A central objective of business processes and information systems – so far – is supporting people in coordinating their work and handling and tracing information. The systems hold and provide information to workers, schedule, coordinate, and orchestrate specific pieces of work, support decisions in how to proceed by providing overview and details to human decisions, or automating simple decisions in the presence of complete information.
Against this backdrop, the growing maturity in machine learning and block chains has been enabling technologies for new applications that have not been possible before
- Blockchain introduces a machine-readable and verifiable record of transactions, giving machines a trustworthy, verified data source for all involved parties, thereby enabling verification and decisions by algorithms not possible earlier because the input data necessary for the decision was not available at the required level of trust and completeness.
- AI and Machine Learning techniques made significant improvements in classification and prediction allowing to replace human processing by machines at faster speed and higher reliability. Well-trained deep neural networks can incorporate numerous nuanced factors into a decision that matches or exceeds the capabilities of skilled workers.
As many of these techniques are now reaching their “hype cycle peak”, awareness in industry is growing rapidly, giving rise to the question how they will impact balance of human and technical factors in business process and information systems.
During this panel, we will look at this question from different angles, discuss the potentials new technologies can have on a variety of use cases, which factors limit their adoption in practice, and which methodological challenges arise in a domain where techniques and trends once established remain standard for decades.
When and where
Thursday 14th, 10:40h — 12:10h (Auditori, Vertex)
- Jan Mendling (chair)
- Ingo Weber, Data61, CSIRO, Australia
- Gero Decker, Signavio, Germany
- Hajo Reijers, VU Amsterdam, NL
- Rick Hull, IBM, USA