Let’s face it. You can’t last one day day working in GTM these days without someone saying AI. Since ChatGPT came onto the scene, the way revenue organizations prospect, engage, close, and retain customers now has to have an “AI strategy”. AI is the top down mandate from boards, investors, CEOs, and CROs down to Revenue Operations. Everyone is trying it but most use cases today that I see are far from what I think it will eventually be.
For RevOps leaders, the shift is both opportunity and challenge. The opportunity is to deploy AI to focus resources where they create the most value. The challenge is knowing where to start, how to build trust in outputs, and how to connect AI tools with existing GTM workflows.
In no particular order I’m listing out what I think are the top 10 use cases, as of today. Who knows, perhaps a year from now these are all table stakes and laughable.
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Lead Scoring and Prioritization
If there is one AI use case that nearly every GTM leader has heard of, it’s predictive lead scoring. The problem is as old as CRM systems themselves: how do we decide which leads are worth the sales team’s time?
Traditionally, lead scoring has been handled with a rules-based model. Marketing or operations teams assign points to firmographic and behavioral traits. A prospect might receive 10 points for being a VP, 20 points for working at a company with more than 500 employees, and 5 points for opening an email. It’s straightforward, transparent, and easy to adjust. But it’s also brittle. A static model ignores the nuances of buyer behavior and the fact that markets shift over time. My previous article on lead scoring below.
AI could change the equation. Instead of static point allocations, machine learning models ingest thousands of closed-won and closed-lost records and identify patterns too subtle for humans to see. Perhaps it’s not the company size that matters most but the combination of hiring velocity, use of a particular technology, and whether the buyer has engaged with a webinar. An AI model can continuously learn and adapt, reprioritizing leads dynamically as conditions evolve. LLMs do not do this today, but I could see workflow automations tools combined with databases to run recursive scoring models which dynamically change weights and biases.
Tools such as Salesforce Einstein, HubSpot Predictive Lead Scoring, MadKudu, and 6sense are leading the way, while platforms like Clay give RevOps leaders flexibility to enrich data and experiment with scoring logic before operationalizing in the CRM.
The practical path forward is incremental. Many teams begin by keeping their existing rule-based model for transparency but layering AI-based prioritization on top. Over time, as trust builds, AI scoring becomes the system of record.
Ideal Customer Profile (ICP) Identification
Defining the ICP has always been more art than science. GTM teams look at their top accounts, pull out common attributes, and declare those to be their ICP. While this approach offers a starting point, it is inherently backward-looking and vulnerable to bias.
AI allows RevOps to move from anecdote to evidence. By training on historical wins and losses, churn events, and expansions, models can identify which features best predict success. The outcome might surprise teams. Instead of the broad definition “mid-market software companies in North America”, AI may surface that the highest fit customers are “Series B SaaS firms between $20–100M ARR, using Snowflake and hiring data engineers.”
The advantage isn’t only precision. AI also enables micro-ICP segmentation. Through clustering algorithms or embedding models, you can uncover subgroups within your ICP, each with distinct buying triggers and needs. Again, LLMs fall short here. You’re going to need to unlock machine learning here.
Practical deployment requires three steps: enrich account and opportunity data, train a predictive model (often logistic regression is sufficient), and validate results against win rates and average contract value. Tools like ZoomInfo GTM Studio, 6sense, and MadKudu offer packaged approaches, while Snowflake Cortex or BigQuery ML provide a path for custom models.
Personalization at Scale
Buyers have grown weary of generic outreach. Spam cannons think that reference fields are enough to break through the noise. A templated email or a one-size-fits-all landing page no longer captures attention. Yet true personalization has historically been limited by the human capacity to tailor messages.
AI could enable one-to-one personalization at scale. Large language models (LLMs) can take account context (industry, role, funding stage) and generate outreach that reads as if it were written for that specific buyer. Marketing platforms can dynamically adjust website content or ad creative in real time. Now to be fair I would only do this with industries with very large TAMs such as B2C plays or selling into SMBs. Companies with less than 1,000 accounts in their TAM could suffer greatly if you poorly execute this type of play.
This goes beyond surface-level mail merge fields. An AI-generated message can reference industry trends, match tone to persona, and highlight relevant product use cases. In marketing, tools like Mutiny optimize website experiences, while Clay and Lavender assist SDRs in generating bespoke outbound emails.
The key is control. AI should not be left to write unchecked. A good approach is to create templates with guardrails, letting the model fill in contextual details while humans maintain brand and compliance consistency. The result could be higher engagement and conversion rates without the burden of manual tailoring.
Forecasting and Pipeline Health
Forecasting is where the credibility of a revenue organization is tested. Executives need to know whether the team will hit the number, yet traditional forecasting remains plagued by bias and inconsistency. Spreadsheets, stage-based multipliers, and rep judgment dominate the process.
AI-driven forecasting offers a more objective lens. By analyzing historical deal progression, activity signals, and rep behaviors, models can predict the likelihood of each deal closing within the quarter. They can flag risk when a deal looks inflated or when pipeline lacks coverage.
Platforms like Clari, BoostUp, and Salesforce Einstein Forecasting integrate these capabilities directly into CRM workflows. The most effective use cases pair human judgment with AI predictions. A forecast call may start with the AI model’s projection, followed by rep insights to explain outliers.
Implementation begins with data hygiene. Without accurate opportunity histories and stage progression, AI has little to work with. Once data quality is assured, a phased rollout builds trust: start by using AI as a forecast overlay, then allow it to influence quotas and resourcing decisions.
Pricing and Packaging Optimization
Pricing has often been managed as an art form, relying on surveys, competitive analysis, and finance-driven assumptions. But static pricing can leave revenue on the table or undercut margins.
AI introduces the ability to model price elasticity and package optimization. By examining historical transactions, discount patterns, and win/loss outcomes, models can suggest optimal price points and bundles. Instead of relying on anecdotal rep feedback, you have data-backed insights into what customers are willing to pay.
Vendors like Price Intelligently (ProfitWell) provide packaged pricing analytics, while more advanced teams can build their own models in Snowflake or Databricks. The implementation path is to start with discount guardrails, simple recommendations for reps on acceptable ranges, before moving to dynamic pricing experiments.
The long-term vision is adaptive pricing: systems that recommend different offers based on buyer characteristics and likelihood to convert. While still emerging, this represents one of the most strategic applications of AI in GTM.
Conversational Intelligence
Call coaching has traditionally been resource intensive. Sales managers could only listen to a fraction of rep calls, relying on notes or anecdotal observations. Insights were shallow and inconsistent.
Conversational intelligence tools powered by AI now record, transcribe, and analyze every customer interaction. Natural language processing identifies patterns such as objection handling, competitor mentions, and time allocation. Ultimately surfacing actionable coaching insights.
Platforms like Gong, Chorus, and Avoma not only review calls but also provide real-time guidance. A rep may receive an onscreen suggestion when a competitor is mentioned or when talk time becomes imbalanced. I’m not a huge fan of guidance during the call but I could see some folks limiting the guidance so as not to appear as selling through a teleprompter.
The impact is twofold: managers gain visibility across the entire sales floor, and reps receive continuous feedback rather than sporadic coaching. Over time, conversational data also becomes an input for ICP refinement, product messaging, and competitive strategy.
Marketing Campaign Optimization
Marketing has always relied on experimentation, but human-driven A/B testing is slow. Adjusting budget allocation across channels often lags weeks behind performance data.
AI brings real-time optimization. Using multi armed bandit algorithms and machine learning, campaign platforms can automatically adjust spend and creative to maximize pipeline or revenue outcomes. Google Ads’ Smart Bidding is a mainstream example, but B2B-focused platforms like Metadata.io and Adobe Sensei extend these capabilities.
The practical guide is straightforward: define success metrics (not just clicks or leads but qualified pipeline), feed campaign data into the AI system, and allow it to reallocate budget dynamically. Marketing’s role shifts from manual adjustment to monitoring performance and refining strategy.
The result is more efficient spend and faster learning cycles. Instead of quarterly campaign reviews, optimization becomes continuous.
Customer Success and Retention
Churn kills SaaS businesses. Traditionally, CSMs rely on intuition, periodic account reviews, or basic health scores driven by product logins. This often leads to surprises: a seemingly healthy account churns while an at-risk customer is ignored.
AI based retention models provide a more systematic approach. By analyzing product usage telemetry, support interactions, financial health, and sentiment signals, AI can predict churn risk with far greater accuracy. Gainsight Horizon AI, Totango, and ChurnZero all embed such models, while advanced teams may develop custom classifiers in their data warehouse.
The practical guide is to start small: enrich CS platforms with AI-predicted health scores, test their accuracy against actual churn, and then use them to prioritize outreach. Over time, AI can also suggest specific playbooks for CSMs whether to escalate, upsell, or offer training.
Territory and Account Segmentation
Companies split accounts by geography, alphabet, or employee bands, often leading to imbalances in opportunity. AI can optimize segmentation by clustering accounts based on potential value, density, and similarity. The model balances workload and opportunity across reps, reducing disputes and improving coverage. Tools like Xactly, Fullcast, and Anaplan increasingly integrate AI into their carving workflows. I would also check out what my sponsor TigerEye has to offer.
Implementation involves three steps: enrich accounts with potential indicators (fit, intent, TAM), apply clustering algorithms to create balanced groupings, and validate with sales leadership. The output is a territory plan that reflects true market opportunity rather than arbitrary boundaries.
This approach not only improves fairness but also maximizes revenue capture by aligning rep focus with market potential.
Workflow Automation with AI Agents
Finally, AI agents represent the frontier of GTM automation. Traditional workflows depend on reps manually entering data, sending follow-up emails, or routing leads. Even with automation platforms like Zapier, processes remain largely rules-based.
AI agents, by contrast, can perform complex tasks autonomously. An agentcan enrich a lead, generate a personalized outreach email, log the interaction in Salesforce, and schedule a follow-up without human intervention. Check out the Zapier agent capability by the way. Here’s a video of me using it to create an EA for my to-dos.
The key is incremental adoption. Start with low risk automations such as call note generation or meeting scheduling. Monitor for accuracy and gradually expand the scope. The long-term potential is a world where GTM professionals focus on strategy and relationship building while AI handles the operational load.
Building a Roadmap for AI in GTM
For RevOps leaders, the question is not whether to deploy AI but where to begin. The ten use cases described here can be organized into phases:
Quick Wins (0–3 months): Lead scoring, personalization, conversational intelligence, campaign optimization.
Medium Plays (3–9 months): Forecasting, retention prediction, territory optimization.
Transformational (9–18 months): Dynamic ICP identification, pricing optimization, workflow agents.
The sequencing matters. Early wins build credibility and trust in AI outputs, paving the way for deeper transformations that require stronger data foundations.
No silver bullets
AI is not a silver bullet. Poor data hygiene, lack of process discipline, or unclear strategy cannot be solved by machine learning models. Yet when deployed thoughtfully, AI amplifies GTM effectiveness. It allows teams to focus human effort where it matters most such as building relationships, crafting strategy, and closing deals.
Good luck out there!
Go forth and operate