Why Implement a Management System?

In this project, we work with you to build a meaningful AI management system (AIMS). All information flows into one central place. What matters most: we don't create a system designed around rigid documentation, but a system that can genuinely provide answers and is actually lived.

Our prerequisite: This project builds on a prior Assessment, which transparently reveals what you really need to build your AI management system. That way, we start from a clear foundation – with no surprises.

What you have at the end: Transparent AI risks, always in view, and a valid solution for continuously evaluating the AI products you use. A system that works – not one that merely exists.

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We understand what we audit – technically and in terms of subject matter. That's why we build systems that don't just pass the audit, but create real value: transparency, risk awareness, autonomy.

ISO-Proof Mission Statement

How We Work

Structured from planning to a living system – in 3 steps

Step 1

Laying the Foundation

Establishing the basics & gaining an overview

Building on the prior Assessment, we draw up a clear project plan and lay the foundation:

Set up a clear project plan with a realistic timeframe
Gather all fundamental information (context, scope, stakeholders)
Conduct a complete AI inventory
Create a clear plan of the expected effort
Initial AI risk assessment based on the inventory

Outcome: A clear baseline with a complete AI inventory, a project plan and an initial overview of your individual situation in a report

Step 2

Establishing Processes & Responsibilities

Making the system operational

We set up the structures that get your management system up and running:

Establish processes for information delivery and data flows
Clearly define and communicate responsibilities
Assign areas of responsibility and ownership
Set up processes and data inflows in line with those responsibilities
Build the AI management system on the basis of the information and data inflows
Create all required documents and artifacts

Outcome: Established processes, clear responsibilities, a complete management system with all documents

Step 3

Operating Schedule & Go-Live

Bringing the system to life

Now it gets concrete – who does what, and when? This is how your system is put into practice:

Define the operating schedule: when each person in the process delivers their information
Link responsibilities to concrete timelines
Establish regular review cycles (continuous improvement)
Conduct an internal audit as preparation
Train the team and enable it to run the system autonomously
Support through to certification (optional)

Outcome: A living system with a clear operating schedule, a trained team, audit readiness

What Sets Us Apart

Why ISO-Proof is the right partner

AI Expertise

The certification process for auditors does not teach any AI expertise. Nor do company audits convey what AI systems actually are from a technical standpoint.

We have years of experience in ML, data science and AI engineering. We understand what we audit – technically and in terms of subject matter.

Operational Focus

Many audits are optimized for passing the audit rather than for leveraging the underlying benefits (risk awareness, transparency).

We build systems that don't just document, but help you in your day-to-day work – because it can be done in a meaningful way.

Asset Management Expertise

Inventorying and classifying AI systems, tracing data flows – that's our daily business.

We have many years of experience in software asset management and know how to structure complex IT landscapes.

Audit Edge Platform

Rigid checklists and spreadsheet chaos? Not with us.

Our smart solution Audit Edge automates the extensive process and makes it intuitive and fast – with AI support and intelligent analytics.

What You Receive

A complete, audit-ready management system

Complete Documentation

75–100 artifacts in accordance with ISO 42001
Management system manual
Policies and guidelines (AI ethics, data governance, etc.)
Process descriptions (development, deployment, monitoring)
AI system documentation (all systems in scope)
Risk assessments and action plans
Audit evidence (logs, reports, etc.)

Implemented Processes

PDCA cycle for AI management established
AI lifecycle processes defined (design → monitoring)
Risk management process operational
Incident response process
Continuous improvement mechanisms
Internal audit process

Trained Team

Workshops and training for all roles
Management system owners empowered
Data scientists / engineers made aware
Documentation best practices taught
Audit preparation completed
Autonomy built – you can carry on independently

Audit Readiness

Internal audit successfully completed
All findings resolved
Complete audit trail
Evidence structured and accessible
Team prepared for Stage 1 & 2 audits
Ready for certification! ✅

What You Really Take Away

Beyond the certificate

Transparent AI Risks

You always have certainty about which AI-related risks exist in your organization. No more black box – instead, a clear overview and assessment of all AI systems.

A Valid Assessment Solution

You receive a working, valid solution for assessing AI risks and the AI products you use at any time – not just at the time of the audit.

Genuine Autonomy

Your team is equipped to keep the system running on its own. No dependence on external consultants – you have built up the expertise in-house.

Optional: Audit Edge Integration

On request, we integrate our smart platform Audit Edge into your management system. This delivers massive efficiency gains and automation:

AI Support

Automatic document suggestions, intelligent gap analyses, smart checklists

Intuitive Dashboards

Real-time compliance status, progress tracking, risk heatmaps

Seamless Integration

Connection to your existing tools (Jira, Confluence, GitLab, etc.)

Reach Your Goal Faster

30–50% time savings through automation and templates

Investment & Timeframe

Implementing an AI management system is a substantial project. The actual costs and duration depend on many factors:

Number of AI Systems

The more systems in scope, the more extensive the documentation

Complexity of the Systems

High-risk AI vs. low-risk AI, in-house development vs. SaaS

Existing Maturity

How much is already in place? How big is the gap?

Internal Resources

How much can your team do itself? How much support do you need?

Price: On request

Duration: 3–6 months (from Assessment to audit readiness)

Following an initial consultation, we prepare an individual proposal for you – transparent, fair and with no hidden costs, based on your specific scope and the level of support you need.

Management System Implementation