Client from the automotive industry
Exploration of new mobility services and risk reduction for technology investments
Challenge
Artificial intelligence, data mining, automation: the potential value of new technologies is widely discussed and - in theory - easy to recognize. But testing unknown technologies for their value in real-world applications and identifying the right use cases can be a much bigger challenge.
Our client from the automotive industry wanted to find out the true potential of an AI-based technology in the context of mobility services: What are the limitations of this technology? How can added value be created despite strict data guidelines? Does it create real value that can be perceived by the customer?
So we set out to translate a hyped trending topic into a real-life experiment and validate the relevant business impact.
Our approach
In a design sprint, we developed three new services that offer next-generation mobility solutions based on predictive analytics.
The services that performed best in an initial feedback round were prototyped into digital applications that form the basis for interaction between the brand and users.
The moment of truth
In the spirit of a "Wizard of Oz" approach, we simulated these services in the real world, including all operational tasks, such as transporting real users from A to B, the customer service hotline, or billing for the mobility services used.
In this way, we were not only able to obtain valid feedback on the acceptance and likeability of the services, but also how real data points influence and change the experience - providing valuable insights into the potential and limitations of artificial intelligence software in the mobility context.
3 New service ideas, 10 test scenarios, 50 test users, 8 weeks
Jean Boucsein
Business Partner Company Innovation, diffferent
"With the help of the minimum viable product, we were not only able to determine the acceptance and attitude of users towards the data-driven mobility service, but also assess the added value and quality of the predictive engine used."
The result
We succeeded in reducing the risk of innovation and technology investments in artificial intelligence.
The positive results of the simulation and the positive feedback from users were crucial in convincing management to pursue this use case beyond the initial prototype.
By working with real mobility data in real use cases in the simulation, we were able to realistically demonstrate the benefits, but also the limitations and challenges of predictive analytics in this context and provide a unique proof point for evaluating the technology.
Usage requirements were generated to evaluate potential partners and software that now form the backbone of all future mobility services of the brand.
Key Insights
- Development and testing of 3 service ideas
- Consideration of 10 testing scenarios
- 50 users in real-life testing
- Period: 8 weeks
Methods
- Design Sprint
- Rapid prototyping
- Simulation of the real world ("Wizard of Oz" testing)
- App & interface design & creation
- Data design & architecture
- Evaluation of suitability and acceptance
New Growth
Real world simulation makes it possible to test complex services and use cases in a suitable environment and minimize risks. This allows companies to grow with new services without having to take major risks or jeopardizing their existing business.
Further projects
We are happy to receive project inquiries
Jean Boucsein,
Business Partner Company Innovation
