> For the complete documentation index, see [llms.txt](https://docs.goodfit.io/goodfit-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.goodfit.io/goodfit-docs/product/personas-and-contacts/personas/ai-personas.md).

# AI Personas

### What are AI Personas?

An AI Persona is a reusable definition of the type of person you want to identify at every company in your target market.

Instead of defining a persona using job title keywords (for example, *VP Sales*, *Head of Revenue Operations*, or *Director of Marketing*), you describe the **responsibility** you're looking for. AI then reasons over every employee at a company to determine who is most likely to own that responsibility.

For example:

> **"Who is responsible for deciding which accounts the sales team should target?"**

The AI evaluates every person at the company using signals including:

* Current and previous job titles
* Full career history
* Seniority and tenure
* LinkedIn profile and bio
* Company and organisational context
* Location
* Historical job descriptions (where available)

Each candidate receives a confidence score and an explanation of why they were selected.

***

### Why use AI Personas?

Traditional contact matching relies on keyword rules.

For example:

* VP Sales
* Head of Sales
* Director of Sales

This works for straightforward organisations, but breaks down when:

* companies use unusual titles
* responsibilities differ between organisations
* buying committees are complex
* companies operate in multiple languages
* titles don't accurately describe ownership

AI Personas identify **who actually owns the responsibility**, regardless of their title.

***

### Creating an AI Persona

When creating a persona, focus on **responsibility**, not job titles.

#### Good example

> Find the person responsible for selecting and prioritising target companies for the sales organisation.

#### Avoid

> VP Sales OR Head of Sales OR Director Sales

The more clearly you describe the business responsibility, the better the AI can reason about who owns it.

***

### How matching works

For each company, GoodFit:

1. Collects available employee data.
2. Evaluates every employee against your persona definition.
3. Uses AI reasoning to determine responsibility ownership.
4. Returns the best matching contact(s).
5. Explains why each person was selected.
6. Assigns a confidence score.

Where available, GoodFit also incorporates additional context such as historical hiring data to better understand the remit of a role.

***

### Understanding results

Every AI match includes:

* **Matched contact**
* **Confidence score**
* **Reasoning**
* Supporting evidence where available

This makes every recommendation transparent and easy to review.

***

### Keeping personas up to date

AI Personas are continuously maintained.

When people:

* change roles
* join a company
* leave a company
* are promoted

GoodFit automatically refreshes the matching so your CRM stays current without manual maintenance.

***

### Best practices

* Describe responsibilities rather than titles.
* Focus on outcomes ("owns procurement decisions") rather than departments.
* Keep persona descriptions concise and specific.
* Review confidence scores for lower-confidence matches.
* Compare AI Personas alongside existing keyword personas during evaluation.

***

### Example persona

**Persona name**

Revenue Operations Decision Maker

**Description**

> Identify the person responsible for deciding how sales territories, account prioritisation, routing, and revenue operations processes are managed across the organisation.

GoodFit will evaluate every relevant employee and return the individual most likely to own that responsibility—even if their title is something unexpected such as *Commercial Operations Lead*, *Go-to-Market Systems Manager*, or *Business Excellence Director*.