Massimiliano Claps
Max Claps (Research Director, IDC Government Insights)
Jack Vernon
Jack Vernon (Senior Research Analyst, European AI Systems)

One of the most common questions people ask about autonomous vehicles (also known as AVs or self-driving cars) is how they will impact our lives in the short to medium term. The short answer, based on our research, is that we feel the hype around AVs is tapering off.

Automotive and transportation executives agree that levels 4 and 5 of the SAE driving automation scale (see below) won’t be reached before 2030 — and regulators will mandate human-operated backup before that level of autonomy is reached.

But the timeline to achieving true autonomous driving is only part of the equation. Technology suppliers and enterprises that need to decide on their autonomy road map must consider two other areas before they make their decisions:

  • The business value of AV. Early adopters of autonomous vehicles are unlikely to be consumers — they’re more likely to be transportation operators, logistics companies, or fleet managers.

They must analyze the business value of AV from three perspectives: economic/financial, ecosystem (co-creation and co-opetition), and purpose for society (environmental sustainability and social inclusion).

Take, for instance, a logistics company. As AVs do not require rest periods, their utilization rate will increase, at the same time as their profitability. Insurance costs are also likely to go down, while the life cycle of the vehicle may become shorter due to greater utilization.

The ecosystem value will be generated by the logistics company’s ability to partner with retailers, wholesalers, manufacturers, and e-commerce platform companies to develop new services and business models, leveraging the 24/7 availability of AV delivery vehicles.

The logistics company will also be able to contribute to societal improvement because the AVs will be increasingly safe. AV supplier product and sales executives should consider the business value when articulating their messaging for these enterprises.


  • The challenges of AVs. Stakeholders also need to address the technical (algorithms, data, hardware, skills), scientific (advances in understanding human intelligence), organizational (AI literacy, process change, labor relations), regulatory (data protection, legal explainability and safety), and societal (AI ethics and acceptance) challenges of AI.

The vehicles’ ability to drive in snow and rain or to detect human behavior are among the main technical and scientific challenges.

There is a lot of “road etiquette” when it comes to pedestrians and other vehicles. Much of it is subtle and not very scientific — hand gestures, facial expressions, mouthed words, small movements by the car itself, for example — and it varies by geography or urban and rural environments (with there being more pedestrians and cyclists in cities, for example). Being able to make decisions based on these types of signals requires some level of logical interpretation and reasoning by the vehicle’s software — something that present-day AI techniques aren’t very good at.

Regarding the organizational challenges, logistics companies need to think of the potential replacement of professional drivers by robot cars in the long term, as well as business process changes driven by the elimination of rest periods.

The regulatory challenges refer to those areas where policymakers will need to decide if or when it is safe for autonomous vehicles to drive — in city centers or suburban highways, for example, or in more controlled environments such as dedicated lanes or office campuses.

Societal challenges include addressing questions such as, Are we to assume that a self-driving car will never break the speed limit, even to avoid a collision? Are we to assume that it will always wait for the safest point to enter a line of traffic? Can you imagine being late for work because your car is not “pushy” enough?


Given all the hype, AVs are a perfect example of how tech suppliers and enterprises should take a strategic approach to decide how to prioritize their investments in AI systems such as conversational AI, computer vision, affective computing, intelligent automation, and recommendation engines. Decision makers should take a holistic approach to analyzing the business value of AI and the challenges that need to be addressed before the value of full autonomy can be realized.



If you want to learn more about this topic or have any questions, please contact Jack Vernon, or Massimiliano Claps, or head over to and drop your details in the form on the top right.

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