. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Not every deal deserves equal attention. AI helps reps focus on the right opportunities at the right time. 𝟱. 𝗜𝗻𝘀𝘁𝗮𝗻𝘁 𝗥𝗲𝗽 𝗖𝗼𝗮𝗰𝗵𝗶𝗻𝗴 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 Coaching doesn’t have to wait for a review. AI analyzes performance patterns and provides real-time guidance for improvement. 𝟲. 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸𝘀 𝗳𝗿𝗼𝗺 𝗥𝗲𝗮𝗹 𝗪𝗶𝗻𝘀 Winning deals leave a trail of patterns. AI turns those into living playbooks that adapt across industries and personas. 𝟳. 𝗪𝗼𝗿𝗸𝘀 𝗜𝗻𝘀𝗶𝗱𝗲 𝗧𝗼𝗼𝗹𝘀 𝗬𝗼𝘂 𝗔𝗹𝗿𝗲𝗮𝗱𝘆 𝗨𝘀𝗲 Adoption is everything. Modern AI integrates into your team’s daily tools, so insights appear exactly where reps work. 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗿𝗲𝗽𝘀 𝗿𝗲𝗹𝘆 𝗼𝗻 𝗴𝘂𝘁 𝗳𝗲𝗲𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗿𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸𝘀. 𝗪𝗶𝘁𝗵 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗲𝘃𝗲𝗿𝘆 𝗺𝗼𝘃𝗲 𝗶𝘀 𝗴𝗿𝗼𝘂𝗻𝗱𝗲𝗱 𝗶𝗻 𝘀𝗶𝗴𝗻𝗮𝗹 𝗮𝗻𝗱 𝘁𝗶𝗺𝗶𝗻𝗴. 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝗲𝗮𝗿𝗹𝘆 𝗮𝗰𝗰𝗲𝘀𝘀 to your own AI sales agent that does this for your team: 👉 https://tally.so/r/m6BA6P

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. 

Here's how Nate Vogel led enablement teams at Tableau, Salesforce, Gong, Databricks, and more 👇 Clara Johnson and I got Nate in studio here in Seattle for an interview. And bummer, the video was corrupted, but we captured the audio! Here's what we learned: ✅ Tableau/Salesforce: Focus on managers FIRST and specialize It's easy to over-rotate on sales training for reps. But managers are where the magic happens. Build every program WITH managers, get their buy-in, and show them how to reinforce the training. Second, as much as you can, tailor enablement by skill level, role, segment, etc. I can't tell you how many teams don't provide SDR-specific enablement and just have them join the AE sessions. ✅ Gong: Put aside your pride The Gong enablement did a big messaging rollout. Trained the entire sales org, certified them, etc. But Gong's conversational intelligence data showed that it wasn't effective: - Customers weren’t asking questions or paying attention to slides - Every customer was asking about Integrations, which wasn't a focus at all You need great tools to measure the correlation between behavior change and results. Don't guess. And don't be afraid to scrap what isn't working. ✅ Databricks: AI Nate's a big believer that enablement professionals should be experts in AI. Not to create a few email templates or sequences. Or to draft some new messaging. But to spot big trends in what buyers are sharing in sales calls. To analyze what the best reps are doing. To give reps an "always on" sales coach to ask anything (and actually get great insights). ✅ Pigment: Scaling internationally We see this all the time in Outbound Squad: international teams subject to US-centric training. We treat EMEA like it's a country instead of a continent with hundreds of languages and cultures. AI gives us a big leg up now with global sales teams. Translate everything. Not just the words, but the meaning. The culture references. The nuances. EMEA and APAC must get the attention they deserve. ~~~ Nate, we'll have to get you back in the studio again soon so we can do proper video interview! Listen to the full interview with Nate on the Outbound Squad podcast here: https://lnkd.in/gA4j5hUS

Last week, I hosted a dinner for a room full of CROs and VPs of Sales (Turing, Faire, Salesforce, Luma AI, Maven). We spent 3 hours talking about the BIGGEST pain points in sales. 𝟭. 𝗧𝗼𝗼𝗹 𝗼𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 Everyone is overwhelmed: "I go to Notion for this, then Gong, then HubSpot, then Claude, then 5 more sales and AI tools...” More than ever, there’s a desire to simplify reps' lives. They want one sales tool for each rep that is really good at the main jobs out-of-the-box. Claude/ChatGPT are not built for managing large sales organizations; they're hard to govern and reps are not adopting them for anything other than research. Vast majority of tasks remain manual. 𝟮. 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗲 𝗼𝘂𝘁 𝗼𝗳 𝗔𝗜 Reps aren't tinkerers. They don't want another AI tool to learn or figure out which AI model to use. They need dead-simple activation around the jobs that matter. At the same time, CROs need visibility on token costs, action costs, and potential savings from a cheaper model. And they want it optimized on their behalf. 𝟯. 𝗘𝗻𝗮𝗯𝗹𝗲𝗺𝗲𝗻𝘁 𝗶𝘀 𝗮 𝗵𝗮𝗶𝗿 𝗼𝗻 𝗳𝗶𝗿𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 AI product development is a double edged sword. Every week, 5 new features get released, but there’s no process or owner to communicate to sales how they impact the story they tell customers. 1:1 coaching was the top requested use case, with hopes that reps will prefer a great AI coach over their manager harassing them about activity goals. 𝟰. 𝗔𝗜 𝗶𝘀 𝗿𝗲𝗮𝗹𝗹𝘆 𝗯𝗮𝗱 𝗮𝘁 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿-𝗳𝗮𝗰𝗶𝗻𝗴 𝘄𝗼𝗿𝗸 When reps use tools like Claude to generate assets, the consistency and quality is just not there. The voice doesn’t sound like them. The visuals don’t match their branding. Every generation looks 20% different than the last one, leading to hours wasted on last mile editing. — Every CRO in that room has the budget and authority to buy any tool in the market. But they're still looking for tools that add up to less work instead of more. At Mutiny, we are committed to building a sales assistant that simplifies reps' lives and seamlessly handles complex enterprise deals.

Human-in-the-loop and AI orchestration are the most misunderstood concepts in B2B SaaS. 1️⃣ Most think "human-in-the-loop" means AI makes their work easier. Wrong. 👋 It means humans handle the cases AI can't solve. All day long. In real time, or as close as possible. The 30% of support tickets that are too complex. The sales conversations that need real judgment. The edge cases that break automated systems. 🚵 As AI gets better, the remaining human work gets HARDER, not easier. 2️⃣ And "orchestration" isn't picking vendors and watching dashboards. It's 60+ days of intensive training after deployment. Daily quality auditing. Managing 5-10 AI systems that each have unique failure modes. 👉 SaaStr's reality check: We sent 4,495 AI emails with top response rates, but it required: • 90 minutes every morning training the AI • 1 hour every night reviewing performance • Real-time responses throughout the day • 20+ million words of training content 🫵 Doing AI right is more work than not using AI at all Perplexity's CBO revealed another layer at SaaStr AI Summit 2025: AI changes WHEN you work, not just what you do. Sales reps now use AI live during prospect calls, making split-second decisions about what intelligence to surface while maintaining authentic conversations. Support already proved this model works: • Decagon: 70% deflection rates • Duolingo: 80%+ automation • Intercom: 86% resolution rates But those numbers hide the human orchestration behind them. Support teams evolved into AI managers, not disappearing but becoming more specialized. They do the tough stuff now. The multiplication effect hits when you deploy >multiple< AI systems. Now you need people who understand how your chatbot's limitations interact with your email automation's strengths. How to prevent AI systems from amplifying each other's errors. The uncomfortable truth: AI success requires "S-tier human orchestration" to get top-tier results. The companies winning with AI aren't replacing humans—they're making humans AI-capable. The future with AI in B2B isn't >less< human work. It's different and more human work: more complex, more valuable, and just plain more of it. And yes, more intense. Higher ROI? Yes. Much more work? Also yes.