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The challenge

Artificial intelligence is a very powerful tool in solving complex tasks and is already being used for more and more processes. Nevertheless, many companies are only at the beginning of using AI possibilities. One reason for this is a high level of uncertainty in dealing with self-learning systems, another is the lack of trust in the results.

AI solutions are often perceived as a "black box": Input and output are visible, everything in between is in the dark. This lack of transparency can lead to uncertainty and causes many AI projects to fail early on. Other challenges are imprecise outputs of AI solutions and lack of scalability. In addition, there are growing regulatory requirements, especially with the upcoming EU "AI Act".

Our solution

With our offering in the area of "KPMG AI Performance & Governance", we accompany our clients in implementing and checking data science best practices and compliance requirements from a single source. With solutions such as our "KPMG XAI Cockpit", we close the trust gap and enable the control of algorithms with regard to their credibility and objectivity.


We support customers with our "ABC for AI":

In Action – Optimising the Machine Learning Lifecycle: Explaining Predictions, Detecting Misbehaviour, Understanding in the Enterprise.

For AI solutions to be successful, it is important to focus on the performance of the model. Data Scientists need to consider various quality measures during the machine learning lifecycle to evaluate, understand and optimise the model. Monitoring the appropriate KPIs is critical and helps avoid costly AI errors and harmful behaviour.

For Business – Enterprise-wide change management: sustaining adoption by focusing on employee or customer perspective.

In today's world, AI is key to automating, accelerating and improving key business processes to support large-scale transformation and drive value. A business-focused approach throughout an organisation's AI journey is critical to create tailored solutions for users and customers, while also taking into account the cultural changes involved.

In Control – Well prepared for the regulatory future: Consideration of legal requirements such as DSGVO and AI Act

The need for uniform AI guidelines has also been recognised globally. At EU level, the focus is currently on the EU AI Act. This will have extensive effects on companies that offer or use AI solutions, both internally and externally. We would be happy to advise you on a needs-based and practicable implementation of the regulatory requirements: This ranges from determining the status quo, setting up appropriate governance processes and guidelines, to operational implementation and compliance with the requirements.