AI identifies gaps in the customer journey and drastically reduces time spent on repetitive administrative tasks. When used well, it supports scalable growth without sacrificing the crucial human connection that drives strong customer relationships. Find out more about how AI is helping RevOps teams work smarter.
You already know that delivering a consistent and effective customer experience is essential for driving revenue. That understanding may have led you to invest in new tools and systems. By now, you're likely using some form of AI or planning to integrate it soon.
And yet, growth might still be slower than you'd like.
Perhaps your CRM is cluttered with outdated or duplicate entries. Maybe your sales team rarely uses the sophisticated tools you've provided. Content output might be up, but engagement remains stubbornly low. You have access to a wealth of customer data, but it's scattered across disparate platforms, making it difficult to extract actionable insights. Your teams are full of capable people, but their efforts aren't consistently translating into predictable business performance.
This is precisely where Revenue Operations (RevOps) plays a key role. RevOps brings essential structure and alignment to the often scattered efforts across sales, marketing, and customer success. It's about getting everyone to work toward the same unified goals, fostering collaboration, and making it easier to clearly see what actually drives revenue, so you can do more of it.
AI significantly enhances RevOps capabilities by improving data quality, pinpointing friction in the customer journey, and automating repetitive tasks. For example, AI can automatically:
- Identify where leads drop off between marketing campaigns and sales follow-ups.
- Flag deals that are stalling in the sales pipeline by analyzing engagement history and predicted close dates.
- Detect when slow customer support responses threaten valuable renewals.
Beyond identification, AI handles much of the heavy lifting in administration, data analysis, and forecasting. This frees your team to focus on strategic work and the high-value interactions only humans can provide.
AI in RevOps: Concrete Use Cases
From intelligent lead scoring and precise forecasting to instant call summaries and always-on chatbot support, the applications of AI in RevOps are rapidly expanding.
However, implementing AI in RevOps isn't about replacing your team or ripping out your existing tech stack. It's about solving real operational problems with smarter, more adaptive workflows. Successful adopters use AI to eliminate persistent bottlenecks. Which bottlenecks are slowing your team down?
How to start with AI
If you’re not sure where to begin, start by listening to your team. What tasks drain their time but add little value? What processes feel manual, repetitive, or just plain annoying?
Still enriching lead data by hand before outreach? Use AI tools that automatically pull in company size, industry, tech stack, or recent news directly into your CRM.
Still updating spreadsheets every quarter to prep for Quarterly Business Reviews (QBRs)? These meetings are meant to align on revenue performance, pipeline health, and strategic goals. They should not burn hours on manual data collection. Let AI handle the consolidation and visualization so your team can focus on the insights.
Still copy-pasting webinar or event attendee lists into your CRM? Automate it with an integration that tags contacts, triggers follow-ups, and scores leads based on engagement.
Start small, solve a real pain, and build trust in the process. The best AI use cases are the ones that quietly remove friction and give people their time back.
A simple AI workflow that auto-classifies incoming leads, pulls context from LinkedIn, or summarizes past conversations can free up hours every week. You don’t need to build a custom model to get value. Many CRM and Marketing Automation (MA) systems already have robust AI functionality built-in:
- In Marketo, smart audience segmentation uses AI to group contacts based on behavior, intent signals, and demographic data, enabling hyper-targeted campaigns.
- In Salesforce, pipeline inspection agents surface deals at risk by analyzing engagement history, close dates, and activity patterns, alerting sales managers before a deal quietly slips through the cracks.
- In HubSpot, AI optimizes campaigns and workflows, suggesting content improvements, email send times, and personalized customer journeys.
Just to name a few examples.
How our clients use AI
The specific AI use cases differ for each organization. We always analyze the unique customer journey and sales process before suggesting AI implementations. Here’s how some of our clients are leveraging AI:
- Some clients use AI to analyze call transcripts from sales and support interactions, highlighting common objections, identifying product feature requests, or summarizing key themes from unstructured customer feedback. This provides invaluable insights for product development, sales training, and marketing messaging.
- Others employ AI for predictive analytics to identify customers at risk of churn based on usage patterns, support ticket frequency, and engagement scores. This allows customer success teams to proactively intervene and retain valuable accounts.
- Another common application is using AI to optimize ad spend and campaign performance. AI algorithms analyze historical campaign data, audience behavior, and conversion rates to automatically adjust bidding strategies and content targeting for maximum ROI.
And of course, our clients leverage the obvious use case for AI in RevOps – using standard or dedicated LLMs to personalize content based on persona, buying stage, account, language etc. There are of course different levels of how our clients leverage this, all the way on the more advanced that is using our sister company KontentPlus with direct integration to your Marketing Automation platform, to having a decent prompt to customize individual emails based on an original human produced content.
Here is prompt you can test for yourself:
You are a strategic B2B marketer and copywriter with experience in complex buying journeys and revenue operations. Your task is to generate multiple tailored versions of the email provided below. Each version should reflect the specified persona, buying stage, and relationship type, while maintaining the same overall message and call to action.
Please generate versions of the email based on the following parameters:
- Original email: [Insert original email here]
- Personas to target: [Insert personas]
- Buying stages: [Insert buying stages]
- Relationship to us: [Insert relationship types]
- Tone of voice: [Insert tone, or write "match original"]
- Language or localization (optional): [Insert language or regional preference]
Instructions:
For each combination of persona, buying stage, and relationship type:
- Do not change the core message or CTA
- Maintain the original structure and intent
- Adjust tone, language, and benefit framing to suit the audience
- Ensure each version is natural, clear, and relevant
- Label each version clearly with persona, stage, and relationship
Real-World Use Case: Categorize Form Submissions with AI + SyncCloud
Here’s a real-world use case that you can set up this week with no development time required:
The Problem: Your website has a free-text contact form. Visitors write anything from "I need help with integrations" to "I’m evaluating HubSpot" or “Do you need investment advice, reach out to spam@crapinvesments.com”. You want to understand their intent, categorize it, and route it properly to the right team or person. Manual sorting is slow and takes up valuable sales time.
Different solutions:
1. Code a custom service that you host on a cloud platform.
a. Setup a service that can receive incoming webhooks.
b. Connect it to an LLM of your choice, if you use OpenAI I would recommend setting up an assistant where you can give custom instructions on how to interpret the form message and what information should be sent back.
c. After the form message is interpreted, and categorized by the LLM, you need to send the information back to the platform using a standard API call.
d. In the MAP, create an outbound webhook to your new service. Include the “Form Message” and the Id of the record, so that you can send it back to the platform.
2. For Marketo specifically you can use Marketo Self Service Flow Steps.
a. Find a provider that allows you to interpret unstructured data, and connect that one. You can now get a free trial for 20 interpretations using Exelement Unstructured Data Interpreter.
3. Connect through an integration platforms.
a. If you want to be completely hands-off you can look at Fully Managed Integration platforms like SyncCloud
b. If you want to build a bit yourself, you can look at Zapier and others.
The Result: You gain more structured data instantly. Leads are routed with greater accuracy and speed. Your team performs less manual work, and hot leads get the attention they need faster, improving conversion rates.
Do This Before You Implement AI
It’s tempting to plug in AI and hope for the best. But if you automate a broken process, you’re just moving faster in the wrong direction. Before you roll out any AI workflows, take time to map your actual customer journey, your current sales process, and your existing tools.
Understanding these foundational elements ensures AI enhances, rather than complicates, your operations. It might be helpful to enlist a RevOps strategist to help with this critical analysis.
