How Top-Down AI Introduction Leads to Incremental Business Improvement

Artificial intelligence offers the opportunity for radical improvements such as completely new business solutions. It also enables the improvement of existing business. This paper reports on a case study that tests two strategies to identify AI use cases: top-down and bottom-up. The use cases are differentiated according to whether they promise incremental or radical business improvements and whether they are realizable in the short or long term. The top-down strategy identifies use cases that promise short-term but incremental improvements. They relate to existing business, but no disruptive ideas emerge. The bottom-up strategy allows for a broader understanding of AI’s potentials to improve business. Completely new and disruptive ideas emerge, but require huge upfront effort. Organizations best start with AI pilot projects that are feasible in the short term: Either by first applying a bottom-up strategy that is supplemented and evaluated with the top-down strategy, or top-down only

Top-Down or Explorative? A Case Study on the Identification of AI Use Cases

Despite the huge potentials granted to AI to improve business, several organizations already struggle to identify purposeful AI use cases. To guide organizations to systematically identify and assess AI use cases, two trajectories emerge: a top-down approach aiming to improve current processes, offerings or decisions by AI. And an explorative approach that broadly explores business problems and AI’s technological potentials to identify AI-enabled solutions. We apply both approaches in a case study and report on the results and evaluation. The top-down approach identifies AI use cases that are highly aligned with existing business and data. They aim to improve current solutions while no entirely new ideas were found. The explorative approach leads to AI use case ideas aiming for analyses that were not addressed before. They mostly create new ideas with a broader business perspective but are often infeasible due to low data availability

idea-AI: Developing a Method for the Systematic Identification of AI Use Cases

Artificial intelligence offers various paths to value for organizations. However, many organizations struggle to identify its actual potential in order to solve or improve business problems. Following the design science research paradigm, this paper suggests idea-AI as a method supporting organizations to systematically identify and assess artificial intelligence use cases. Following CRISP-DM, idea-AI uses a business understanding and a data understanding phase. For the business understanding phase, idea-AI suggests two approaches to identify suitable use cases: a systematic top-down approach and an explorative user-centered approach. For both approaches, appropriate activities, roles, instructions, tools and outputs are suggested. Finally, idea-AI is tested and evaluated within a case study in the construction sector