The implementation gap remains a reality and may even be growing. The rate at which AI projects make it from the strategy workshop or press release, through the development and validation phases, into production is still low, and a substantial inventory of undelivered projects exists. These serve to improve the hardware vendors’ numbers and the customers’ image but will eventually be found out.

A key driver of the gap may be projects that are justified by 'soft' KPIs. Retail and the wider consumer sector are the biggest AI growth areas, and they have experienced a bulge of investments into chatbots, VDAs, and product recommendations in the last few years. KPIs such as 'customer engagement' are very common here but are substantially less well-defined than, for example, revenue.

Although those enterprises that report it have seen impressive ROI results, surprisingly few enterprises have chosen to use this as a KPI. This may be due to a so-called file drawer effect, where nobody wants to discuss the projects that did not achieve a return. As a general rule, final goal KPIs such as revenue, ROI, or productivity are rare. The discipline of such KPIs may be an important lever to close the gap.

Across diverse sectors, there is a common pattern of AI readiness. Enterprises across all the sectors studied have made progress in defining strategy and adjusting their organization, and a subset has developed technology and operations capabilities. However, very few are even out of second gear when it comes to data.

Financial services (finserv) and retail did relatively well on AI readiness for contrasting reasons. Finserv was at least 'less bad' on the data category, probably because they already have substantial experience with processes such as credit scoring and fraud detection. Meanwhile, retailers (including e-commerce) stood out for having developed stronger in-house technology and operations skills.