Automation often sounds like the logical solution. However, the case of the Tesla Model 3 shows that even for a technologically leading company, the actual bottleneck does not necessarily lie where the most technology is visible. Sometimes, additional automation exacerbates a problem rather than solving it.
Company:
Tesla
Topic:
Automation and the actual bottleneck
Key takeaway:
More technology is not automatically the right solution if the actual bottleneck lies in the process rather than in the technology itself.
In 2018, during the production of the Model 3, Elon Musk publicly admitted: “Excessive automation at Tesla was a mistake. To be precise, my mistake. Humans are underrated.” At the time, Reuters placed this in a production context in which Tesla had not only automated the highly automated steps common in the automotive industry, such as stamping, painting and welding, but had also intervened more heavily in final assembly – precisely where problems apparently arose.
Tesla is a particularly good case study precisely because no one can claim that the company has been technologically backward or averse to innovation. This makes the real lesson all the clearer:
More technology does not automatically mean greater impact. If processes, handover procedures, timing, variability or organisational requirements do not fit together properly, additional automation can actually exacerbate a bottleneck. Reuters pointed out at the time that it was precisely the complexity of the final assembly stage that was a problem area.
The problematic assumption in such cases is often:
If a process is overloaded, more automation must be the right answer.
The Tesla case, however, shows that it must first be clear where the bottleneck actually lies. Is it in manual tasks? In process stability? In handover points? In variability? In timing? Or in unrealistic expectations regarding the level of automation? Only when this question has been properly answered will technology become a genuine lever rather than an expensive source of additional complexity.
For Aiquiro Research, this scenario would be the starting point for a rigorous preliminary assessment:
1. Where is the actual bottleneck – in the task itself or in the surrounding process?
2. Which parts of the workflow are stable enough for further automation?
3. Which interfaces, handovers or data issues limit the benefits?
4. Is the investment a genuine lever – or merely a technologically attractive symbol?
This preliminary assessment is particularly important when it comes to automation, digitalisation and AI. After all, modern technology appears convincing, even when it is applied in the wrong place.
The Tesla case shows:
Not every apparent friction is automatically a technical problem. Those who automate too soon, without understanding the actual bottleneck, easily create additional complexity. Those who, on the other hand, analyse the process thoroughly first, invest more strategically and with a higher probability of success.
It is therefore worth asking first whether the bottleneck is actually where the technology is intended to be applied.