The Productivity J-Curve best explains AI’s productivity paradox. The investments across different categories beyond technology initially depress measurable output. Rather…
Incumbent corporations are investing in and incorporating AI, yet most fail to fundamentally alter their operating models or achieve strong ROI as a result. The root of this failure is not technological. It is macroeconomic and organizational. US enterprises are attempting to execute a paradigm-shifting technological transition within a market environment that offers them no structural shock absorber, in the way a formal industrial policy can. These enterprises must navigate the AI transition while addressing a rapidly changing market environment and the AI Stakeholder Squeeze.
The business world is buzzing with AI experimentation. However, a structured approach for moving from experimentation to production, although necessary, is often lacking, which hinders AI’s transformative potential. This article introduces a decision-support framework, implemented as a decision tree, to help executives mitigate the risks associated with decisions relating to moving AI experiments to production, focusing on whether to enhance existing business processes with AI or redesign them as AI-first.
Despite the noise from high-tech corporations, startups, venture investors, and analysts, we are still in a very early phase of generative AI. As a result, it is hard to assume, let alone declare, that generative AI will transform every business process driving operational efficiencies, create new revenue streams, result in massive productivity improvements, and curtail costs. However, the early signs are adequately encouraging for corporations to allocate budgets and start pilot projects.