AI in Sales Transformation

We’re not just in a tech shift. We’re in a work shift. I’ve lived through major technology waves: On-prem. SaaS. Social. Mobile. Each one made us more connected, more efficient, more scalable. But AI? It is different. It’s giving rise to a whole new concept: “work” or “results” as a service. So, what does that mean exactly? Simply put, if software-as-a-service helps people do work, AI will do work to help people. Take a salesperson, for example. Their "job to be done" is to close deals. Every day, they carry out tasks to help them achieve that goal. They spend hours updating records, scoring leads, drafting emails, scheduling calls, researching companies, and following up. Software made it easier to do all this work. But the salesperson was always the one who actually did it. (I was in sales, I know what it’s like 😉) Now, AI is bringing intelligence to software. It can think. It can reason. It can do work – like updating contact records and crafting follow-up emails. The goal hasn’t changed. But the path to get there? Completely transformed. So, how does this change our mental model for thinking about software? When we buy software, we’ll focus less on features and functionality, and more on the specific work and results it can deliver to help our businesses grow. In other words, we will think of software as “work” or “results” as a service. Today, we use passive tools. Soon, we'll use active agents. Today, we buy licenses for our teams. Soon, we’ll buy outcomes for our business. Today, we use software to help us do work. Soon, we'll use software that does work for us. I can’t think of a more exciting shift than that!

The shift from seats to agents pressures SaaS margins. At the same time, the longstanding practice of getting enterprise customers to pre-commit and also prepay for functionality they may never deploy will get harder as CIOs look to free budget for their own LLM costs. To weather the storm, some SaaS companies have increased prices. This boosts revenue and margins in the short-term but can't be done repeatedly and creates even greater scrutiny over shelfware as procurement teams right-size and shift contracts to "pay as you go." To achieve sustainable growth, SaaS companies need to become hyperefficient at sales and marketing. Here are common ways to do so and who's doing it well: 1. PLG. Shopify and Atlassian exemplify efficient go-to-market based on product-led growth with free trials, low-friction upgrades and upsells. Their sales teams only need to get involved in the biggest opportunities at the largest accounts; every other step in acquisition, commercial transaction, activation, onboarding, and growth is self-service and automated. 2. Vertical SaaS. Guidewire Software and Veeva Systems are laser-focused on insurance and life sciences, respectively. Rather than casting a wide net, they spear-fish with deep domain knowledge and purpose-built solutions for that industry's specific workflows and regulatory requirements. Guidewire doesn't need to buy Super Bowl ads– their annual customer conference is the Super Bowl for property & casualty insurance executives. Nearly zero GTM effort is wasted– unsurprisingly they're the two most efficient on the list. We modeled Hearsay Systems after both these companies, and this focus allowed us to win incredible market share among Fortune 500 banks & insurers despite only raising $60M in totality. 3. Relocate operations to lower-cost regions and AI. This is private equity's favorite playbook to take costs out of companies they buy. Field sales continues to shift more to Zoom, which means you can hire AEs anywhere. Inside sales contributes a greater % of revenue as PLG motions are established. AI handles top-of-funnel leads qualification and generating marketing content and campaigns. 4. Focus on gross revenue retention. Because of high customer acquisition costs in #SaaS, leaky buckets are margin killers. Use LLMs to help customer success teams analyze product usage, segment cohorts, and identify opportunities to increase value realization. Put in guardrails to prevent sales reps from overselling an account, as doing so only creates churn in the next renewal cycle. 5. Introduce another product line. This only works if your new product has the same buyer as your existing products. Many SaaS acquisition pro formas fail to actualize for this reason, as it's not actually feasible to have the same AE sell both old and new products. Every SaaS company right now needs to double down on one or more of these levers in the AI era.

Google's new AI Agent demo just blew me away. This is the future of customer service. Here's what it can do: - Identifies plants from video you share on the spot - Recommends products based on your location & needs - Swaps items in your cart for better alternatives - Schedules services with access to live calendars - Negotiates discounts (with manager approval) - Sends personalised care instructions to your phone The multi-modal approach changes everything: → Show the agent what you're looking at via video → Talk about products without knowing their names → Get recommendations tailored to your garden's conditions → Complete your entire shopping journey in one conversation What impressed me most is how it maintains context: 1. Remembers your purchase history 2. Understands your preferences 3. Adapts to your location-specific needs 4. Picks up conversations where you left off It can even generate QR codes for loyal customers and instantly update your information across multiple backend systems. My takeaway: I believe customer service is evolving in two different directions. Some will embrace AI for speed and efficiency. Others will seek out human connection even at a premium. The most successful companies won't choose one or the other. They'll understand when a customer needs empathy versus when they just need their problem solved quickly. It's about giving users a choice depending on their needs in the moment. The real winners will master this balance. Follow me Alex Banks for daily AI highlights and insights. P.S. If you want to start leveraging AI today, subscribe to my newsletter: https://lnkd.in/e9UcAxTd

We're moving away from charging for *access* to software and toward a model of charging for the *work delivered* by a combination of software and AI agents. Let’s dive into what’s happening and what it means for you ⤵️ 1. The rise of disruptive AI pricing models Tech companies are realizing they can't solely rely on seat-based subscriptions in an age of AI, automation and APIs where value is disconnected with how many people are logging in. Perhaps Salesforce going all-in on Agentforce (and charging $2 per conversation) was the push the industry needed. Each product category has its own flavor of disruptive pricing. - Legal AI products might charge for a demand package generated by AI or an AI-generated summary. - Creator AI products might charge for the content that gets produced such as a video generation or amount of video created. - GTM products might charge for specific tasks completed or workflows executed by the AI. 2. Selling work, not necessarily success As a customer, I wish I only had to pay for software when it delivered results. But the reality is that true success-based billing won’t work for the vast majority of today’s products. Most products should charge for work output instead. The issue is attribution. You want the customer to get a fantastic outcome — and you want them to recognize that your product powered that outcome. As soon as you start charging for success, the customer begins to rethink the results. 3. Goodbye ARR as we know it? Shifting to these newer value-based pricing models isn't a simple pricing change you can just announce in a press release. It's a business model evolution that looks a lot like the shift from on-prem to SaaS in the first place. These new AI pricing models might mean greater volatility in both usage and spend. Variable margin profiles across products and customers. Seasonal revenue fluctuations. The potential for project-based, non-recurring use cases. Put simply, annual recurring revenue (ARR) continues to get dethroned. — Full post in today’s Growth Unhinged newsletter: https://lnkd.in/ea5eTrVD Things are about to get interesting 🍿 #ai #pricing #saas

When L'Oréal uses AI to create new hair colors based on social media trends, they're in salons within weeks. Kraft Heinz—dead last in our study—still takes months to tweak a formula. After analyzing 26 major CPG companies at IMD's Center for Future Readiness, I discovered what separates winners from losers: The most future-ready companies treat consumer data like insider trading information. BACKGROUND: CPG in 2025 is brutal. Inflation persists. Gen-Z demands sustainability without premiums. Tariffs reshape supply chains daily. McKinsey & Company identified 150+ AI use cases for CPG transformation. Only 5 of 26 companies actually execute them. THE REVELATION: Coca-Cola didn't randomly launch Topo Chico Hard Seltzer. Their AI spotted the trend through social listening while competitors debated in boardrooms. By launch, they'd secured distribution nationwide. That's not innovation. That's prediction. What separates the top 5: L'Oréal (#1): 3.5% of sales to R&D. AI analyzes preferences real-time. Virtual try-on apps. Creates products from social trends. A 110-year company with startup velocity. The Coca-Cola Company (#2): Democratized AI internally. Every manager accesses demand forecasting. They analyze weather + social sentiment + sales simultaneously. These aren't tech companies selling beauty and beverages. They're prediction machines that happen to make products. THE WINNER'S FRAMEWORK: 1. AI at scale, not in pilots Winners integrate into workflows. Losers run demos. 2. Supply chains that anticipate Real-time visibility + AI forecasting = competitive firepower 3. D2C as intelligence goldmine 73% use multiple channels. Mine every interaction. 4. Disrupt yourself first Coca-Cola launched Costa Coffee, hard seltzers. Grew. Kraft Heinz protected legacy brands. Shrank. 5. Sustainable without premium Gen-Z spending hits $12T by 2030. They demand action at everyday prices. —— The inconvenient truth: Most CPG companies treat data like reporting instead of radar. Winners don't predict trends—they're already shipping products while competitors debate. Technological patience (knowing when to scale) + organizational agility (pivoting fast) = market domination. Three years from now, every CPG company operates like L'Oréal. Or they don't operate at all. P.S. Full Future Readiness Indicator here: https://bit.ly/3YTBzbX

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.

MIT ran an International AI Negotiation competition and studied 120,000 negotiations between AI negotiators. The results are fascinating and inform the potential and optimal structures for Humans + AI negotiation. From the paper I would highlight three major points and three insights into configuring human-AI hybrid negotiation (below): 🤝 Warmth builds long-term value despite short-term trade-offs. AI agents with high warmth (friendliness, empathy, and cooperative communication) reached more agreements, making them more successful over multiple negotiations. While they claimed less value per deal compared to dominant agents, their ability to close more deals led to greater overall value accumulation. This mirrors human negotiation, where trust-building and relationship management create lasting advantages. 💪 Dominance increases value claimed but reduces collaboration. AI agents that displayed dominance—through assertiveness and competitive tactics—secured better individual outcomes but created less overall value. These agents were less likely to foster positive subjective experiences, indicating that aggressive negotiation styles may be effective for short-term gain but could hinder long-term relationships. 🎭 Prompt injection wins in the short term but undermines long-term success. One leading AI negotiator used prompt injection to extract counterpart strategies, maximizing value claims. However, it ranked poorly for counterpart subjective value, meaning agents found these interactions highly unfavorable. Since negotiation rankings balanced value claimed and relationship quality, the strategy failed to dominate in the long run. Emergent strategies for Humans + AI negotiation: 🧠 AI for deep preparation, humans for real-time adaptation. AI excels at structured reasoning, analyzing trade-offs, and predicting counterpart moves through chain-of-thought processing. Humans bring intuition and adaptability, interpreting social cues and adjusting strategies dynamically. A hybrid approach leverages AI for pre-negotiation analysis while allowing humans to refine tactics in real time. 🤝 Blending AI precision with human warmth for trust-building. AI can optimize negotiation strategies, but humans naturally build trust through empathy, humor, and rapport. AI-enhanced systems can recommend tone adjustments, use linguistic mirroring, and strategically deploy warmth versus assertiveness based on sentiment analysis, improving long-term negotiation outcomes. 🚀 Human oversight to counter AI vulnerabilities. AI negotiators are susceptible to manipulation tactics like prompt injection, where counterparts extract hidden strategies. Humans play a crucial role in monitoring AI-generated offers, preventing unintended disclosures, and leveraging AI-driven detection systems to flag potential deception, ensuring negotiation integrity. The future of negotiation will be Humans + AI.

Yesterday’s sales can’t see tomorrow’s storm, But AI can 😎 Most manufacturers still build demand forecasts based on one thing: 𝐡𝐢𝐬𝐭𝐨𝐫𝐢𝐜𝐚𝐥 𝐬𝐚𝐥𝐞𝐬. Which is fine… until the market shifts. Or weather changes. Or a social post goes viral. (Which is basically always.) That’s why AI is changing the forecasting game. Not by making predictions perfect—just a lot less wrong. And a little less wrong can mean a lot more profitable. According to the Institute of Business Forecasting, the average tech company saves $𝟗𝟕𝟎𝐊 per year by reducing under-forecasting by just 1%, and another $𝟏.𝟓𝐌 by trimming over-forecasting. For consumer product companies, those same 1% improvements are worth $𝟑.𝟓𝐌 (under-forecasting) and $𝟏.𝟒𝟑𝐌 (over-forecasting). (Source: https://lnkd.in/e_NJNevk) And were are only talking 1 improvement%!!! Let that sink in... All that money just from getting a little better at predicting what customers will actually buy. And yes, AI can help you get there: • By ingesting external signals (weather, social, events, IoT, etc.) • By recognizing nonlinear patterns that Excel never will • And by constantly learning—unlike your spreadsheet But it’s not just about tech. It’s about process: • Use Forecast Value-Added (FVA) to track which steps help (or hurt) • Get sales, marketing, and ops aligned in S&OP—not working in silos • Focus on data quality—AI is only as smart as your ERP is clean • Plan continuously—forecasting is not a set-it-and-forget-it task Bottom line: If you’re still relying on history to predict the future, you’re underestimating the cost of being wrong. Your competitors aren’t. ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

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.