What Data Do You Actually Need to Build an AI Agent?

"We don't have enough data for AI."

This is the #1 thing stopping businesses from starting with AI agents. But here's the truth: you need less data than you think, and it doesn't have to be perfect.

Let's clear up exactly what you need (and don't need) to get started.

The Big Misconception

What people think AI needs:

• Millions of rows of perfectly formatted data
• Years of historical records
• Clean, organized databases
• Everything in one system

What AI agents actually need:

• Examples of the task you want to automate
• Basic information to reference (product details, policies, etc.)
• Enough patterns to learn from (usually 20-50 examples minimum)

The difference between these two is the difference between starting now and never starting.

Data Needs by Agent Type

Different agents need different data. Let's break it down:

Customer Service Agents

Minimum Viable Data:

• 30-50 example customer inquiries with your team's responses
• Product/service information (what you sell, pricing, policies)
• FAQs or common questions (even if informal)
• Contact information and business hours

Nice to Have:

• Historical support tickets
• Customer database
• Product specifications
• Return/refund policies

Don't Need:

• CRM with perfect customer histories
• Years of organized ticket data
• Formal knowledge base

Where to find it: Your email, chat logs, and team members' sent folders. It doesn't have to be in a database—Google Docs works fine to start.

Sales Qualification Agents

Minimum Viable Data:

• 20-40 examples of qualified vs. unqualified leads
• Your ideal customer profile written down
• Basic questions you ask leads
• Your product/service offering details

Nice to Have:

• CRM data on conversion rates
• Lead source information
• Historical win/loss reasons

Don't Need:

• Sophisticated lead scoring models
• Years of CRM data
• Perfect classification of every past lead

Where to find it: Ask your sales team what makes a good lead vs. a bad one. Document those criteria. That's often enough.

Workflow Automation Agents

Minimum Viable Data:

• Written process documentation (even informal)
• 10-20 examples of the workflow completed
• Input/output examples
• Any templates or forms you use

Nice to Have:

• Process completion times
• Error rate data
• Volume metrics

Don't Need:

• Formal process maps
• Six Sigma documentation
• Complete historical records

Where to find it: Watch someone do the task once and document the steps. Use screen recordings if helpful.

The "Good Enough" Standard

Your data doesn't have to be perfect. Here's what "good enough" looks like:

✅ Good Enough Data

• Emails between team and customers (even if scattered)
• Google Docs with product info (even if outdated in parts)
• Slack conversations showing how you handle things
• Spreadsheets tracking information (even if incomplete)
• Team knowledge that can be written down in a day

❌ Not Enough Data

• Literally nothing written down anywhere
• No examples of the task being done
• Complete inability to articulate the process
• Zero information about your products/services

The line is lower than you think. If you can do the task, there's enough data to teach an AI to help.

The 4 Types of Data That Matter

1. Training Examples (Most Important)

What it is: Examples of the task being done correctly.

How much you need: 20-100 examples (more is better, but not required)

Example:

• For customer service: 30 customer emails and how you responded
• For data entry: 20 completed forms showing input → output
• For qualification: 40 leads marked as qualified/unqualified with reasons

Where to get it:

• Past emails/tickets
• Team members' work
• Manual records
• Even creating examples from scratch works

2. Reference Knowledge (Second Most Important)

What it is: Information the AI needs to reference to do its job.

How much you need: Enough to answer common questions.

Example:

• Product specifications
• Company policies
• Pricing information
• Hours, locations, contact info

Where to get it:

• Your website
• Internal wikis/docs
• Team shared drives
• Even handwritten notes work initially

3. Context (Nice to Have)

What it is: Background information that improves responses.

How much you need: Optional but helpful.

Example:

• Customer purchase history
• Previous interactions
• Account information
• Preferences

Where to get it:

• CRM if you have one
• Transaction records
• Notes in systems

Don't have this? You can still build an effective agent and add it later.

4. Performance Data (For Optimization)

What it is: Metrics to measure and improve.

How much you need: Can be collected after launch.

Example:

• Response times
• Resolution rates
• Customer satisfaction
• Error rates

When you need it: After deployment, not before.

Common Data Scenarios

Scenario 1: "We Have Nothing Organized"

Reality: You probably have more than you think.

Look for:

• Sent items in email
• Chat history (Slack, Teams, etc.)
• Attachments in old emails
• Team members' personal notes
• Your own memory of how things work

Action: Spend 2-3 hours documenting what exists. You'll usually find 80% of what you need.

Scenario 2: "Our Data is Messy and Incomplete"

Reality: Messy data is better than no data.

What to do:

• Use what you have, even if imperfect
• Fill obvious gaps manually
• Clean as you go (not before you start)
• Agents can help organize your data as part of implementation

Remember: Perfect data is the enemy of good enough data.

Scenario 3: "We Have Data But It's Spread Everywhere"

Reality: This is totally normal.

What to do:

• Make a list of where data lives
• Export what you can
• Integrate what you can't export
• Start with most accessible data first

Don't: Wait until everything is in one perfect system.

The Data Prep Process (2-5 Days, Not Months)

Here's the realistic timeline for preparing data:

Day 1-2: Inventory

• List what data exists and where
• Identify gaps
• Estimate effort to gather

Day 3-4: Collection

• Export relevant information
• Document key processes
• Gather example conversations/transactions

Day 5: Organization

• Put it in a format that's usable (even just Google Docs)
• Fill critical gaps
• Document any assumptions

Total: Less than a week for most small businesses.

What If You Really Don't Have Enough?

If you genuinely don't have enough data, here are your options:

Option 1: Create Sample Data

Generate examples by:

• Having team members document their work for 1-2 weeks
• Creating hypothetical scenarios
• Role-playing customer interactions and recording them

Timeline: 1-2 weeks to generate minimum viable data

Option 2: Start with Collection

Build an agent that helps you collect data:

• Handles inquiries and logs them
• Gathers information from customers
• Documents team responses

Use it as both a tool and a data collection mechanism.

Option 3: Delay and Prepare

Sometimes it's worth spending 1-2 months organizing first if:

• You're truly starting from zero
• Your processes are completely undocumented
• No one can articulate how things work

But this should be rare. Most businesses have more data than they realize.

The Bottom Line

You don't need:

• ❌ Perfect data
• ❌ Years of history
• ❌ Everything in one system
• ❌ Millions of records
• ❌ Formal databases

You do need:

• ✅ 20-100 examples of the task
• ✅ Basic reference information
• ✅ Ability to describe the process
• ✅ Willingness to refine as you go

The best data strategy: Start with what you have, launch faster, improve continuously.

The worst data strategy: Spend 6 months "getting data ready" and never launch.

Next Steps

1. Take inventory of what data you already have (1-2 hours)
2. Identify your biggest data gaps
3. Decide if you have "good enough" to start
4. If yes → Move forward with implementation
5. If no → Spend 1-2 weeks collecting minimum viable data

Still not sure if your data is sufficient?

Take our AI Readiness Assessment to get specific guidance for your situation, or schedule a consultation to review your data with us.

Remember: The question isn't "Is my data perfect?" It's "Is my data good enough to start?"

And the answer is almost always yes.