15 to 30 minutes per company. That is the reality for SDRs who today research their prospects with ChatGPT, Gemini or Perplexity. Open the website, scan the LinkedIn profile, write a prompt, read the result, copy relevant data into the CRM. Next company. From the start.
It works. And for many teams it is an enormous step forward compared to pure Google research. AI tools compress hours into minutes, deliver usable summaries and help with phrasing outreach messages. No wonder that by now almost every sales team uses AI in some form for prospecting research.
But there is a problem that only becomes visible when you try to scale this approach. In this article we compare manual AI research with OpenProspect and show when each approach makes sense.
Stick with manual AI research if you...
- ...only need a few prospects per week and want to design each piece of research individually.
- ...have no budget for specialised tools and get by with a ChatGPT or Gemini subscription.
- ...handle various tasks with AI (writing emails, competitive analyses, market research) and sales research is only one use case among many.
- ...work as a founder or solo salesperson and want to keep full control over every piece of research.
- ...have a product that requires a lot of explanation and every prospect must be prepared individually.
Choose OpenProspect if you...
- ...regularly need 20, 50 or 100+ prospects per week and manual research becomes the bottleneck.
- ...sell in the DACH market and need structured German data sources that are not reachable via a web search.
- ...have a sales team that should work with uniform, pre-qualified prospect briefings.
- ...need timing signals that tell you why you should contact a company now.
- ...want to free your team from research work so it can focus on conversations and closing.
What is "DIY AI research"?
By this we mean the workflow that most SDRs and sales teams use today: a general AI tool like ChatGPT, Gemini, Perplexity or a comparable model is opened, a prompt is formulated, and the AI researches a company or a contact. The results are read, filtered and manually transferred into the CRM or a spreadsheet.
What the typical workflow looks like
- Create company list. Potential companies are collected from a database, LinkedIn Sales Navigator or a manually maintained list.
- Formulate AI prompt. "Analyse company XY. What do they do, how big are they, who are the decision-makers?"
- Read and assess the result. The answer is checked: does the company fit the ICP? Is the data plausible?
- Extract relevant data. Name, industry, size, contact, relevant details are manually transferred into a spreadsheet or a CRM.
- Prepare outreach. Another prompt generates a personalised email or LinkedIn message.
- Repeat. For every additional company the process starts from scratch.
The strengths of this approach
Low entry costs. ChatGPT Plus costs $20/month, Gemini Advanced likewise. Perplexity Pro is at $20/month. For a small team that is barely noticeable in the budget.
Maximum flexibility. You can ask the AI anything. "How does company X compare to Y in market position?" or "What challenges do mid-sized machine builders currently face?" That goes far beyond structured sales intelligence.
Usable immediately. No setup, no onboarding, no integration. Create an account, write a prompt, get a result. Everyone on the team can start tomorrow.
Good personalisation. If you take 15 minutes for each prospect, you get very individual results. The AI can interpret website text, analyse job postings and generate tailored outreach text from them.
Versatility. The same tool that researches prospects can also formulate objection handling, create competitive analyses or take over meeting preparation.
Where the approach reaches its limits
It does not scale. That is the central weakness and the reason we are writing this article. At 10 prospects per week, manual AI research is practical. At 50 it becomes a full-time job. At 100 it is impossible. Studies show that SDRs already spend 37% of their working time on research and only 30% on actual selling. More manual research aggravates this problem.
Every piece of research starts from zero. There is no learning effect between sessions. At company no. 50, the AI does not know what it found out at company no. 1. Your ICP, your offering, your acquisition strategy you have to explain anew every time or build into a prompt.
No pre-qualification. You decide yourself which companies you research. If you start with a list of 500 companies, you have to manually review all 500 to find the 50 relevant ones. The AI does not help you sort out the irrelevant ones beforehand.
Data quality is uncontrolled. General AI tools have no structured access to company databases. The results are based on web search and what is publicly available. Headcounts are estimated, revenue data derived from press releases, industry classifications interpreted from website text. That can be correct, but does not have to be.
No timing signals. The AI can tell you what a company does. It cannot systematically tell you why now is the right moment to reach out. Leadership changes, funding rounds or relevant job postings are only found if you ask specifically for them and if they happen to appear in the search results.
Inconsistent outputs. Every AI answer is structured differently. Sometimes you get five paragraphs, sometimes three bullet points. For a team that needs uniform prospect data in the CRM, that means additional work in processing.
The hidden costs. $20/month sounds cheap. But do the math on working time: an SDR who researches 20 minutes per prospect and handles 40 prospects per week spends over 13 hours weekly on research alone. At an SDR salary of €50,000, that is around €1,400 per month in pure research costs. For a team with three SDRs it is over €4,000 monthly that does not flow into conversations.
What is OpenProspect?
OpenProspect is a sales intelligence platform for the DACH market. The fundamental difference from manual AI research: you define your ideal customer profile once and then continuously receive ready-made, pre-qualified prospects, without researching yourself.
OpenProspect's strengths
No manual research effort. Instead of 20 minutes per company, your team spends zero minutes on research. The prospects arrive ready-made, with context, timing signals and a recommended action. The SDR’s work begins where it belongs: with reaching out.
The system knows your ICP and your offering. Unlike a general AI tool that starts from zero in every session, OpenProspect permanently understands who you are looking for, what you offer and how you acquire customers. This information feeds into every single piece of research.
Automatic pre-screening. From a base set of hundreds or thousands of companies, those that do not fit your profile are automatically filtered out. You only see the prospects that have passed a basic qualification.
20+ specialised DACH data sources. Commercial register, Bundesanzeiger, industry codes, Google business profiles, job postings and other local sources. This structured data delivers verified facts, not estimates.
Timing signals, automatically detected. Leadership changes, funding rounds, open positions in relevant departments, website relaunches, expansion plans. You learn not only who fits your ICP, but why now is the right moment.
Uniform prospect briefings. Every briefing has the same structure, the same depth, the same quality. Your team can focus on the content, not the format.
Where OpenProspect reaches its limits
Not usable for everything. OpenProspect is a specialised sales intelligence system. You cannot use it for competitive analyses, meeting preparation or formulating objection handling. For that, a general AI tool is the better choice.
No single research on demand. You cannot spontaneously say: "Research company XY for me." The system works systematically based on your ICP, not on demand.
DACH focus. For international markets you need additional tools.
Costs. OpenProspect costs more than a ChatGPT subscription. For a solo founder who needs five prospects per week, that may not be economical.
Core comparison: where the differences really lie
1. Working time: the underestimated cost factor
Most teams underestimate how much manual AI research really costs, because the calculation is reduced to the subscription price. But the actual costs are the working hours.
A concrete example: a sales team with three SDRs should contact 150 prospects per week. At 20 minutes of research per prospect, that is 50 hours of research work per week. That is more than a full-time position occupied exclusively with research, not with selling.
With OpenProspect this work disappears. The 50 hours become hours your team can use for conversations, follow-ups and closing.
2. Quality: single result vs. system
Viewed individually, a well-conducted ChatGPT search can deliver an excellent result. If you take 20 minutes, ask the right prompts and check the results carefully, you get a usable company profile.
The problem is consistency. At 150 prospects per week, quality inevitably drops. The tenth prompt of the day is no longer phrased as carefully as the first. Important follow-up questions are skipped because time is short. And the results have a different format every time, which makes further processing harder.
OpenProspect delivers the same depth of analysis for every prospect, regardless of whether it is the first or the hundredth.
3. Data: web search vs. structured sources
When ChatGPT or Perplexity researches a company, they use web search. That often delivers usable results, but with important limitations: the headcount may come from an outdated LinkedIn page. The revenue is derived from a two-year-old press article. The industry classification is based on what the website says.
OpenProspect accesses structured sources: the commercial register delivers the current management and legal form, the Bundesanzeiger delivers published annual financial statements, industry codes deliver the official classification. This data is not estimated, it is retrieved.
4. From research to action
Even the best AI research ends with a block of text that someone has to read, assess and translate into an action. The SDR has to decide: is the contact worthwhile? Via which channel? With which message?
OpenProspect delivers not just data, but context. The prospect briefing contains timing signals that explain why a company is approachable right now, and an assessment that shows how well the company fits the ICP. The path from research to outreach becomes significantly shorter.
Comparison table
Dimension | OpenProspect | Manual AI research |
|---|---|---|
Approach | Define ICP, receive ready-made prospects | Research company by company in the chat |
Time per prospect | None, automated | 15-30 minutes |
Pre-qualification | Automatic pre-screening | None, manual per company |
DACH data | Native: commercial register, industry codes, local sources | Web search, not structured |
Timing signals | Automatically detected and categorised | Only on targeted query, chance findings |
Scalability | 50, 100, 500 prospects with no extra effort | Linear: more prospects = more working time |
Output | Standardised prospect briefing | Individual text answers, varying format |
Costs | Platform fee | $20/month subscription + working time (the bigger item) |
Who is each the better choice for?
Manual AI research is the better choice if you operate as a founder or in a very small team, need few prospects per week and want to keep full control over each individual piece of research. If your sales process is so individual that every prospect needs a tailored analysis, and you do not (yet) have the budget for specialised tools. And if sales research is only one of many use cases for which you use AI.
OpenProspect is the better choice if your team is growing and you notice that manual research becomes the bottleneck. If you sell in the DACH market and need structured German data sources. If you want your SDRs to invest their time in conversations instead of research. And if you need a continuous, qualified flow of prospects, not occasional single analyses.
Conclusion
Manual AI research with ChatGPT, Gemini or Perplexity is, for many teams, the entry into AI-assisted prospecting, and for good reason. It is cheap, flexible and immediately usable. For founders and small teams it is often the right choice.
But it is an approach that has to grow with the team and at some point can no longer keep up. When 10 prospects per week become 50, when one SDR becomes three and when the research hours eat up the selling hours, you need a system instead of a tool.
OpenProspect is that system. Specialised in the DACH market, automated from market scan to briefing, with structured data sources and built-in pre-qualification. Not because DIY AI research is bad, but because it eventually becomes the most expensive way to find prospects. And precisely at the moment when it is needed most.