The Research Lab Management of AI in collaboration with HCL is pleased to invite you to the virtual launch of the report “Future Trends and Requirements in Educating and Re-Educating the Workforce in the Financial Industry”.

There is an urgent reed to reskill and upskill talent to stay relevant in the changing times. The World Economic Forum predicted in January 2020 that the digital revolution will transform the future of work and the workplace: as many as 133 million new jobs will be created, but 75 million jobs are likely to be eliminated. World economic forum also launched Reskilling Revolution, a scheme aimed to future-proof workers from technological change and help economies by providing new skills for the Fourth Industrial Revolution.

Soon after, the world was catapulted into the realms of uncertainty with the arrival of a global pandemic. Now in a post-pandemic world, the need to gain valuables skills to stay relevant has just been accelerated. Global 2020 Talent trend study show that in financial services sector, according to executives, only 50% of the workforce is able to adapt to the new way of work. But 75% of employees say they are ready to learn new skills.

In this virtual forum, we hope to bring together industry experts, decision makers and academics to unveil the findings of the survey report. Our study reveals six major areas that financial services organization focus on to address the need to reskill and upskill their workforce. In this session, we will cover the highlights of the study that will help you understand what measures financial firms take to enable their workforces to meet the changing customer expectations, to adopt new technologies, and to stay competitive in the market.

We will also hear from industry experts on how the pandemic has forced financial organization to enable their workforce to build a resilient digital future.

Apply now as a corporate partner of our Design Thinking for Artificial Intelligence (DT4AI) program!

We help corporate partners to tackle real-world challenges at the intersection of human-centered innovation and the development of scalable AI use cases.

Expect to work with some of the brightest, motivated students from the University of St.Gallen and to join the innovation journey!

Some highlights of what to expect:

• Network with some of the best students from HSG
• Join our DT4AI boot camp sessions as a corporate partner to learn how DT can help you with AI innovation
• Expect a business-relevant, human-centered AI innovation prototype developed by cross-functional teams!

The application window is open until August, beginning of September at the very latest

Please reach out to Jennifer Hehn or Dr. Benjamin Van Giffen to work out an AI-innovation challenge and to join our program.

For more information klick here.

Artificial intelligence offers companies new opportunities to innovate processes, products, services and business models and to change existing ones. Therefore, the professional management of Artificial Intelligence in companies becomes a central task to realize the new value propositions with productive systems.

The article presents the St. Gallen Management Model for AI (SGMM-AI) and shows seven fields of action for the operational use of AI: (1) Management of Artificial Intelligence, (2) Organization of the business, (3) Legal design, (4) Regulation and Compliance, (5) Lifecycle Management, (6) Management of the technology infrastructure, and (7) Cyber Security.

This article guides concrete first steps and is primarily aimed at members of management, IT and innovation managers and project managers who want to put the new value propositions of AI into practice.

Digitization affects us all. Professionally and privately. The value of data is undisputed and can be considerably increased through the use of artificial intelligence (AI) and here especially through machine learning. But it takes much more than this insight to evaluate the AI potential for your company and to use it profitably. Not only by collecting data, but only by a value-adding configuration new knowledge can be extracted, which supports you in achieving your business goals. Nevertheless, it often turns out during project implementation that the hoped-for results cannot be achieved due to the special features and the multi-layered complexity of AI projects. Critical success factors are, for example, a clear customer and value orientation, the choice of suitable data science methods, an iterative approach, cross-domain collaboration and the establishment of AI-specific skills in the company. We provide you with advice on procedures and activities and work with you to create an individual needs analysis.

Bias plays a major role in the implementation of AI projects. Bias is a systematic deviation of the results of an algorithm from the desired results. This can be caused by a distortion in the data as well as by a bias in the algorithm and can lead to inaccurate or unwanted results. As a result, AI projects can suffer long-term damage and trust in the AI solution can be gambled away. There are numerous examples of this, such as the recruitment algorithm that prefers men for technical job postings because the underlying training data set mainly contained men in technical professions.

Different bias can occur at each stage of the project, so it is important to be aware of the possible bias at each stage of the project. To achieve this, we have identified the potential bias in the CRISP-DM. Once a bias is identified, it can be treated with mitigation strategies adapted to the bias in question. A bias is not always mitigated in the same CRISP-DM phase in which it occurs. For this reason, it is necessary to map the mitigation strategies to both the types of bias and the project phases.

If you have any questions about bias in AI model development, please do not hesitate to contact us!