It is well known that artificial intelligence (AI) enables better, faster and more automated decisions. Indeed, it has been proposed that AI is driving a resurgence of interest in redesign business processes.[i] That’s partly due to the ability of certain AI tools, such as robotic process automation (RPA) which when combined with machine learning as “intelligent process automation,” can automate information-intensive processes. It has also been argued that AI fits well into improvement methods such as Lean Six Sigma and can be applied at each stage of the so called DMAIC process (Define-Measure-Analyze-Improve- Control).[ii] Note that Six Sigma and Lean Six Sigma are highly codified and structured methods of process improvements which have a strong bias towards incremental improvement within organizational boundaries. The integration of AI into process improvement may have the potential to reignite interest in more major change – targeted at large enterprise processes – perhaps even reengineering.
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While excitement about generative AI is high, some questions persist as to how much value is being delivered. AI has been used by leading firms such as Amazon and Netflix to improve shopping recommendations, but examples of significant applications to improve overall business performance are not abundant. One area where AI has considerable potential is new product development (NPD). The NPD process has not changed much in most organizations for decades with fewer than 30% of new product projects becoming commercial successes. Yet only 13% of firms are using AI in NPD.
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