Why Your First AI Hire Should Be A Researcher, Not A Writer
Your first AI use case should be research, not writing, because research is the slower, invisible half of the work and its output doesn't need to sound like you. Hand off the fact-gathering, keep the voice yourself, and the writing usually gets faster on its own.
Most founders reach for an AI writing tool first. Writing feels like the most visible, most obviously time-consuming thing on their plate, so that's where the first subscription goes.
I think that's backwards. Research is usually the better first place to add leverage, and it's almost never where people start.
If you're deciding where AI earns its keep in your business, start with the legwork nobody sees, not the voice everybody reads.
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Research. Writing is the visible half of most content and communication work. Research is the invisible half: gathering facts, pulling numbers, checking competitor positioning, summarizing a long document.
That invisible half usually takes longer. It's also easier to hand off cleanly, because the output is information, not voice. Information doesn't need to sound like you.
Good first research jobs to hand to an AI assistant:
- Summarizing a long report, contract or transcript into the five points that matter.
- Pulling how three competitors describe their pricing and positioning.
- Gathering background on a prospect before a sales conversation.
- Collecting the data points behind a weekly or monthly update.
A founder who hands research to AI first frees up hours without risking the thing that needs to stay personal: the specific voice a reader trusts.
Why Starting With AI Writing Backfires
AI-drafted writing that goes out unedited tends to read generically. That's the opposite of what building a reputation requires. Start there, and you risk diluting the exact voice your content depends on before you've built the habit of reviewing output carefully.
Research has a safety net writing doesn't. A wrong fact can be checked. A bland paragraph that sounds like everyone else often sails through review, gets published, and slowly makes you forgettable.
Whatever you hand over, how you ask matters more than which tool you pick. The prompt matters more than the model, and that's doubly true for research, where you want the tool to show its sources so you can check them.
How Much Time Does AI Research Save? A Worked Example
Picture a hypothetical founder who writes a weekly market update. It takes three hours: ninety minutes gathering data and reading sources, ninety minutes writing it up.
They hand the research half to an AI assistant and spend their own time reviewing and correcting the facts it pulls.
| Step | Before | After |
|---|---|---|
| Research and reading | 90 minutes | 20 minutes, reviewing what AI pulled |
| Writing | 90 minutes | 50 minutes, no stopping mid-draft to look things up |
| Total | 180 minutes | 70 minutes |
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GET THE FREE CHECKLIST →The research cut is the obvious win: 90 minutes down to 20. The less obvious win is the writing. With the facts already on the page, the draft goes faster too. Total time falls from three hours to about seventy minutes, and the part that needs the founder's voice stays entirely in the founder's hands.
The Objection: "Writing Is The Bigger Time Sink, Shouldn't I Fix That First?"
The pushback: writing takes me longer than research, so why not automate the bigger cost first?
Often the writing takes longer because the research underneath it wasn't done well. You stop mid-sentence to find a number. You open six tabs to confirm a claim. You lose the thread and start the paragraph over.
Fix the research bottleneck first, and the writing frequently speeds up on its own. That's what happened in the example above: nobody touched the writing, and it still dropped by forty minutes.
Where To Go Once Research Is Handled
Once the research layer runs reliably, the natural next steps are tasks that are similarly information-heavy and light on voice: first-pass scheduling, routine replies, internal summaries. If you're not sure a task is ready, use the test in how to know when a task is ready to hand to an agent.
Save the tasks that carry your specific voice, the ones a reader would notice if they sounded like someone else, for last, if at all. When you do get there, train the tool on your voice deliberately rather than hoping it guesses.
What To Do This Week
Pick one recurring task where research eats more time than writing. Time it once the old way. Then hand the research half to an AI assistant, review what it brings back, and time it again.
Protect the voice. Automate the legwork underneath it.
Key takeaways
- Research is the slower, invisible half of most content work.
- Research output doesn't need your voice, so it hands off cleanly.
- Fix the research bottleneck and the writing often speeds up on its own.
Frequently asked questions
What is the best first task to give an AI tool in a small business?
Research: summarizing long documents, pulling competitor positioning, gathering prospect background or collecting data for a regular update. It saves real hours, it's easy to check, and the output is information rather than your voice, so nothing personal gets diluted.
Should I use AI to write my content?
Not first. Unedited AI writing tends to read generically, which erodes the voice readers trust. Build the review habit on research tasks, then bring AI into writing carefully, with your own examples and point of view in every request.
How do I check AI research for mistakes?
Ask the tool to show its sources, then spot-check the facts that matter most before you use them. Treat the output as a first pass from a fast assistant, not a finished answer, and correct anything you can't verify.
Mike Nathan
Founder & CEO, Impero Ventures · Founding Partner, Exit 156 Capital
20+ companies. $170M revenue. $55M raised. 3 exits. 2 VC funds. 1M+ YouTube subscribers.
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