Buyers have gotten very good at researching you before you ever get a chance to research them. By the time most sellers make first contact, the buying group has already shortlisted vendors, consumed competitor content, and formed opinions that are harder to change than most reps realize.
The only real response is to close the information gap. Sellers who show up knowing what an account cares about, who’s involved, and what’s already happened in the buying journey perform better.
How sales reps are currently using AI
AI sales research is already happening on your team. The real question is whether it’s surfacing the signals that lead to pipeline.
Your reps are pasting prospect bios into ChatGPT, summarizing 10-Ks, and pulling icebreakers from LinkedIn profiles. The instinct is right; research wins deals, and anything that speeds it up is worth trying.
But the tools they’re improvising with weren’t built for this job. Three problems tend to follow:
- Data leakage. Consumer AI tools weren’t designed with enterprise data handling in mind. Pasting personally identifiable information or sensitive account details into them creates real compliance exposure.
- Garbage in, garbage out. General-purpose AI is only as useful as what you feed it. Reps still have to hunt down inputs manually before the AI can format the output.
- Stale signals. Some orgs are standing up retrieval-augmented generation (RAG) systems, which let an AI pull answers from a connected data source rather than relying on its training alone. It’s promising, but a RAG grounded in stale CRM data or first-party signals still misses most of what’s happening in an account.
How to make AI research more useful
There’s a difference between information and intelligence. Information provides basic facts. Intelligence provides context. For instance, if an account just posted four RevOps roles, that means someone’s under pressure to fix their pipeline process.
Anothyer example: Knowing an account’s name, headcount, and tech stack is information. Knowing that three members of their buying group are actively researching your product category, that they just posted five job openings, and that two of them have been reading a competitor’s case studies is intelligence… that’s the kind of context that changes how a rep shows up to a conversation.
The useful ingredients of this context include:
- What’s happening at the company (news, hiring trends, funding)
- What the account is actively researching right now, and what they researched previously (intent signals)
- Who’s in the buying group and where they are in their buying journey
- How to appeal to key buying group members based on the details above
Most AI tools can help you organize and summarize the first thing on that list. The rest requires signal data that general-purpose AI can’t access.
Four ways context-rich AI changes the research equation
The shift shows up in four specific places for sellers.
Consolidated account research that draws from signals
The old way: Open a dozen tabs, stitch together a company overview from a website, a LinkedIn page, a press release from 18 months ago, and whatever your CRM happens to have. Then spend 20 minutes trying to assemble context before you’ve sent a single word.
The better way: Start with an account overview that already synthesizes what the account is doing right now, their buying stage, their intent activity, the contacts worth prioritizing, and the news worth referencing. For example, instead of spending your meeting prep time hunting for context, you walk in already knowing the account has three buying group members in active research mode and which pain points are indicated based on their research patterns.
Find the right contact, not just any contact
The old way: Export a list from Sales Navigator, take a guess at who owns the budget, and cold call whoever picks up.
The better way: AI-guided prospecting that identifies people based on actual engagement history, role relevance within the buying group, and verified contact information. For example, instead of reaching out to the title most likely to have authority, you reach out to the person who has already been engaging with content in your category with a message that reflects what they’ve been reading. That is the person most likely to respond. From there, you can begin broadening your reach to other buying team members.
Tailor outreach with precision
The old way: Run the same nurture sequence for everyone in the segment and wait to see who bites.
The better way: Outreach grounded in company and company-level activity. For example, instead of a generic “just checking in” sequence, your messaging reflects that the account’s VP of Sales just posted about pipeline forecasting challenges.
What AI with context looks like
RevvyAI is a GTM-GPT designed to help revenue teams act on what matters. It’s free to all 6sense users and available in the tools where users are already working, whether that’s:
- The 6sense platform
- Your web browser, via a Chrome extension
- Native integrations with Salesloft, Salesforce, Outreach
Access to CRM, MAP, intent data, and predictive analytics allows RevvyAI to quickly gather context about accounts and synthesize it into summaries and recommendations.
And unlike the consumer AI tools reps are currently improvising with, RevvyAI is backed by enterprise-grade security certifications from 6sense, so sellers can tap into a rich signal foundation without creating compliance exposure for their organization.
Conclusion
The best salespeople have always been great researchers. They read the room before they walk in. They know what’s changed, who cares, and what’s at stake. AI doesn’t replace this instinct. It changes what it costs to act on it.
Webinar: A Customer’s RevvyAI Story
Join us live on June 17th, 9 AM PDT / 12 PM EDT / 4 PM GMT.
