The Role of AI in Revenue Team Strategies

Something big is shifting in the revenue engine. Every week brings another AI release: new copilots, autonomous agents, predictive insights embedded into the flow of work. This isn’t experimentation anymore. It’s operational. For years, GTM teams optimized for visibility: more dashboards, tighter reporting, more activity tracking. All of it was focused on explaining what already happened. AI is flipping that orientation. Systems are starting to influence what happens next. Forecasting is becoming probabilistic, account planning is driven by live signals, and pipeline reviews are shifting from data inspection to risk interpretation. AI isn’t diminishing the human side of selling. It’s amplifying it. Judgment still matters. Trust still matters. Creativity still closes complex deals. But here’s the shift: AI is compressing the distance between signal and action. It’s collapsing research cycles. It’s surfacing risk before it shows up in the forecast. It’s rewriting how pipeline is built, qualified, and expanded. Which means advantage is being redefined. The edge will not come from having AI. Everyone will have it. The edge will come from how deeply it’s embedded into the way your revenue engine runs. ➡️ What this means for revenue leaders: If AI isn’t wired into forecasting, account prioritization, demand generation, renewal strategy, and planning rhythms — it’s ornamental. Clean data foundations, unified signals, and AI-native operating cadences will matter more than any single tool choice. ➡️ What this means for frontline sellers:  Activity volume won’t differentiate you. Interpreting system-driven insight will. The sellers who pressure-test recommendations, combine signal with instinct, and use AI as a thinking partner (not a shortcut) will move faster and make better calls. We don’t know exactly what the revenue org looks like by year-end. But this isn’t a tooling cycle. It’s a structural one. And I say this a lot: "Don’t be upset at the results you didn’t get for the work you didn’t do." This is the work.

I went quiet for months. I've been deep inside 5 GTM teams from $20M to $200M ARR, SF to Paris. Here are 5 ways top teams quietly use AI to fill pipeline (but no one talks about): 1. Dynamic ICP Refreshes Quarterly Forget the annual ICP review. Top teams use AI to analyze closed-won patterns every 90 days. They look for buying triggers, industry shifts, department size changes - building multi-layer profiles that continually sharpen their targeting. 2. High-Impact Plays Only That viral LinkedIn post promising 125 warm leads? Pure fantasy. Elite teams focus on AI plays that actually drive pipeline: past clients changing jobs, cross-sell opportunities, and companies actively hiring their ICP. They measure impact, not activity. 3. GTM Command Centers They connect 1st, 2nd, and 3rd-party data into a single context layer. For each account: GTM readiness score, sales play fit, buying committee maps, signal summaries. Their AI then suggests the best "next play" based on signals and historical engagement. No more random acts of sales. 4. AI + Human Orchestration Only lowest-tier accounts get full AI automation. For everything else, AI captures insights at company/contact levels to help sales find unique angles. The human element still drives deals - AI just makes them smarter and faster. 5. Dedicated AI Training Cadence The AI landscape changes weekly. Smart teams allocate specific slots to experiment with AI use cases and bring in experts to level up their team skills. They're building the muscle while competitors debate whether to start. -- I’m Alexis Martial. I help B2B marketing teams break free from outdated playbooks and build GTM systems that drive revenue. Tired of fluffy AI content from self-proclaimed gurus? I share real, tactical ways to use AI to grow revenue—every week 👇

Stop asking AI to summarize. Start using it to improve your decisions. Most founders use AI to clean up notes, draft emails, or shorten documents. But the real upside to it is clearer thinking.  It can analyze patterns in your sales calls, stress-test your strategy, surface revenue leaks in your pipeline, and refine your positioning. So, if you’re only using it to condense information, you’re 100% underusing it. When used well, AI transforms your business from good to outstanding. Here are 8 prompts I use to turn AI into a growth tool👇 1️⃣ Extract Customer Buying Signals “Analyze this sales call transcript. Identify the 3 strongest buying signals and what objections are hiding beneath the surface.” Reveal what your ICP really cares about. 2️⃣ Turn Feedback Into Revenue Moves “Translate this customer feedback into 3 actionable changes that would increase retention or upsells. Prioritize by revenue impact.” This turns feedback into revenue decisions. 3️⃣ Spot Market Gaps “Review this competitor research. Find 3 positioning gaps where we could own a category or problem they're ignoring.” AI completes your market research and saves you hours of time. 4️⃣ Build Pipeline Forecasts “Using this deal data, forecast pipeline velocity and flag which stages are slowing conversions. Suggest 2 fixes.” AI acts as your revenue ops lead by spotting bottlenecks, tightening stages, and protecting revenue before it slips. 5️⃣ Reframe for Your ICP “Rewrite this pitch for a [specific persona]: what language do they use, what outcomes do they care about, and what proof do they need?” AI strengthens how you frame your offer, turning generic pitches into buyer-specific arguments. 6️⃣ Design a Repeatable Playbook “Extract the repeatable steps from this campaign. Turn it into a playbook with inputs, actions, and success metrics.” AI turns what you’re doing into a repeatable system. 7️⃣ Find High-Leverage Opportunities “Analyze this data and identify leverage points where small changes would create the biggest revenue lift. Explain why each matters.” AI helps you focus on growth by isolating the 20% of inputs driving 80% of revenue. 8️⃣ Challenge Your Strategy “Act as a skeptical advisor. Poke holes in this strategy and surface the assumptions that could break it if they're wrong.” Use AI as your second brain, so flawed assumptions don’t turn into pricey mistakes. Founders who treat AI like a shortcut get efficiency... Founders who treat it like a thinking partner get better outcomes. Let AI make your life easier and increase your output tenfold. The ego feels good, but systems make money. Which of these AI prompts will you try out this week?  Let me know in the comments. I share practical frameworks like this every week in Network to Net Worth.  For more, subscribe here 👉 https://lnkd.in/gFp5bEbt ♻️ Repost to help others use AI for growth as well as productivity. And follow me, Rohan Sheth, for more on business growth. 

Let's address the elephant in our revenue space: Are AI agents real or just another tech buzzword? After years of building AI solutions, Waseem Alshikh's recent breakdown of agent types perfectly captures where we really stand - and, more importantly, where the true value lies. The reality is that AI agents aren't some futuristic concept—they're already reshaping how revenue teams operate. At Aviso AI, we moved beyond the hype, understood the distinct types of agents, and built real-world applications for modern GTM teams. These applications are designed not only to improve the productivity of knowledge workers but also to help our customers generate new revenue opportunities. Here's how we're actually leveraging different types of LLM frameworks: 1️⃣ Basic LLM Agents The High-Volume Task Masters Aviso’s Agentic Workflows leverage open-source LLMs to generate personalized emails and multi-channel sales sequences. These agents excel at handling high-volume tasks with consistent quality, freeing up time for strategic work. 2️⃣ Chain of Thought Agents The Problem Solvers By integrating LangChain, Aviso’s agents analyze complex RFPs, earnings calls, and reports. They provide logical insights and actionable recommendations, empowering teams to make confident, high-stakes decisions. 3️⃣ RAG Agents (Retrieval-Augmented Generation) The Intelligence Gatherers Aviso’s Ask Anything tool synthesizes data from CRM, calls, emails, and more to answer deal-specific queries. These agents turn data overload into strategic advantage by providing contextual, real-time insights. 4️⃣ ReAct Agents (Reasoning and Action) The Real-Time Revenue Coaches Aviso’s MIKI analyzes data and provides actionable guidance, executing the next-best actions instantly. They act like your always-on AI Chief of Staff, helping teams save time and make better decisions in real-time. 5️⃣ Planning Agents The Strategic Orchestrators Aviso’s Agentic Workflows help map paths to quota, perform account planning, and create sustainable growth strategies. These agents turn complex revenue challenges into clear, actionable plans for scaling success. Aviso's AI Brain, the mastermind behind Agentic Worfklows, orchestrates these agent types in harmony, each playing its crucial role in the revenue ecosystem. What makes this approach powerful isn't just the individual capabilities - it's how these agents work together to solve complex revenue challenges. The discussion around AI agents needs to evolve from "Are they real?" to "How do we deploy them effectively?" Because when implemented thoughtfully, they're not just tools - they're transformative partners in revenue generation. #AI #RevenueIntelligence #AIAgents #FutureOfSales

AI is reshaping GTM - but are we using it right? AI is already transforming how sales, marketing, and revenue teams operate, but many GTM leaders are making a critical mistake - they're relying on AI without ensuring the data feeding it is accurate and complete. New research from Pavilion and Ebsta analyzed 655,000 opportunities, 240,000 minutes of discovery calls, and insights from 2,000+ CROs and sales leaders and the results are eye-opening: Key Findings: ✅ 81% of businesses will use AI for pipeline generation by 2025. ✅ 91% of CROs say AI’s biggest impact is eliminating manual tasks, freeing up teams to focus on high-value work. ✅ Yet 44% of seller interactions are never recorded in CRM, meaning AI models often lack the full data needed to make accurate predictions. ✅ 36% of deals slip, often because key decision-makers are missing from CRM records. 🚨 Here's what this means for GTM leaders: 1️⃣ AI won’t fix bad data, so you need to prioritize data hygiene. - Audit your CRM: Ensure key contacts and interactions are logged. - Automate data capture to eliminate manual entry gaps. - Regularly review pipeline accuracy - who's actually engaging in deals? 2️⃣ Increase selling time by reducing non-revenue-generating tasks. - Re-evaluate your team's time: If sellers spend only 21% of their day with customers, what’s taking up the rest? - Use AI for admin-heavy tasks like follow-ups, scheduling, and prospect research. - Invest in conversation intelligence to track key interactions automatically. 3️⃣ Fix pipeline blind spots - track engagement across buying teams. - Ensure every opportunity is multi-threaded - too many deals hinge on a single stakeholder. - Use AI-powered insights to flag disengaging accounts before it’s too late. - Train sellers to proactively identify who isn’t in the deal yet but needs to be. 4️⃣ AI should enhance decision-making, not replace it. - AI can surface insights, but human judgment is still critical. - Review AI-driven recommendations with real-world deal context. - Test, refine, and train AI models using your best reps' winning behaviors. The bottom line: AI can’t replace great GTM leadership, but it can make leaders smarter, faster, and more efficient. Now is the time to focus on clean data, intelligent automation, and AI-driven insights to unlock its full potential. How is AI changing your GTM approach? Let’s discuss. 👇 #GTM #AI #RevenueLeadership #kathleenhq

Your old GTM playbook is soon dead. AI agents are writing the next one. Based on my extensive background as an Enterprise Software CRO, many CEO’s and revenue leaders are asking me: “Which AI tool should we add to our stack?” Wrong question. The real shift is this: AI is turning GTM from manual playbooks into autonomous systems that plan, act, and learn across your entire revenue engine. The AI technologies that matter most for GTM aren’t just dashboards and copilots. They are: • Agentic AI for execution – AI agents that run parts of your GTM: qualifying leads, handling outreach, routing opportunities, cleaning CRM, orchestrating campaigns. Not “assistants”, but autonomous doers owning workflows end‑to‑end. • Predictive & prescriptive intelligence – Models that stop telling you what happened and start telling you what to do next: which accounts to prioritize, what motion to use, and how to allocate resources across segments and regions. • Generative AI for content and proposals – Systems that generate hyper‑personalized emails, pages, collateral, and proposals in minutes, at scale, tuned to each account, persona, and stage of the journey. • Conversational AI and call intelligence – Digital teammates on every call and every page: qualifying visitors in real time, surfacing battlecards, capturing objections, and feeding those insights back into product, marketing, and enablement. • AI‑driven ABM and journey orchestration – Engines that detect buying intent long before a form fill, then personalize every touchpoint across ads, website, email, sales outreach, and product. • AI‑native “revenue brains” on top of the stack – A meta‑layer that sits above CRM, MAP, CS tools and continually optimizes GTM like a living system: testing offers, channels, territories, and messaging, then redeploying what works automatically. The provocation is simple: If your GTM still depends on humans stitching together disconnected tools and spreadsheets, you’re competing against organizations whose GTM is literally learning faster than yours every day. This is no longer about “augmenting reps.” It’s about redesigning GTM so that AI owns the repetitive work and humans own trust, creativity, and strategy. My recommendation to GTM leaders: • Map where AI agents can own workflows end‑to‑end, not just produce recommendations. • Decide where humans truly create differentiated value—and remove them from everything else. • Start treating your GTM like a product: instrumented, experiment‑driven, and continuously improved by an intelligent core. The next competitive moat won’t be your playbook. It will be the learning speed of your AI‑powered GTM system. Are you still adding tools to your stack, or are you building a revenue brain? #AI #GTM #Sales #Marketing #SaaS #AgenticAI #RevenueLeadership

AI will not replace sales teams. But it will expose weak sales execution. Over the past months, I’ve seen many revenue teams experiment with AI. Most of the use cases are still quite superficial: • writing outbound emails • generating marketing content • summarising meetings Useful, yes. But this is not where the real impact lies. The real opportunity is when AI is applied to revenue execution itself. Three areas stand out. 1 — Pipeline intelligence In many organisations, pipeline quality still relies heavily on individual judgement. AI can help detect patterns such as: • artificially inflated deals • unusually long sales cycles • poorly qualified opportunities Used properly, this can significantly improve pipeline quality and forecast reliability. 2 — Deal strategy Complex deals are often lost for very simple reasons: • incomplete stakeholder mapping • weak negotiation strategy • poor understanding of the buyer’s priorities AI can act as a strategic thinking partner to analyse deals and highlight risks before they become blockers. 3 — Customer intelligence The most profitable growth often sits in the customer base. Yet many companies under-utilize the signals they already have: • product usage • support interactions • customer feedback AI can help identify: • early churn signals • expansion opportunities • new use cases within existing accounts AI will not replace revenue teams. But it will likely become a powerful tool to improve how revenue teams think, prepare and execute. And the organisations that integrate it into their daily GTM execution will probably gain a meaningful advantage.

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.

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

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.