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.

Just spoke with a sales leader who went 'all in' on AI: AI email writer: $25k AI dialer: $80k AI research tool: $25k AI coaching platform: $30k Total spend: $160k Results after 90 days: - Reps jumping between 4 different AI interfaces - Contradicting suggestions from different AIs - 4 different chrome extensions fighting each other - Multiple tools with less than 30% adoption - Rep productivity down 35% Throwing AI tools at the problem isn't the answer. Having your reps manage 4 different AI assistants is worse than having none at all. The future isn't more AI assistants. It's a smarter workflow. #sales #AI #prospecting

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.

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?

We hear from customers who are interested in using the power of AI to help their sales team scale their business. Internally, AI is one of the tools that Amazonians use daily to improve our productivity and do things faster and more efficiently. In that vein, our latest ML blog gives a great inside look at how AWS sales teams are using Account Summaries—one of our first production GenAI use cases built on Amazon Bedrock. Account Summaries help us stay customer obsessed by generating 360-degree views of an account, available on demand and delivered proactively ahead of meetings via Slack. They integrate both structured and unstructured data, including key metrics, real-time web data, ML insights and AI-driven recommendations. Since its internal rollout last year, more than 100,000 summaries have been generated by our sellers, saving them 35 minutes per briefing. Check out our ML blog to learn how Account Summaries are helping our field teams scale and deliver better customer outcomes. https://lnkd.in/gTee4agv Here’s part of a sample output from Account Summaries:

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

Here's how AI kills clicks and why we're moving to the intent era of marketing. The internet era was all about clicks. Your buyer typed in Google: "best sales software for startups" Clicked several blog posts and reviews on third-party sites and maybe eventually clicked on your website. Today, your buyer spends all day with their AI assistant (Claude, ChatGPT, Gemini). Eventually, all those AI assistants will use the software on their behalf. They know they're adding sales headcount and working on a new sales methodology, and they've helped them forecast this year's sales growth numbers. It recommends they upgrade their sales software to meet those goals, and it curates everything they need to know to on what product to buy. No search. No clicks. Just one AI-curated answer delivered immediately—personalized to that person's needs, based on all of their previous chats. Anyone who has turned memory in ChatGTP can already understand how personal that chat experience will be. It's amazing. How do marketers win in the AI era? 1. Build influence. Creator approach to content. Build influence through incredible media (YouTube, Newsletter, Podcasts, Social). 2. Stop waiting for clicks. Be able to detect buyer intent signals in real time. 3. Don't spray and pray content. Craft assets tailored to individual intent. 4. Be agile. AI allows you to go through rapid iteration. Monthly and quarterly cycles are too long. In the AI era, if you wait for clicks, you've already lost.

I've been asking tech GTM leaders about their most impactful AI workflows. Almost none of them are taking on arguably the biggest $$ opportunity: AI workflows for enterprise sellers. I took this data to Emir Atli, co-founder & CRO at HockeyStack. He's been living AI x GTM firsthand as his team adopts AI agents internally to increase win rates (5-7 pts) & reduce cycle times (down 18-20%). The biggest takeaways from our conversation on AI agents for GTM: 1. AI depends on context. Yet nobody has clean data on their enterprise sales pipeline & CRM fields weren't built to give AI the context it needs. 2. Emir's most impactful AI agent at HockeyStack has been a "next best action" agent, which helps AE's process more pipeline w/o dropping the ball on anything. 3. Deploying this AI agent started with an entire blueprint of the sales process based on reverse-engineering what the best AEs did during actual deals that were won vs lost. For example: -- The best AEs used a gifting motion w/ their champion -- They created a custom deal workspace & Slack channel for each prospect -- They pulled in execs (like Emir) to get to peer execs w/in their accounts 4. These actions could then be turned into a product (AI agent) that gives AEs precise guidelines about what to do next for each deal. AEs can still choose to accept or ignore the AI recommendations. -- This helps AE's deliver a VIP experience to all of their accounts, not just the top ones. -- This makes forecasting/pipeline reviews more objective since AI agents already know exactly where each account sits & whether it's progressing. -- This accelerates cycle teams since there's less guesswork about what to do next for each deal. 5. Once a standard AI agent was deployed (& working), the team could keep iterating off this baseline. Their next step: better tailoring the sales blueprint to the unique ways that different AEs work. Listen to the full episode of the Mostly Growth podcast here: https://lnkd.in/eybqpcxe --- I'd love to see (& feature) more folks tackling AI for enterprise sellers. If you're seeing traction here, drop a comment or send me a DM 😁 #hockeystackpartner

Most AI workflows overpromise & undersell. But one of my favorites has (actually) driven hundreds of thousands in incremental revenue. The CEO of Zapier—who’s the homie—shared it with me, and I’ve been hooked ever since. Think of it as an AI SDR, who qualifies, organizes, and engages sales leads. Here are all of the steps my sales sidekick takes: 1) Extracts the name, email, company, role, and website for any lead that fills out a sales form on our website 2) Researches the lead online to gather the following info: - Company website & recent news - Linkedin profile and background - Company size, industry, and estimated funding/revenue/growth indicators - Specific pain points related to my company’s service 3) Compares lead info against ideal ICP criteria I’ve set: - US-based company - VP-level & up  - Revenue: $10m-$500m annually  - Company size: >50 employees 4) Scores the lead as “Great Fit,” “Possible Fit,” or “Poor Fit” based on ICP comparison 5) Adds a new record to our CRM with the following details: - Contact details (name, email, company, role) - Research findings (company size, revenue, industry) - ICP fit score - Date submitted 6) Conditional logic based on Lead Fit IF lead is “Great Fit” Draft a personalized email in Gmail incorporating: - Their specific company challenges identified in research - Relevant case studies from similar companies - Clear next steps for a discovery call IF lead is “Possible Fit” Send direct message in Slack to me with:  - A summary of lead and research findings - Reasons for uncertainty regarding ICP fit - A recommendation with supporting data - The question: “Should I draft a response email for this lead?” IF response is “yes”: follow great fit action  IF response is “no”: no response Update CRM for this lead based on action taken in Step 6. Let me know if you have any questions—and if you take it for a spin—let me know what you think. #ZapierPartner

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