---
title: "en:twine vs AI Wat Nu: which AI partner fits?"
description: Compare AI agencies AI Wat Nu and en:twine. AI Wat Nu offers
  workshops and a 12-month programme. en:twine suits custom work around your own
  processes.
lang: en
canonical: https://www.en-twine.ai/en/ai-agencies/ai-wat-nu/
updated: 2026-10-02T17:19:37.000Z
---
If you compare en:twine with AI Wat Nu, you are comparing two practical routes to AI for SMEs. AI Wat Nu is a good fit when you want a visible SME partner with workshops, a structured programme, public prices, and support from experimentation to organisation-wide adoption. en:twine is a better fit when you first want to understand your own processes, systems, and data sources, and then implement a solution that fits them.

## Where AI Wat Nu is strong

AI Wat Nu is a logical option if you want structure. The public site positions the agency as an AI partner for SMEs, offering advice, training, and implementation. The AI Programma is designed as a 12-month programme for organisations with 50-200 employees, with parallel tracks for management, employees, and workflows.

Individual components are also visible, such as an AI talk, a management workshop, an ambassador workshop, workflow analyses, an AI Act compliance audit, training, and implementation support. Public prices and starting prices quickly give you a reference point if you want an initial budget indication or a clearly scoped way to begin.

## How en:twine differs

en:twine does not deliver a standard product or a fixed package. We start with your organisation: where does work get stuck, what information does your team lack, where do errors arise, and where is there enough value to put AI to serious use? This is not only about saving time. It can also mean better output, more reliable processes, new services, or better management information because systems and data sources start talking to each other.

We only choose the technology once it is clear which process needs to work better. Then we connect to the systems and data sources you already use. That can be within the Microsoft ecosystem, through integrations, or with targeted connections to your CRM, ERP, documents, and databases. The goal is a process that demonstrably runs better.

Adoption is part of that work. We start by mapping the opportunities with the [AI Readiness Scan](/en/ai-readiness-scan/), then design and build the solution, and adjust it as your team starts working with it. This keeps AI from remaining an isolated experiment and makes it part of a process that runs measurably better.