How to Integrate AI in GTM Strategies

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

Step 3 of 7 for AI Enablement: Identify and Prioritize AI Use Cases See full 7-step breakdown here: https://lnkd.in/g3t7MiZb In setting up AI for success, we’ve covered the foundations: Step 1 defined clear business objectives. Step 2 assessed team readiness, revealing gaps to achieve outcomes. Now for Step 3: Identify and Prioritize AI Use Cases. This step isn’t just about knowing where AI could fit; it’s also about evaluating tools to ensure they meet essential requirements—and testing the top choices with trial runs. First: Explore What AI Tools Are Out There Before diving into specific use cases, it’s important to understand the types of AI tools available that could support your goals. If you’re unsure where to start, here are two valuable resources: • Theresanaiforthat.com – A searchable directory of AI tools across industries. • GTM AI Tools Demo Library – A curated list of go-to-market AI tools from the GTM AI Academy (l^nk in comments). Identify AI Opportunities with the PRIME Framework With a better understanding of AI options, use the PRIME Framework to identify use cases that directly address your most critical business gaps: • Predictive: Can AI help forecast outcomes? • Repetitive: Are there time-consuming, repeated tasks? • Interactive: Could AI enhance customer engagement? • Measurable: Can AI provide useful metrics? • Empowering: Can AI support creativity or productivity? Evaluate Tools with a Checklist Once you’ve outlined use cases, evaluate potential tools to ensure they meet critical requirements before trialing them: • Security & Compliance: Does the tool meet company standards? • Governance: Does it support data governance and accountability? • Cost & ROI: Is it cost-effective based on expected value? • Scalability: Can it grow with your team’s needs? • Integration: Will it fit with your current systems? Evaluate Tools: Make sure selected tools meet security, compliance, and integration needs before trial runs. Pilot Testing Once you’ve prioritized and evaluated, move into a pilot phase. Select top tools to trial with a small pilot team. This phase helps test effectiveness, build internal champions, and refine any processes before rolling out to the larger team in Step 4. Your Checklist for Step 3 1. Explore AI Options: Start with Theresanaiforthat.com and GTM AI Tools Demo Library. 2. Identify Use Cases with PRIME: Target high-impact areas. 3. Evaluate Tools with the Checklist: Confirm tools meet security, compliance, and integration needs. 4. Pilot Test: Trial top tools with a small team to validate effectiveness. By following this approach, you’ll set your team up for measurable, AI-driven success with tools that are tested and proven valuable. Ready to PRIME your AI Enablement? Check out free resources in the GTM AI Academy: • PRIME Use Case Guide • Impact-Feasibility Template • AI Critical Requirements Assessment Up next.. Step 4 of 7 for AI Enablement..

after talking to 37 different CEOs in one week (spoke at a closed-door PE event for $50M+ companies), here’s what i learned: effective GTM teams are using AI to: 1. personalize 2. experiment 3. accelerate so i created THIS slide that is now most shared in the CEO community on how to use AI across all 6 GTM motions: → inbound use AI to qualify leads, send video intros, and reduce sales friction before the first call. → outbound automate repetitive tasks and personalize outreach at scale — so your reps focus on what matters. → product-led guide users with AI-powered walkthroughs and demos tailored to behavior and context. → partner-led train and enable partners faster with on-demand demos and custom content. → event-led turn event leads into pipeline with AI follow-ups and content based on interests. → community-led use AI avatars to keep community conversations alive with instant answers and real-time moderation. in a noisy market, personalization wins. in a fast-moving world, experimentation wins. AI can help you do both, across every GTM motion. your hot take? love, sangram p.s. i share frameworks and real-world GTM examples like this every week. follow Sangram Vajre or DM me to get access to these.

“Our AI SDR was generating decent volume, but nothing was converting, so we shut it down.” 👆 I’ve heard a version of that from GTM leaders too many times to count. Here's what I'm seeing in the field: - Revenue leaders either treat AI SDRs like magic bullets that will solve all of their pipeline problems or… - They write them off entirely after a failed pilot that generates more unsubscribes than meetings. After working with dozens of GTM teams to roll out AI SDR programs using RoomieAI—our suite of AI agents—I can tell you where the winners are seeing success. —— NOTE: Every AI SDR tool can send emails at scale. The differentiator is the intelligence behind the automation. Basic firmographic data—company size, industry, job title—is not enough. You need real intent signals. Real account context. Real understanding of where prospects are in their buying journey. The data inputs make all the difference. —— 🪶 Long-tail acquisition Your reps should be focused on their 500 target accounts. Your AI agents should be focused on the 5,000+ other accounts that don’t make the “white glove” list. One of our customers fully automates outbound to low-ACV accounts while doubling down on human touch for high-ACV accounts. AEs have more time to research prioritized accounts, craft relevant messaging, and multithread. Meanwhile, the company makes sure it doesn’t leave pipeline from downmarket accounts on the table. 🏎️ Rapid response to specific buying signals Some buying signals have a shelf life measured in hours, not days. Others are incredibly straightforward and transactional. This is where full LLM automation makes the most sense. One of our customers automates AI outreach whenever an economic buyer visits the pricing page. The AI agent doesn’t reference the web visit. Instead, it crafts messaging in real time based on deep research from a *different* AI agent, pulling from contextual information that explains why an account is in-market. ⚖️ Inbound qualification and routing Just because a lead came inbound doesn’t mean it should go to the top of your list. AI can help filter the best for human reps and handle the rest. One of our customers uses AI scoring to pre-qualify, contextualize, and route leads based on dozens of data points and signals that humans would never catch at scale. If the intent and fit are high enough, it’s handed off to an SDR who now has all the context they need to have a valuable conversation from the first interaction. If not, a prebuilt sequence can be triggered and an AI agent can handle the first touch based on relevant account research and person-level intent signals. —— The companies winning with AI SDRs aren't the ones deploying spray-and-pray bots. They're the ones that understand exactly where AI excels and where human relationship building still reigns supreme.

Most people building GTM tooling are obsessing over third-party data. But, there is a goldmine of information in every company’s systems already: customer conversations, emails, and meeting transcripts. The problem is - this data sits dormant (becoming less useful over time as it collects dust in the corners of the CRM or CDW or otherwise) and is distributed across disparate systems and “objects.” Attention is activating this data. Mining for the interesting nuggets and then operationalizing them, in real-time. That’s the vision they’re realizing, by building a system of AI agents that don't just capture sales conversations—they automate the work traditionally done by the best enablement analysts, RevOps specialists, and top performers. The goal? Help GTM orgs achieve 10x results with just 10% of the workforce. This is super exciting to me, which is why I was stoked to spend some time with Anis Bennaceur, Co-founder & CEO of Attention, recently. And I put together a deep dive post on The Signal. I agree with Jeff Bezos' analogy of AI being like electricity ("it will be everywhere, in every application"). For example, here are 9 ways a GTM team could leverage AI/Attention: 1/ One-click sales collateral generation: After a discovery call, automatically create a tailored sales deck that incorporates the prospect's specific pain points, business goals, and objections mentioned during the conversation. 2/ Competitive intelligence automation: Receive weekly reports on competitors mentioned in deals, including how they're perceived, their positioning, and the frequency of mentions—all without manual analysis. 3/ Closed-won/closed-lost analysis: Instead of spending days manually reviewing won and lost deals, get comprehensive insights in minutes on why deals are succeeding or failing. 4/ Automated call scoring: Evaluate rep performance based on best practices without requiring managers to listen to hours of calls. 5/ Cross-selling opportunity identification: Automatically identify and route opportunities mentioned in conversations that might be relevant to other teams or products. 6/ Business case generator: The agent compiles a comprehensive business case document based on all conversations with an account, extracting the specific pain points, quantifying the impact, and building a compelling ROI model. 7/ Content gap analysis: Identify questions from prospects that reps struggle to answer effectively, highlighting needs for new content or training. 8/ Outbound signal detection: Extract compelling events from prospect conversations to inform outbound strategies, like "Company X just lost their growth marketing manager and needs to get pipeline back in order." 9/ Brand perception tracking: Monitor how your positioning against specific competitors evolves over time, with insights drawn directly from customer conversations. The possibilities are endless. Check out the full article now: https://lnkd.in/g85JNmdj