Case study on the use of Microsoft AI solutions

Currently, not a day goes by without news in the area of AI. The following article is about a project with a company from Germany that has used features from the Microsoft AI stack to implement solutions for employees' daily work.

Highlights

• Added value of AI technologies in daily work
• How can AI be used in the context of customer projects 
• Requirements related to the AI Act and GDPR
• How can AI be used effectively in harmony with the human factor
• How does the secure use of AI solutions look in terms of an IT security strategy?

Challenges

As a leading consultancy strategy projects, the company's focus is on what their customers need. The human factor remains one of the most important aspects here. AI solutions must be easy to use and deliver reliable, reproducible results if they want to add value in day-to-day work. This made it even challenging to classify and use artificial intelligence correctly.

Statement taken during the project:

• With the solutions based on Azure OpenAI, we speed up our qualification process and the final validation activities in consulting projects, which is a significant advantage - explains the management.
• We use the Microsoft Azure solution architecture to meet the high requirements of our customers for the secure collection, storage and analysis of data - says the data protection officer.
• By using the Azure tools for SecDevOps, i.e. the interaction between security, development and IT operations, we can automate and standardize processes. - This is how the CISO summarizes the framework for the AI solution.

These design principles, as outlined by the CISO, are the foundation for fulfilling the requirements of the GDPR and the AI Act / AI Regulation without any problems.

Even sensitive data with aspects relating to the Geschäftsgeheimnis-Gesetz / German Trade Secrets Act can now be processed by Microsoft's AI solutions.

Example / quote from the project: One of our customers has well over 100 existing patents. The customer now wanted to know what new product suggestions the AI would generate based on the existing patents.

Objectives and solutions in the project

The driver of successful companies is not exclusively digitalization. Our markets have been saturated for a long time now, and only a few sectors are still focused on real growth, but more often just on shifting market share. The pace of innovations is becoming ever tighter. However, speed is only effective if there is a strategic idea behind the innovations. Finding out whether an idea meets the actual requirements of the market takes time. The aggregation of survey results and feedback from beta phases takes time to complete. 

Beta testing for new processes and products always has to survive against established expertise and experience. This specialist knowledge exists either in the heads of employees or in countless internal company repositories, databases and knowledge sources. Making this knowledge usable when testing new solutions was a challenge that could be solved with AI.

Quote from the project: With the Microsoft 365 Chat function, we can answer questions relating to data in our M365 environment in seconds. As we store data and information from our customers in secure project rooms in Microsoft Teams, this function is also available to us there.

The Microsoft 365 Chat solution is a feature in the context of Microsoft Copilot. Here, the quality of the prompt that a user enters determines the quality of the result that the AI generates. It was therefore a key factor during the rollout to ensure employee empowerment through a training concept.

Data accuracy was necessary, especially in the area of quality management of results. Here, the typical hallucination of generative AI solutions was prevented by teaching existing large language models in Azure OpenAI and using predefined prompts.

The solutions:

• Storing project data and information in Microsoft Teams / SharePoint means that this information can be analyzed using Microsoft 365 Chat.
• Training concept for the use of AI solutions / Prompt Engineering
• Special processes and quality control based on AI were made possible with customized Large Language Models in Azure OpenAI
• Solutions relating to specific topics were implemented using predefined prompts/prompt extensions with Microsoft Copilot Studio. 

Benefits

A data-supported approach to developing new and innovative processes and products can be applied much more efficiently, quickly and scalably with AI. The definition of new solutions, as well as the associated testing and quality management, is also supported by AI solutions. 

• Evaluating the current situation / evaluating exsisitng data pools in consulting projects
• The creativity of generative AI solutions is used to identify new processes and approaches for product innovations
• The human factor is seen as an initial component of the consulting approach, but can now focus on the essentials