Today, we see extensive investments in AI initiatives across all companies—either complementing Digital Transformation or as a separate initiative. Furthermore, in our daily use of consumer goods (smartphones, desktop PCs) or while using public internet services, applications of Artificial Intelligence (AI) have become indispensable.
At the start of any investment, the fundamental questions arise: What goals do I want to achieve, what results do I expect, in which areas and business processes must changes be made, what does the business case look like, and how can maximum return on investment (ROI) be achieved?
According to a report by the Harvard Business Review, approximately 80% of all AI projects could not be successfully completed in recent years. This is very high compared to the failure rate of traditional business IT projects (approx. 40%).
In many cases, the risk (that one's own AI project will end up among this 80% upon completion) can be assessed prior to investment by conducting a comprehensive (360-degree) analysis across all relevant business domains beforehand.
Selecting the right project management methodology has a major impact on the success of the project or initiative. In most cases, this methodology must be agile while also accounting for the specific characteristics of AI projects compared to traditional IT projects. The complexity and additional requirements for data and model functions cannot be adequately addressed using conventional agile projects in the AI domain. Applying advanced methodologies—such as CPMAI (Cognitive Project Management for AI)—leads to a higher success rate here.
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