AI and Strategy

Artificial Intelligence provides immediate opportunities to improve processes and there is a continuous flow of attractive use cases, in part due to rapidly evolving technology. However, AI also provides opportunities for total transformation in the somewhat longer term. Properly anticipating AI therefore requires a strategy with room for two speeds.

AI is far from new. Scientists have been working on it for decades, and data scientists have also been applying AI in numerous applications in business and government for quite some time. But with the breakthrough of Generative AI, things have changed. The transformative power of AI has now also become a major topic at boardroom tables. Most agree that AI is a gamechanger in virtually every sector. Views differ on the pace at which that will occur. This is quite understandable, as there are still many uncertainties. This is not only because of the stormy and non-linear development of the technology itself but also because of shifting planes on other fronts, such as legislation and related societal expectations and values. 

Within the context of this uncertainty, it makes sense to go for a two-speed strategy. 

On the one hand, this involves quickly realising use cases and conducting experiments. These applications of (Generative) AI are often quick and easy to implement (in software terms: ‘next, next, finish’) and deliver immediate value, for example through improved operational excellence or personalised customer contact. Apart from the immediate value, there is another benefit of this ‘fast lane’: the organisation gains affinity with AI and builds knowledge. 

On the other hand, businesses must consider the more fundamental and structural transformative power of AI. This power varies by sector and involves, for example, the emergence of completely new business models, cross-sector innovations, the use of intelligent personal agents by employees, or other radical innovations. The long-term transformation can be very fundamental but is often perceived as less urgent. However, proper preparation for this cannot wait until tomorrow and will entail a redesign of the operating model, IT architecture, data management, governance, capabilities, compliance and other issues. In terms of content, this preparation involves leveraging as much as possible what we now know of (future) technology and experiences in other organisations. Up-to-date knowledge can make a lot of difference here. 

Practice shows that a certain 'use case fatigue' can set in within many companies after a period with much focus on quick use cases. There is a need for more holistic responses to the advent of AI. AI is therefore increasingly seen as an integral part of broader digitisation efforts (e.g. cloud, data, security, resilience). This also means a greater focus on integrated and scalable solutions rather than standalone AI applications.

KPMG helps you with up-to-date technology insights and industry knowledge to refine your strategy as much as possible. As mentioned, there are many uncertainties at play here. However, one thing seems certain: humans will play a crucial role in the transformation and, more specifically, in differentiating organisations. A new division of labour is emerging between humans and machines. The machine is increasingly taking over intelligent tasks from humans (and ‘learning’ the language of humans instead of the classic situation where humans had to master the language of the machine). But this does not put humans in a less distinctive role. On the contrary, with intelligence quotient (IQ) being largely taken over by machines, humans can differentiate with emotional quotient (EQ) and cultural quotient (CQ). The former primarily concerns empathy – a trait that machines are poor at – and the latter is about the ability to function effectively in culturally diverse situations.