AI and automation

# How to prepare data for implementing AI solutions

What data is needed, how to assess quality, and where are the boundaries of safe automation.

Author **Editor MAWERSTIN**

Reading time **3 minutes**

MAWERSTIN / KNOWLEDGE **AI and automation**

At a glance 

**How to prepare data for implementing AI solutions.**  What data is needed, how to assess quality, and where are the limits of safe automation. Below is a sequence that helps move from general understanding to verifiable actions.

**01** Identify who is responsible for the data

**02** Remove contradictions and duplicates

**03** Configure access according to roles

01 / CONTEXT

## How to prepare data for implementing AI solutions: what is important to understand first

The quality of an AI solution depends on what information is available and whether it can be trusted. Mixed versions of instructions, omissions, and unclear access rights lead to errors even with a good prototype. Data preparation begins with an inventory of sources.

01 

### Purpose

What business outcome needs to change and why is it important now.

02 

### Audience

Who is assigned the decision and what prevents the person from taking the next step.

03 

### Data

What facts will distinguish a real effect from a random change.

02 / ACTION PLAN

## Practical work plan on the topic “How to prepare data for the implementation of AI solutions”

Go through the steps sequentially and save the output, solutions and test results.

- **1** 

### Make a list of sources

For each source, identify its owner, format, update frequency and access method. Keep current instructions, archived materials and user data separate.

- **2** 

### Assess completeness and relevance

Check for duplicates, omissions and contradictions with real examples. Record which version is considered correct and who is responsible for updates.

- **3** 

### Determine acceptable use

Determine what can be transferred to an external service and what information should be excluded. Tool access must match the task and user rights.

- **4** 

### Prepare a test set

Collect typical questions, difficult cases and unanswered situations. For each, set the expected behavior: correct result, clarification or transfer to a person.

03 / RISKS

## Common mistakes and how to avoid them

- ✓ **Don’t confuse a goal with a metric.**  Views and clicks are only useful in relation to referrals and sales.

- ✓ **Do not introduce everything at once.**  Individual hypotheses are easier to test and compare.

- ✓ **Don’t lose context.**  The decision must take into account the company’s product, market, resources and constraints.

04 / FAQ

## Frequently asked questions on the topic

**Where to start?+**

From fixing the initial situation, the goal and one priority scenario that can be measured.

**When to evaluate the result?+**

After sufficient data and considering the decision cycle in your business.

**Can you do everything yourself?+**

Basic diagnostics — yes. For complex architecture, integrations or risks, specialized expertise is useful.

AI and automation 

## Need help with implementation?

Let’s analyze the initial situation, define priorities and propose a clear next step.

[Request a consultation **→**](https://en.mawerstin.com/contacts/)

Source: https://en.mawerstin.com/data-for-ai-solutions/