Business model and strategy
Business models and technologies are closely interrelated. In the past, the business model often determined which technologies could be used. This “technology follows business” approach entailed that improvements could only be achieved in small increments. Technology was thus considered a means to an end and a limitation to business ambition.
The disruptive impact of artificial intelligence, however, is clearly not only a consequence of but also a significant influence on the business model. “Business follows technology” is coming to the fore. When used correctly, the new technological applications will not only help to make established applications better and more efficient, but also to develop entirely new innovative products and services or opening up new markets. 
 Competition, customers, business models and market potential alike are all affected by the change. Therefore, the use of data-driven technologies must be in line with the company’s strategy and vision and requires an active approach to the necessary transformation of the business model. 

Operating model and governance 
Data-driven technologies place high demands on people and processes as well as organization, culture, and leadership: analytics and AI require new skills and roles such as data analysts, data engineers or data scientists to be integrated into the organizational structure and orchestrated in their functions. In addition to a modern understanding of leadership and a “data-driven mindset”, an organization needs suitable processes and methods to be able to carry out data-driven projects effectively and efficiently. Considering the various special requirements, the operating model must be prepared for and aligned with the changes, while fulfilling governance and compliance criteria.
Among other things, the course needs to be adjusted in areas such as recruiting and upskilling in order to identify and meet future requirements at an early stage. Moreover, appropriate processes and new responsibilities – both within and between organizational units – need to be established. In terms of data availability, (internal & external) sources from which to procure meaningful data must first be identified. Additionally, structures suitable for its storage and transfer as well as consistent data quality need to be ensured. Sound data management is an absolute prerequisite for the success of any AI initiative, regardless of its scale.

Technology and architecture
The success of data-driven applications is largely based on the quality and effectiveness of the developed models as well as the availability of reliable and up-to-date data. The most important factor in that regard is professional data management, accompanied by a selection of future-proof tools, frameworks, platforms and elements of IT infrastructure. Aligning these components and embedding them in the enterprise architecture is crucial for implementing a target-oriented AI strategy.
Performance, stability and reliability of AI solutions affect both their ongoing development and day-to-day operation. When it comes to choosing the corresponding elements, criteria such as interfaces, functionalities, IT security and costs are of the essence. All in all, the new systems must be in harmony with each other as well as with the existing IT landscape. In the long run, this can save the effort of ongoing adjustments.

AI is not a question; it is the answer to profound changes in the market for financial services providers. Do you want to create an ideal environment for your AI systems? We look forward to helping you tackle your challenges!