Methodology · AI-supported delivery
Methodology · AI-supported delivery

AI that streamlines project work.

From analysis and data migration to testing and documentation: we automate recurring project work with AI tools and our own programming. Faster and more precise, at equal or higher quality.

Discuss using AI
Built in, not bolted on

AI is built into the methodology, not bolted on.

AI-supported delivery at DGP means: artificial intelligence is built into our project methodology, not bolted on. We automate recurring project work with AI tools and our own programming, from analysis through documentation to data migration and testing. That streamlines effort and time, at equal or higher quality. Control stays with people.

With us, AI works on your data foundation. It cannot substitute for it. From analysis through to go-live it speeds up project work wherever the basis is clean and rule-based. The value comes from the preparation, not from the feature itself. We know what holds up and what does not from over 25 years of project work, not from a product demo.

Where AI accelerates delivery

Five concrete fields.

Not as hype, but as a tool in the project, always with control in human hands.

Analysis & requirements

We structure and document requirements faster and more completely, as a basis for gap-fit and target picture.

Data migration

AI supports mapping, spotting duplicates and anomalies and validating, before migration.

Configuration & development

Our own programming and AI tools streamline recurring tasks in configuration and extension development.

Testing & regression

Generate test cases faster and find regressions earlier, so testing stops being the part everyone underestimates.

Documentation & status

Documentation and status reports emerge along the way, instead of at the end, and stay up to date.

The AI works on your data foundation. It cannot substitute for it. What stays is ready for any technology change.
Frank Maier, founder of DGP
The effect in the project

Less effort, equal or higher quality.

Because we streamline the repetitive work, effort drops while quality stays the same or improves. That saves time and budget. No hype: AI replaces neither clean process design nor data maintenance, it lifts efficiency where the basis is right, and exposes weaknesses where it is missing.

From the field

AI-supported, in a real project.

What it looks like when AI accelerates delivery and control stays on your side.

Success Story

A special solution, made usable in Business Central.

A complex product configurator, rebuilt AI-supported and senior-led, and cleanly connected to Business Central.

To the success story →
The honest framing

Maturity before function.

Hype

  • AI as a button that solves problems
  • Automate before processes are clarified
  • Feature first, foundation later
  • Hand control to the machine

How we use AI

  • AI as a tool on a clean data foundation
  • First mature processes, then support
  • Maturity before function, measurable value first
  • Traceable rules and checkpoints, humans decide
Further reading

AI in delivery, not AI in operation.

This page describes AI in project work: how we implement faster and more precisely. AI that then works permanently in the ERP, such as the Sales Order Agent or the Payables Agent, is a separate topic.

To BC Agents / AI in the ERP
Insights

Related content.

Knowledge

AI in BC: what is realistically possible

Read
Knowledge

How AI changes the effort in ERP projects

Read
Frequently asked questions

What is asked about AI-supported delivery.

Does AI replace your consultants?

No. AI streamlines the recurring work, control, the decisions and the responsibility stay with experienced people.

Do we need a separate AI project for this?

No. AI is part of our approach. What it needs is a mature data foundation and defined processes, otherwise every feature stays decoration.

Do we keep control of our data?

Yes. AI works on traceable rules with checkpoints, control stays on your side.

What does an agentic engineering process with Business Central look like?

As an interplay of AI agents and senior accountability: agents take on bounded work packages such as analysis, code scaffolding, tests and documentation, while an experienced lead defines the tasks, reviews the results and carries the responsibility. That accelerates delivery, but changes nothing about the order: processes and data foundation first, then automation.

Where could AI already pay off for you?

In conversation we find out which process is mature enough to support it. Senior-led, without hype.

Arrange a conversation