The rise of artificial intelligence (AI) as a transformative technology is well documented, and with projections of future AI-based technology investment values reaching eye-watering values of USD 4.4 trillion,(1) and as such, the broad implementation and use of AI (generative AI and machine learning) seems almost inevitable.
Historically, the implementation of traditional and less complex technology has not achieved the expected level of success (value and benefit outcomes). In fact, some publications suggest that as little as 30%(2) of historic technology implementation has resulted in expectations being met.
At the same time, the use of business process re-engineering (BPR) methods, inter alia Michael Hammer, Lean, Six Sigma and Total Quality Management (TQM) in the preparation and implementation of technology solutions, have become common practice across all industries and functions.
However, as BPR practitioners, we have asked ourselves how our established BPR methodologies and frameworks need to be adapted for the implementation of AI. Through our own research, we found publications on this topic to be few and far between. Published AI use cases seem abundant, but the details around how these are successfully implemented are far scarcer.
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