Proactive
For more trust in AI
Activism and isolated solutions increase liability and reputational risks
You need overarching leadership and governance structures to coordinate the use of AI in your company.
Does this sound familiar to you?
The first AI applications are in use, but nobody knows who is responsible. There are many ideas, but priorities are unclear. Pilot projects are running, somehow, but the desired effects have not been achieved.
This is not a technology problem. It is a lack of AI management.
What gives companies headaches regarding digital technologies
For years, I've observed and analysed how difficult companies find it to adopt new technologies. First it was digitalisation, now it's AI:
– Seemingly endless pilot projects
– Unmet ROI expectations
– Cybersecurity fears
Concerns about loss of control over data flows, for example through shadow AI
– Misinvestments that are difficult to justify to supervisory bodies and capital providers
With a systematic and structured approach, realistic expectations and a willingness to learn, digitalisation, automation and AI can be effectively embedded within the organisation.
AI management provides orientation and control, AI governance provides the guardrails.
AI management is the overarching leadership and control task for the entire use of AI within a company, across all use cases. AI governance is not a counter-model to this; rather, it provides the trustworthy framework with clear responsibilities, rules, controls, and evidence. Both belong together.
Not as an abstract strategy paper. But as a pragmatic approach that brings together benefits, risks, responsibilities and feasibility. Tailored to the reality of medium-sized companies.
These are four fundamental topics that every management team should consider before embarking on AI transformation:
1. clarify goals
What specific benefits should AI bring? And for whom? Why use AI?
2. regulate responsibility
Who decides, who controls, who bears the consequences? Governance is more than just a guideline that defines what AI can and cannot do. It determines whether the use of AI in the company remains controllable or grows uncontrollably.
3. manage risks
Making dependencies, limits and control mechanisms visible at an early stage. Clarity about how to deal with risks when they materialise.
4. anchor implementation
Do not treat AI implementation as a project and finalise it, but establish it as an ongoing component of corporate management.
AI management becomes relevant when...
the first AI applications are in use,
It's missing one common framework for objectives, rules, and responsibilities.
many ideas exist, but no priorities have been set,
There is confusion about which use cases are truly beneficial and where to start or proceed.
a pilot is to be scaled,
The implementation of the first AI application can trigger a patchwork of isolated solutions.
AI should become part of corporate management,
AI is considered an isolated topic for individual employees, rather than a management task with clear responsibilities.
Custom-fit AI management solutions
I am Achim Korten. Strategy research, company valuation, auditing, audit-related consulting – my professional background is also diverse. I support companies in introducing and effectively managing AI so that its use is trustworthy and auditable.
I look at AI from different angles: as a technology topic, through the eyes of an auditor, as a management tool and from the user's perspective.
For my consulting practice, this means that I do not work with standard programmes or ready-made solutions that adapt to every context.
I always look first at where a company is and where it wants to go. For the gap, I will develop a solution with you that fits the company's goals.
Yes? More about me
From why to how
AI management is not an end in itself. It only unfolds its value when goals, priorities, responsibilities and guardrails are turned into a concrete, actionable approach. Suited to the size, maturity and requirements of the company.
This is precisely what I developed d+Ai for: a structured approach to effective AI management. AI governance is the central pillar within this for responsibilities, controls, and evidence.
Let's discuss your topics and questions.
Within 30 minutes, we can clarify whether and how I can usefully support you.
Achim Korten | Auditor
Certifications: EU AI Act · ISO/IEC 27001:2022 + 42001:2023
Publisher of the LinkedIn newsletter „AI Governance Compass“