What Is AI Research?

How Is AI Used for Research?

AI can help throughout the research process rather than only at the end.

A traditional research process might look like:

Question → search → open sources → read → take notes → compare → summarize

AI can compress several of those steps:

Question → AI searches or analyzes information → key findings organized → sources reviewed → conclusions developed

That doesn’t necessarily mean AI is doing all of the research.

It means the researcher has another tool for processing information.

For example, instead of manually reading 50 customer reviews one by one, AI could help categorize them into themes such as:

• Pricing complaints
• Product quality
• Customer service
• Ease of use
• Feature requests

You still need to determine whether those categories are accurate and what they actually mean.

AI helps reduce the mechanical work.

What Can AI Research Tools Do?

AI research tools can support a wide range of research tasks.

Find Information

Some AI tools can search the web or connected information sources to locate relevant material.

This can be useful for:

• Market research
• Competitor research
• Product research
• Industry research
• Content research
• Background research

The advantage is that you can often ask a complete question rather than reducing the topic to several disconnected search terms.

Summarize Information

AI can summarize:

• Articles
• Reports
• PDFs
• Research papers
• Interviews
• Meeting transcripts
• Long documents

This can save time when you’re trying to determine which sources deserve deeper attention.

A summary shouldn’t automatically replace reading the source.

For important research, it should help you decide what to read more closely.

Compare Sources

AI can help compare information across several sources.

For example:

Compare these three CRM platforms based on price, automation, integrations, and target customer.

The AI can organize the information into a common structure.

That can make differences easier to see.

Extract Information

AI can pull specific details from large documents.

Examples include:

• Dates
• Statistics
• Product features
• Names
• Quotes
• Requirements
• Pricing information
• Themes

This can be especially useful when reviewing large collections of material.

Identify Patterns

AI can help categorize or analyze large amounts of unstructured information.

For example:

1,000 customer comments → AI analysis → recurring complaints and themes

Or:

50 competitor pages → AI analysis → common positioning language

Pattern recognition can help researchers identify areas worth investigating further.

Generate Research Questions

AI can also help expand the research process.

If you’re researching a new topic, you might ask:

What questions should I investigate before evaluating this market?

That can help identify:

• Competitors
• Audience
• Pricing
• Market size
• Customer pain points
• Regulation
• Technology
• Distribution

AI is particularly useful at the beginning of research when you don’t yet know what you don’t know.

AI Research vs. Traditional Research

AI research doesn’t replace traditional research.

It adds another layer to it.

Traditional research remains essential when accuracy, methodology, and source quality matter.

AI mainly changes the speed at which information can be processed.

AI Research vs. AI Search

AI research and AI search overlap, but they’re not identical.

AI search helps find answers or information. AI research involves a broader process of investigating, comparing, evaluating, and synthesizing information.

For example:

AI Search

What is customer acquisition cost?

You receive an answer.

AI Research

Compare how SaaS companies calculate customer acquisition cost, identify the major differences in methodology, and summarize the implications.

The second task requires more than retrieval.

It requires interpretation and synthesis.

Growth Gary’s guide to what AI search is covers the search side separately.

AI Research vs. Generative AI

Generative AI creates new outputs based on patterns in existing information.

That might include:

• Text
• Images
• Code
• Summaries
• Explanations

AI research can use generative AI, but research involves a larger process.

A generative AI model might produce a summary.

An AI research system might:

Search → retrieve sources → read documents → compare findings → generate summary → cite sources

The research workflow matters.

That’s why a general chatbot and a dedicated research tool don’t always behave the same way.

What Are AI Research Tools?

AI research tools are software applications that use AI to help users discover, analyze, summarize, or organize information.

Different tools specialize in different kinds of research.

Some focus on:

• Web research
• Academic research
• Company research
• Document analysis
• Search
• Knowledge management
• Data analysis

General-purpose AI assistants can also support research, particularly when they have access to search or uploaded files.

Examples of research-related tools may include:

• ChatGPT
• Claude
• Gemini
• Perplexity
• NotebookLM
• Elicit
• Consensus

The right tool depends on what you’re researching.

An academic researcher evaluating scientific literature has different requirements from a marketer researching competitors.

Tools should match the job.

What Are Some Examples of AI Research?

AI research becomes easier to understand through specific use cases.

Competitor Research

A marketer could use AI to:

1. Identify competitors.
2. Review websites.
3. Extract positioning.
4. Compare pricing.
5. Compare features.
6. Identify content themes.
7. Summarize differences.

The result can become a competitor brief.

Content Research

AI can help identify:

• Questions audiences ask
• Competing content
• Common subtopics
• Missing information
• Relevant sources

That can support a broader content marketing strategy.

Customer Research

AI can analyze:

• Reviews
• Surveys
• Sales calls
• Support tickets
• Interview transcripts

and identify recurring themes.

This can help marketers better understand:

• Pain points
• Objections
• Language customers use
• Desired features
• Common complaints

Market Research

AI can help collect and organize information about:

• Competitors
• Market trends
• Products
• Categories
• Pricing
• Audience behavior

But important market conclusions should still be verified against reliable sources.

Product Research

AI can compare products based on:

• Features
• Price
• Reviews
• Strengths
• Weaknesses
• Use cases

That’s useful for both buyers and marketers.

What Are the Benefits of AI Research?

The main advantage is speed.

But there are several practical benefits.

Faster First-Pass Research

AI can help determine which information deserves deeper attention.

Instead of reading every source equally, you can triage.

Better Organization

Large amounts of information can be grouped into:

• Themes
• Categories
• Comparisons
• Timelines
• Summaries

That reduces cognitive load.

More Questions

AI can help identify research angles you may not have considered.

This is particularly useful when entering an unfamiliar subject.

Easier Comparison

Structured comparisons make complex information easier to evaluate.

Better Access to Large Information Sets

AI can analyze more information than one person could reasonably review manually in the same amount of time.

That doesn’t mean more information always creates better research.

It means the researcher can process more material before deciding what matters.

What Are the Limitations of AI Research?

AI research can fail in several important ways.

AI Can Be Wrong

AI can produce incorrect information.

That can include:

• Incorrect facts
• Misinterpreted sources
• Outdated information
• Invented details
• Unsupported conclusions

Important claims should be verified.

AI Can Hallucinate Sources

Some AI systems can generate citations or references that don’t exist.

Never assume a citation is legitimate simply because it looks convincing.

Open it.

Check it.

AI Can Oversimplify

A short summary can remove important nuance.

This is especially risky when researching:

• Law
• Medicine
• Science
• Finance
• Politics
• Complex technical topics

Sometimes the caveat is the most important part of the source.

Source Quality Varies

AI can retrieve information from weak sources.

A polished answer doesn’t tell you whether the underlying source is credible.

Source evaluation still matters.

AI May Miss Information

No research tool sees everything.

Search indexes, model knowledge, retrieval systems, permissions, and source availability all create limitations.

Treat AI results as part of the research process.

Not proof that the research is complete.

How Do You Verify AI Research?

Verification is one of the most important parts of AI-assisted research.

A useful process is:

1. Identify Important Claims

Not every sentence needs forensic investigation.

Focus on claims that affect the conclusion.

2. Open the Source

Don’t rely only on the AI’s description.

Read the original material.

3. Check Whether the Source Supports the Claim

A source can be real and still not support what the AI said.

4. Prefer Primary Sources

When possible, use:

• Official documentation
• Government data
• Academic papers
• Company information
• Original research

instead of summaries of summaries.

5. Compare Multiple Sources

If the information is important, look for independent confirmation.

6. Check the Date

Research becomes outdated.

That’s particularly important for:

• Software
• Pricing
• Regulations
• Market data
• AI
• Current events

How Should Marketers Use AI Research?

Marketers can use AI research throughout strategy and execution.

Useful applications include:

• Keyword research
• Audience research
• Competitor research
• Content research
• Product research
• Trend research
• Customer feedback analysis
• Messaging research
• Sales research

AI can accelerate the information-gathering process.

But marketers still need to interpret what the information means.

A tool might tell you:

Competitors frequently mention ease of use.

The marketing question is:

Does that represent an opportunity, a table-stakes feature, or meaningless category language?

AI can surface the pattern.

Strategy determines what to do with it.

How Does AI Research Help Content Marketing?

AI research can make content development faster and more comprehensive.

Before writing an article, AI can help identify:

• Audience questions
• Existing search results
• Relevant entities
• Common explanations
• Competing viewpoints
• Primary sources
• Supporting data
• Content gaps

That doesn’t mean asking AI to scrape the first ten results and rewrite them.

That creates average content by design.

The better approach is:

Research existing information → identify what’s missing → create something more useful

Growth Gary’s guide to what AI writing is covers the content-generation side separately.

Research should come before generation.

AI Research and AI Writing

AI research and AI writing work well together, but they should be treated as different stages.

AI Research

Helps determine:

• What is true?
• What do sources say?
• What does the audience need?
• What information exists?
• What gaps remain?

AI Writing

Helps turn that information into:

• Articles
• Reports
• Emails
• Guides
• Summaries

A weak process is:

Ask AI to write article from memory.

A stronger process is:

Research → verify → organize → write

That’s true whether a human or AI produces the first draft.

How Does AI Research Work With AI Agents?

AI agents can make research more autonomous.

Instead of manually asking for every research step, an agent may be given a broader goal:

Research our three biggest competitors.

The agent could potentially:

1. Search for competitors.
2. Visit websites.
3. Review products.
4. Compare pricing.
5. Analyze positioning.
6. Search for reviews.
7. Identify recurring themes.
8. Produce a report.

The agent determines more of the process.

That’s useful when the workflow contains many repeatable steps.

It’s also riskier because more independent actions create more opportunities for error.

How Do You Use AI for Research?

A simple process works well.

1. Define the Question

Be specific.

Weak:

Research AI.

Better:

Identify the major ways small marketing teams are using AI automation and compare the most common use cases.

2. Define the Evidence You Need

Ask what would support the conclusion.

For example:

• Primary sources
• Industry research
• Customer reviews
• Product documentation

3. Use AI to Find and Organize Information

Have AI help:

• Search
• Summarize
• Compare
• Categorize

4. Verify Important Sources

Open and inspect the underlying materials.

5. Look for Missing Perspectives

Ask:

• What contradicts this?
• What’s missing?
• Are sources independent?
• Are we relying too heavily on one perspective?

6. Build a Research Summary

Organize:

Finding → evidence → source → implication

That creates something useful for the next step.

Common AI Research Mistakes

AI makes research easier enough that bad research can now happen much faster.

Treating the AI Answer as the Source

The AI is the interface.

The underlying evidence matters.

Not Opening Citations

Always inspect important citations.

Researching Too Broadly

Clear questions produce better research.

Collecting Without Synthesizing

A folder containing 100 links isn’t research.

Research requires understanding what those sources collectively tell you.

Trusting One Source

Important conclusions deserve multiple perspectives.

Using AI Only to Confirm What You Already Believe

This is one of the easiest mistakes.

Ask:

What evidence contradicts this conclusion?

Good research should be capable of changing your mind.