Prototyping meets all the criteria for data-driven innovation development. In addition, it improves planning and safeguards decisions against potential resistance.
Key benefits of prototyping
User-centric specifications and development
Low-risk, data-driven decision-making
Structured development of your own data resources
Demo and improved planning for management-support
The choice of a suitable prototyping approach depends on the problem at hand. The options range from short-term effectiveness or feasibility tests to the first preliminary product components of the final application.
AI prototypes require precisely defined goals and six following work steps, until an initial result check is possible. Different approaches can be tested against each other or iteratively optimized and scaled.
We recommend realizing AI prototypes in the sense of a fast and efficient development via cloud-based machine-learning-as-a-service providers.
No complex integration, scalable infrastructure, and direct web deployment are central advantages. The compatibility of the services with common scripting languages such as R or Python and AI frameworks allows vendor-independent further development of the final application.
Functional demo (proof-of-concept)
Concrete specification of data requirements
Functional requirements overview
Process design and user interface specifications
Effort estimation and budget plan
Courses of action
Identification of suitable development partners
Use of a turnkey solution
Connection to existing system interfaces
AI feature process integration and interface development