# Innovation Potentials and Pathways Merging AI, CPS, and IoT
## 5 Case Analysis Autonomous Truck Driving and Maintenance
### Technology Forecasting  
The following Figure 5 is finally combining the technology extrapolation exercise (Figure 3) and the outlined case for autonomous driving (Figure 4). The outlined development in Figure 5 provides a general but instructive scheme for the future of AI applications in CPS and IoT environments: Extrapolation fields 'A' and 'B' as described in Section 4 are characterized by the _AI decisions_ profile--human users will have to adapt to the existence and superiority of AI decisions in order to work efficiency in such manufacturing and transportation environments (compare e.g., [56; 81]). In this area e.g., with cruise control applications in truck driving as described above, the resistance level may be on a medium level. It will also be necessary to train the drivers using such cruise control systems (level 3 with GPS application) in order for them to be able to understand the system. This is mainly directed at their necessary competence to recognize failures in the system and possible dangers from the AI application. This could for example be the fact that lower gears are used for braking effects on downhill sections--an AI application may shift up in order to save fuel. Regarding the extrapolation fields of 'C', 'D', and 'E', it is important to recognize that a double challenge awaits for the construction and implementation of such systems: On the one hand, different actors in supply chains such as manufacturers, suppliers, logistics service providers and transportation operators along with other adjunct service providers (IT, insurance, finance) have to be integrated with their data and decisions into such systems--requiring a high level or corporate trust and standards in terms of information exchange. On the other hand, all users and cooperating personnel will have to accept and embrace _AI autonomy_ on a full day-to-day scale. This may be hard to learn as many operative decisions such as e.g., order lot sizes will have to be accepted which are not understandable to the relevant personnel, not even on a management level. But still, having mastered those two challenges, the cooperating personnel such as truck drivers as well as managers in the supply chain oversight role will have to acquire a 'know-why'--competence regarding the AI systems in place in order to recognize when something is wrong, when security and management overrides could become necessary. Therefore, it will be a completely new game for management research and education to employ routines and signals for these operating personnel to rely on with this important task. Acceptance and even trust of drivers towards such automated systems will therefore play an important role for the competitiveness of road transportation companies from several perspectives: First, if the driver allows the AI application to do its job, it may be more efficient and cost saving than the human driver (saving fuel, reduced travelled distance, and reduced truck wear). Second, as drivers may assume further company tasks and processes instead of driving while travelling, this may save costs at other places (e.g., administrative personnel). Third, driving personnel may also be more effective and efficient due to a motivation boost within an AI application scenario. Finally, looking at the extrapolation field 'F' with the two-sided requirements of enabling and applying cross-supply chain decisions as well as a full scale of '_AI trust_', the gap towards existing technologies as well as competences of human personnel regarding cooperation with such a system become obvious: For autonomous truck driving, driver personnel would have to anticipate and accept that CPS and IoT systems merge with the support of AI applications; this implied that e.g., transportation routing will change on a last-minute basis, depending on global-scale environments regarding prices, material availabilities and manufacturing scheduling.