---
title: Agent-Ready Data Assessment | Lean TECHniques
description: AI agents act on the data you give them. In about two weeks, find out where yours will mislead your AI and what it takes to fix it.
---

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 AGENT-READY DATA

# Is your data ready for AI agents?

The Agent-Ready Data Assessment shows where your data will mislead your AI and what it will take to fix it, in about two weeks.

[Request an assessment](https://learn.leantechniques.com/agent-ready-data#request-assessment)

![Agent-Ready Data](https://learn.leantechniques.com/hubfs/Agent-Ready%20Data.png)

## Why AI pilots stall before production

Many AI pilots do well in a demo. Once they meet real users and real decisions, the answers start to drift.

The model is usually working as designed. It’s reasoning over data that was built for reports and for people, and people know which numbers to question. Agent-ready data closes that gap. It’s clean, curated, governed, and fast, and getting there rarely takes a new platform.

#### Out-of-date data

A person knows yesterday’s report is a day old. An agent acting on it treats it as current.

**Agent-ready means fast:** changes like a filed claim or a new sensor reading arrive as they happen, so agents work from the current state of the business.

#### Terms that mean different things

Finance and operations may count “active customers” differently. Your teams translate automatically. An agent can’t.

**Agent-ready means curated:** important business terms have one agreed definition, shared by reports and agents alike.

#### Access rules built for people

Permissions designed for analysts weren’t built for software that acts on its own, and they rarely show what an agent saw or did.

**Agent-ready means governed:** clear limits on who and what can read or change the data, and a record of every action taken.

#### Answers no one will stand behind

If an output can’t be traced to a trusted source, people stop using it, and the pilot goes no further.

**Agent-ready means clean:** quality checks run before anyone, or anything, uses the data, and records that fail are held back.

**When that work is done, your data speaks the language of your business.** People trust what it tells them, and agents do real work within the limits you set.

## The Agent-Ready Data Assessment

The assessment focuses on the part of your business where agents will do the most work, or already are. LT’s data practitioners spend about two weeks with your team, pairing a close look at your data with the business context that AI tools don’t have.

**You’ll come away with:**

- Where the way your data is modeled, moved, and transformed will mislead an agent
- The gaps in quality, definitions, and access holding your pilot back
- A prioritized roadmap for getting your data where it needs to be
- A plan you can take to leadership and the board

### The experience behind the assessment

LT’s data practice is led by Matt Gordon, Director of Data & Analytics and a **nine-time Microsoft Data Platform MVP**. Because LT’s data and software teams work together, a problem that starts in an application gets fixed there instead of being patched downstream.

## Common questions

### Do we need to be on Microsoft Fabric?

No. LT works across Fabric, Databricks, Snowflake, and mixed environments.

### What if we haven’t started with agents yet?

It’s easier and cheaper to fix data before agents are in production than after.

### What happens after the assessment?

The findings and roadmap are yours. If you want help with the first fixes, LT can work alongside your team so the skills stay in-house.

## Get In Touch

Reach out below to start a conversation around what an agent-ready data assessment can look like for you and your organization. 

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