--- title: AI Prospect Research: A Workflow That Actually Saves Time description: How to build a prospect research workflow with AI that cuts hours of prep work into minutes. Real techniques, not theory. date: February 5, 2026 author: Robert Soares category: ai-for-sales --- Researching prospects eats hours. You know this already. LinkedIn for background. Company website for news. Crunchbase for funding. Google for mentions. Twitter for personality clues. Each source takes five to ten minutes, and you do this dance before every single call, stacking up to six hours weekly according to [Outreach's Prospecting 2025 report](https://www.outreach.io/resources/blog/prospecting-2025) surveying 500 revenue professionals. That time adds up to a full workday lost each week just figuring out who you're talking to. AI changes the math. Not by doing sloppy work faster, but by consolidating scattered research into a single conversation that synthesizes what actually matters. ## The Research Problem Nobody Talks About Most sales advice treats research like a checkbox. Did you look them up? Good. Now go sell. But research quality varies wildly. A quick LinkedIn scan reveals title and tenure. Deep research reveals that the VP you're calling just posted about struggling with attribution, came from a competitor who used your category of tool, and led a team twice the size at their last company. One of these backgrounds sets up a real conversation. The other gets you a polite brush-off. The problem is that deep research takes forever. As one marketer [noted after testing AI research tools](https://martech.org/making-ai-deep-research-work-for-strategic-marketing-tasks/): "That kind of synthesis would have taken me two full days. I got it in less than 90 minutes, including citations and source links." Sales reps don't have two days per prospect. They barely have twenty minutes. So they default to the quick scan and hope for the best. AI collapses the time gap between shallow and deep. You can now do the thorough research that used to be impractical. ## What Good Research Actually Looks Like Before diving into prompts, let's be clear about what we're trying to find. Company-level signals that matter include funding stage and runway, whether they're expanding or consolidating, their tech stack choices, competitive pressures they face, and any recent leadership changes. These shape what they'll care about and how urgently they'll care about it. Person-level signals include how long they've been in role, their career trajectory and where they came from, what they publicly complain about or celebrate, their communication style based on posts and interviews, and their decision-making authority. The goal isn't to know everything. It's to know the handful of things that make your conversation feel relevant rather than random. ## The Single-Prompt Research Request You can run separate prompts for company and person research, or you can consolidate into one request that covers both. Consolidation works better for most reps because it's faster and forces you to prioritize what actually matters. Here's an example prompt that works with any AI tool that can browse the web: ``` Research [Company Name] and [Person Name] for a sales conversation about [your category]. For the company, tell me: - What they do in plain terms - Recent news from the last 6 months - Size, funding, and growth signals - Their likely pain points related to [your category] For the person, tell me: - Their role and tenure - Career background - Any public statements, posts, or interviews - What they seem to care about professionally Based on all of this: - What angle would make this conversation relevant to them? - What objections might they raise? - What questions should I ask to learn more? ``` This single request replaces the six-tab research scramble. One developer marketing lead [described the shift](https://www.strategicnerds.com/blog/automate-pmm-with-claude): "Now I generate in minutes what used to take hours." ## Verifying What You Get AI research has a reliability problem you need to manage. The outputs sound confident even when they're wrong. Dates get confused. Job titles get outdated. News stories get misattributed. If you walk into a call citing something the AI hallucinated, you've done worse than no research at all. Verification takes an extra two minutes but saves you from embarrassment. For critical facts like funding amounts, leadership names, or recent announcements, check the company's own website or press page. LinkedIn remains the source of truth for titles and tenure. Recent news claims deserve a quick Google search to confirm they actually happened. A [Hacker News user testing AI research tools](https://news.ycombinator.com/item?id=43061827) observed that "Deep Research seems to be reading a bunch of arXiv papers for me, combining the results and then giving me the references. Pretty incredible." But another commenter in the same thread cautioned that "It sounds all authoritative and the structure is good. It all sounds and feels substantial on the surface but the content is really poor." Both experiences are common. The variance means you need to spot-check before trusting. ## Scaling Research With Templates Once you've used AI research a few times, patterns emerge. You start asking for the same information in similar ways. This is the moment to build a template. Save your best research prompt somewhere accessible. Copy, paste, fill in the blanks. Instead of writing a new prompt each time, you're running a repeatable process that takes thirty seconds to initiate. Templates also help with batching. If you have five calls tomorrow, research all five in one sitting. The AI doesn't get tired. You can fire off requests back to back, then review the outputs and take notes while they're fresh. One sales professional [documented saving over four hours per week](https://www.saleslabs.io/one-week-with-chatgpt-how-i-saved-over-4-hours-of-prospect-research/) using exactly this approach, cutting per-prospect research from ten minutes down to about one minute with a structured prompting system. ## Matching Depth to Opportunity Not every prospect deserves the same research investment. Cold outreach lists need the bare minimum. Company overview and one personalization hook. Maybe two minutes of effort if you're being thorough. The math doesn't support spending fifteen minutes on someone who might not respond. Qualified leads who've expressed interest deserve more. Full company context, person profile, competitive landscape. Five to seven minutes here makes sense because the conversion probability justifies the investment. Major opportunities should get everything you can find. Multiple stakeholder profiles, org chart research, detailed competitive analysis, historical relationship data if you have any. Fifteen minutes on a six-figure deal is a rounding error. [Outreach's research](https://www.outreach.io/resources/blog/prospecting-2025) found that 45% of sales teams now use AI specifically for account research, and teams who do it well report significant productivity gains. But that productivity comes from knowing when to go deep and when to stay shallow. ## What Research Teaches You to Ask Good research doesn't give you a script. It gives you better questions. You learn that the company just raised a Series B, which means growth pressure and probably new hiring. You can ask how they're thinking about scaling their team. You notice the VP came from a much larger company. You can ask how the transition to a smaller environment has changed their approach. You see they posted about attribution challenges. You can ask whether that's still a priority or if something else has taken over. These questions demonstrate that you paid attention. They also surface real information about where the account stands and what they actually need. The research investment pays off not in what you tell them, but in what you learn from them. ## Building Research Into Your Day The worst time to research is five minutes before the call. You're rushed, you skim, you miss things. The best time is the evening before or first thing in the morning. A focused thirty-minute block can prep all your day's calls with time left to take notes on each one. Post-call research also matters. After a good conversation, dig deeper on the account while details are fresh. Look for the additional stakeholders who might be involved. Research the objections they raised. This enrichment makes your follow-up more substantial than "Great talking to you, here's some materials." [HubSpot's research](https://blog.hubspot.com/sales/ai-time-savers-in-sales) found that 55% of sales professionals now use AI specifically to support customer research, and those who do report being able to spend more time on actual selling. The efficiency gain doesn't just save time. It shifts time from prep work to relationship building. ## When Research Fails Sometimes AI research comes up empty. The company is too new or too small for much public information. The person has minimal online presence. The industry is niche enough that the AI doesn't have much training data on it. When this happens, you have options. Switch models. Different AI tools have different training data and web access capabilities. What one misses, another might find. Adjust your prompt. Ask for specific sources the AI might not have checked. Point it toward industry publications, conference talks, or podcast appearances. Accept the limitation. Some prospects simply won't have much public information. Your research in those cases focuses on the company basics and the questions you'll ask to fill in the gaps. The goal isn't perfect information. It's better information than you had before, obtained faster than it would have taken manually. ## Research and Personalization Research without action is just trivia collection. The point is to use what you learn. One relevant reference to something they actually care about beats ten generic observations about their company. Pick your best hook from the research. One thing that makes the conversation feel relevant. Lead with that, then listen. The research gave you an opening. The conversation reveals whether you read the situation right. Over-personalization is also a risk. Mentioning seven specific things from their LinkedIn makes you seem like a stalker, not a thorough professional. One or two well-chosen references signal that you did your homework without making it weird. ## The Workflow That Sticks The difference between reps who benefit from AI research and those who don't usually comes down to consistency. Occasional research doesn't build the muscle. You forget which prompts worked, you skip it when you're busy, and you never develop the speed that makes it feel worthwhile. Daily research as a built-in habit changes the game. You get faster at prompting. You develop intuition for which signals matter. You start recognizing patterns across accounts. [Outreach's survey](https://www.outreach.io/resources/blog/prospecting-2025) found that among SDRs using AI tools, 100% reported time savings, with 38% saving four to seven hours weekly. Those aren't people who tried it once. They're people who made it part of their routine. Start with one prospect today. Run through the research workflow. See what you learn. If it's useful, do it again tomorrow. The efficiency compounds as you build the habit. What does your current research process look like, and where does it break down? --- *DatBot gives you access to multiple AI models in one interface. Research with Claude, cross-check with GPT, all without switching tools. Try it for your next prospect research session.*