How AI can Improve Prospect Research

I asked my team a simple question last week: "What's still eating up your time every day?" The room got quiet. Then Sarah, one of our top AEs, spoke up. "Meeting prep. I spend 20-30 minutes before every call just trying to figure out who I'm talking to, what their company does, and what questions I should ask. Yes, we have a few tools that give me some fluff about the people I am talking to, but I still need context specific to us, our customer, and how I can add value when I step into the meeting...." She pulled up her screen and walked me through her process: - Check LinkedIn profiles for each attendee Research the company website - Look up recent news or funding Scan their tech stack for competitors - Draft discovery questions Block time for follow-up tasks "This is for ONE meeting," she said. "I have six today." Five minutes into her walkthrough, I stopped her. "Five minutes is five minutes too long. We're fixing this today." That afternoon, I built what my team now calls "the prompt to rule them all." Here's what our Daily Sales Agenda AI agent does automatically every morning: 1. Scans each rep's calendar for the day 2. Researches every non-company attendee 3. Pulls prospect insights and company context 4. Maps strategic connections to our solution 5. Generates tailored discovery questions for each meeting 6. Flags if competitors appear in their tech stack 7. Recommends optimal time blocks for deal management Schedules post-meeting follow-up windows 8. Creates a daily deal hygiene checklist 9. Suggests new prospect research windows The agent delivers this as a personalized briefing document before their first coffee. Sarah tested it the next day. Her reaction? "I feel like I have a research team working for me overnight." But I didn't stop there. Version 2.0 is already in development. It will pull data from Salesforce, analyze recent Gong calls, cross-reference email engagement, and even suggest which deals need attention based on last activity. But here's the real insight: This isn't about the tool I built. It's about changing how we think about sales operations. For years, we've accepted that "good sales reps do their homework." We've normalized 2-3 hours of daily admin work as "part of the job." That's insane. Your reps shouldn't be spending 30% of their day on tasks a computer can do in 30 seconds. The old growth equation was: more people = more revenue. The new equation: remove friction = sales superheroes. Every minute your team spends on manual research, data entry, or administrative tasks is a minute they're not solving problems for prospects. We don't need to buy every shiny new sales tool. We can build targeted solutions for our specific workflows. The question isn't "Can we afford to invest in automation?" The question is "Can we afford NOT to?"

Every B2B sales tool today: "We're powered by AI!" Ughh. Are you? I talk to dozens of founders every month. Most have been burned by buying "AI sales tech" That was just a basic GPT wrapper. With good marketing. 🙈 ❌ THE PROBLEM TODAY: So many "AI" sales vendors today demo well. But their actually product? It's not really AI. It's an API call. To ChatGPT... The red flags you should look for: 🚩 Template based responses 🚩 Minimal error checking 🚩 Basic API calls We've tested so many of these tools ourselves. And guess what? They failed to verify basic company data. They misunderstood qualification tasks. They sent emails with wrong context. That's because they're treating "AI" like... A fancy version of mail merge. SO... What should you look for? 2️⃣ What AI Sales infrastructure SHOULD look like Your AI sales stack needs these core components: Multi-Source Verification: - Cross-reference data across 3+ sources - Source tracking for every data point - Real-time accuracy validation - Automated fact-checking Context Management: - Industry-specific knowledge bases - Historical interaction memory - Company relationship graphs NOW... Here's where I'd focus your AI sales agents first 👇 Start with research heavy tasks. Things like: Lead Research: - Identifying expansion opportunities  - Analyzing technographic data - Mapping org structures - Finding trigger events Prospect Qualification: - Technology stack analysis - Company size verification - Recent company changes - Budget signals BEFORE YOU BUY... Look at THESE metrics 📈 "What are your accuracy rates?" Ask them for: - Research verification percentage - Data freshness metrics - Error correction stats - Learning curve data "What are your performance metrics?" - Error reduction over time - Processing speed at scale - Consistency across tasks - Adaptation to feedback THEN... Here's how I'd do a roll out 1️⃣ MONTH ONE - Audit manual research tasks - Document qualification criteria - Map current research workflow - Identify verification sources 2️⃣ MONTH TWO - Test AI on small lead segment - Measure accuracy vs humans - Document error patterns - Refine verification process 3️⃣ MONTH THREE - Scale successful processes - Build feedback loops - Train team on collaboration - Measure productivity gains -- P.S. Always ask AI vendors: "Show me your error rate metrics" If they can't, you know what you're dealing with. Have more questions? Hit me up in the comments or DM me!

AI isn’t the Future of Sales — it’s the New Competitive Battlefield. The playbook I used to finish as the #1 Enterprise AE at Salesforce just became MUCH faster and easier to execute using AI. Here are my top use cases: 1. Account Research: As an Enterprise AE, it's essential to understand what your customers do, how they make money, and learn about their top goals and priorities so you can align your solutions to their key initiatives. This has been, and still is, my most effective strategy to book meetings with Senior Executives. In the past I would have to manually find, read, and extract account details from various sources across the web, set up google alerts, read news reports, and comb through long financial documents like 10K's and proxy statements (DEF 14A) to find this information. This often took me hours upon hours, and was very tedious work. Now with the right prompts, I can use AI to quickly search for the top priorities and initiatives of my prospects, learn more about their business model and company structure, and import the content of key financial documents to extract data points that will help me develop a tailored, impactful POV. 2. Individual Research In additional to account research, I would spend extensive time researching the priorities of key executives. The gold was often buried in their keynote speeches, podcast interviews, and articles featuring their work. Now I can easily import the transcripts of these interviews to find key discussion points which I can directly support with my solutions. 3. POV development One of the key strategies I teach is linkage, which is the direct connection between their priorities and your solutions. Developing a strong POV which hits the mark with execs is extremely challenging and this "deep work" separates good sellers from elite sellers. With AI, you can marry the key priorities of a company with your solutions, and develop a compelling POV quickly. It's usually necessary to iterate a few times, so that the POV is very specific, addresses existing pain, and has a strong financial impact. As long as you know what a good POV looks like, AI can get you 75% of the way there and you can refine the rest 4. Executive Messaging Writing effective e-mails outlining your POV can often take a long time, especially if you're a perfectionist or have imposter syndrome. AI can help develop the 75% baseline of your message, and you can use your knowledge and skills to refine the other 25% so it sounds like you. These are a just a few of my favorites use cases for AI. If you want to learn how to use AI to drive massive sales growth in 2025, check out the AI-Led Growth conference next week. Register below: https://lnkd.in/gZCx8Qz9

Early in my career, I was in Sales. I learned an essential insight: If you care deeply about your customers' career versus your career, you will be successful. To do that, you need to bring insights, tailor to your customers' business and deeply understand their priorities. The question now is: how can AI possibly play a role in something so fundamentally human?  It might sound counterintuitive, but AI doesn’t replace the human touch—it strengthens it. I have been talking to customers about how they are driving results with AI in sales. Here is what one customer did over the last year. It started with the CEO challenging the team to leverage AI in sales. He wanted to instill a culture of an AI first organization. Then he picked one leader as the DRI (directly responsible individual) to drive the AI initiatives. We have found that both these are crucial for AI initiatives - needs to come from a leader and needs a clear driver. The first step was helping BDRs research faster. They were able to bring together company data, transcripts, conversations and help reps research prospects. The result: they cut prep time by 50% in a few months. Next up, they added a ton of data sources within HubSpot and with the help of AI, they were able to generate draft emails - this improved the follow-up time by 20% and helped them move through discovery much faster. Now, they are using AI to figure out the next best actions after calls. Super cool to see results in a few months! Our takeaway is clear: Let AI handle the groundwork, freeing reps to excel at what they do best—building meaningful relationships, earning trust, and forging deeper connections. This is the true path to sales success. What are your best AI use cases in Sales? Where are you seeing value?