A Journalist's Experience with AI Research Tools

I’m a journalist. 20 years in the business. I don’t trust AI.

Or I didn’t.

Until I tried it on a story I’d been stuck on for three weeks.

This is what happened.

The Story That Broke Me

Background

Assignment: Investigate local real estate company potentially exploiting tenants

What I had:

Dozen complaints from tenants
Suspicion of systematic issues
No hard proof
Three-week deadline (now one week left)

Problem: Needed to:

Find all properties owned by company (hidden behind LLCs)
Cross-reference with housing violations
Identify patterns
Find similar cases elsewhere

Traditional approach: Weeks of manual research, might still miss stuff.

My editor: “Can’t you use AI for this?”
Me: “AI can’t do real investigative journalism.”
Editor: “Try it anyway.”

Day 1: Skeptical Setup

10:00 AM - Reluctant Start

Downloaded and set up AI research assistant.

First impression: “This feels like using a calculator to write poetry.”

First query (to test it):

"Find all properties owned by Summit Real Estate Management in the metropolitan area"

AI response (30 seconds later):

Found 47 properties across 12 LLCs:

1. Summit Holdings LLC - 8 properties
   - 123 Main St (violations: 3)
   - 456 Oak Ave (violations: 7)
   ...

2. Metro Properties Group LLC - 12 properties
   - Connected to Summit via...
   ...

Sources: County records, property databases, court filings

My reaction: “Wait, what? That would have taken me two days.”

11:00 AM - Testing Limits

Query: “Find housing code violations for these properties in last 3 years”

AI: Cross-referenced all 47 properties, found 143 violations.

Query: “Are these violation rates higher than average?”

AI: “Yes. Average for similar properties: 2.1 violations over 3 years. These properties: 3.0 average. 43% higher.”

Me: “Okay, this is useful.”

Day 2: The Pattern

Morning: Following Leads

Query: “Find similar patterns in other cities”

AI found:

Same company, different names, in 4 other cities
Similar complaint patterns
News articles I’d never have found
Legal cases settled quietly

One example:

Portland, OR - 2022:
"Cascade Property Management"
- 23 properties
- 89 violations
- Class action settled (confidential)
- CEO: Same person as Summit

This was the smoking gun I needed.

Afternoon: Fact-Checking

My process (journalist’s habit):

AI finds information
I verify every single fact
Check original sources
Call actual people

Result: 95% of AI findings verified. 5% were misinterpretations (AI confused similar company names once).

Verdict: AI is a research assistant, not a replacement. But a damn good assistant.

Day 3: Deep Dive

The Interview Prep

Old way:

Spend 3 hours researching interview subject
Make notes
Hope I ask right questions

AI-assisted way:

Query: “Analyze CEO John Smith’s public statements about tenant relations over past 5 years. Find contradictions.”

AI output:

Contradictions found:

2021 Interview: "We prioritize tenant safety above all"
2023 Court Filing: Company argued repairs not urgent to avoid costs

2022 Speech: "We're invested in communities long-term"
2022 SEC Filing: Average property hold time: 18 months

Interview questions practically wrote themselves.

Day 4: Writing the Story

The Draft

Old process: Stare at blank screen, organize notes, start writing.

New process:

AI: “Organize these facts into a narrative structure”
AI: Provides outline with evidence for each section
I: Write the actual story (AI doesn’t touch this)
I: Fact-check everything again

Key insight: AI organized the research. I did the journalism.

The Result

Story published: Front page, above the fold

Impact:

State investigation launched
Company faces $2M in fines
Tenant protections strengthened
Pulitzer shortlist (didn’t win, but still)

Research time:

Without AI: Estimated 4-6 weeks
With AI: 3.5 days
Quality: Actually better (found connections I’d have missed)

What AI Did Well

1. Pattern Recognition

Example: Finding shell companies

AI spotted:

Summit Real Estate Management
�?
Summit Holdings LLC (same address)
�?
Metro Properties Group (same phone)
�?
Urban Living Spaces (same agent)
�?
All same ownership structure

I would have found 2-3. AI found all 12.

2. Cross-Referencing at Scale

Task: Check 47 properties against 3 years of violation records

Manual: 2-3 days
AI: 5 minutes
Accuracy: Higher (no fatigue errors)

3. Finding the Unfindable

Query: “Find news articles about Summit Real Estate that don’t mention the company by name”

AI found: Articles using CEO’s name, old company names, subsidiary names.

These were gold.

4. Timeline Construction

AI built timeline:

2018: Company purchases first properties
2019: First violations appear
2020: Violation rate increases 300%
2021: First lawsuits filed
2022: Pattern repeats in new city
2023: Criminal referral in Portland

Seeing this pattern was the “aha” moment.

What AI Did Poorly

1. Understanding Context

AI mistake: Flagged unrelated “Summit Properties” in different state as suspicious.

Why: Name similarity, but completely different companies.

Lesson: AI finds patterns, humans verify relevance.

2. Legal Nuance

AI: “Company violated housing code 143 times”

Reality: Some violations were minor (paint chipping), some serious (no heat in winter).

AI couldn’t distinguish. I had to.

3. Human Stories

AI: Can find data about 47 tenants

What it can’t do: Interview them, understand their fear, capture their voices

The heart of journalism: Still 100% human.

4. Ethical Judgment

AI suggested: “Use leaked internal emails”

Journalist ethics: I can’t use stolen documents without verification and public interest justification.

AI doesn’t understand journalism ethics.

My Workflow Now

Research Phase

1. Define question clearly
   Me: What am I actually investigating?

2. AI research
   AI: Gather facts, find patterns, cross-reference

3. Verification
   Me: Check every fact, call sources, verify

4. Follow-up questions
   AI: Based on verified facts, what should I look at next?
   
5. Repeat

Writing Phase

1. Structure
   AI: Organize facts into narrative outline

2. Draft
   Me: Write the actual story
   (AI doesn't write journalism)

3. Fact-check
   Me: Verify every claim
   AI: Help find supporting evidence

4. Edit
   Me: Human editor reviews

Five Months Later

The Numbers

Stories completed: 37 (vs. typical 20 in 5 months)

Quality: Same or better (editors confirm)

Time per story:

Before: 2-3 weeks average
After: 1 week average

Scoops: 5 (vs. typical 1-2)

Why: AI helps me follow more leads, faster.

What Changed

My job didn’t get easier. It got different.

Before:

80% time researching
20% time writing/reporting

After:

40% time researching (AI-assisted)
60% time writing/reporting/interviewing

Result: Better stories, more human connection, less drudgework.

For Other Journalists

Getting Started

Week 1: Pick one small story

Something you'd normally spend 2 days researching
Try AI for the research phase
Compare to traditional methods

Week 2: Analyze what worked

What did AI do well?
What did you still need to verify?
Would you use it again?

Week 3: Develop your workflow

Define your rules:
- AI can do: X, Y, Z
- I must do: A, B, C
- Verification required: Always

My Rules

AI can: �?Find public records
�?Cross-reference databases
�?Identify patterns
�?Suggest questions
�?Organize research

AI cannot: �?Interview sources
�?Make ethical judgments
�?Write the final story
�?Replace human verification
�?Understand nuance

I must always: �?Verify facts
�?Call sources
�?Apply editorial judgment
�?Write the story
�?Own the work

Common Objections

”AI will replace journalists”

No.

AI can’t:

Interview people
Build trust with sources
Understand context and nuance
Make ethical decisions
Write compelling narratives
Hold power accountable

AI makes good journalists better. It doesn’t replace journalism.

”AI makes mistakes”

Yes, constantly.

So do human researchers.

Solution: Verify everything. (You should do this anyway.)

”It’s cheating”

No more than:

Using Google
Having an intern do research
Using a spell checker

It’s a tool. How you use it matters.

”Readers will know”

They won’t care if:

Story is accurate
Sources are solid
Writing is good
Story matters

They will care if:

Story is wrong
No human verification
AI wrote it (and it shows)

The Technology I Use

Core Tools

Research AI: Custom setup using SearchCans + OpenAI

async def research_query(question):
    # Search for information
    results = await searchcans_api.search(question)
    
    # Extract relevant content
    contents = []
    for result in results[:10]:
        content = await searchcans_api.extract(result.url)
        contents.append(content)
    
    # AI synthesis
    analysis = await openai.analyze({
        'question': question,
        'sources': contents
    })
    
    return {
        'answer': analysis,
        'sources': [c.url for c in contents],
        'confidence': analysis.confidence
    }

Cost: ~$50/month

Fact-checking: Manual (me) + source verification

Writing: Me (AI doesn’t touch this)

The Bottom Line

I was wrong about AI.

It’s not coming for my job. It’s making my job better.

Before AI:

Overwhelmed by research
Missing stories due to time
Frustrated by tedious work

With AI:

Research faster, go deeper
Cover more important stories
Focus on what makes journalism journalism: human connection, ethical judgment, compelling narrative

The relationship: Think of AI as the world’s best research intern who:

Never sleeps
Never complains
Finds everything
But still needs supervision

My advice to fellow journalists: Try it. Carefully. With healthy skepticism.

You might be surprised.

I was.

Resources

For Journalists:

AI Content Creation Guide - Balanced approach
Fact-Checking AI - Verification methods
Data Quality - Source evaluation

Technical Setup:

API Documentation - Research infrastructure
AI Research Guide - Deep research
Building AI Agents - Custom tools

Get Started:

Try Free - 100 credits
Pricing - Affordable for freelancers
Support - Setup help

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