Building AI Innovation Labs together with Companies

TL;DR

This paper introduces a comprehensive framework for building AI innovation labs in collaboration with companies, emphasizing idea generation, implementation, and evaluation based on nine years of practical experience.

Contribution

It presents a detailed, end-to-end framework for AI innovation labs, integrating business and technical aspects, with lessons learned from extensive real-world applications.

Findings

Framework covers idea generation to evaluation

Lessons learned from nine years of practice

Few existing publications on end-to-end AI innovation processes

Abstract

In the future, most companies will be confronted with the topic of Artificial Intelligence (AI) and will have to decide on their strategy in this regards. Currently, a lot of companies are thinking about whether and how AI and the usage of data will impact their business model and what potential use cases could look like. One of the biggest challenges lies in coming up with innovative solution ideas with a clear business value. This requires business competencies on the one hand and technical competencies in AI and data analytics on the other hand. In this article, we present the concept of AI innovation labs and demonstrate a comprehensive framework, from coming up with the right ideas to incrementally implementing and evaluating them regarding their business value and their feasibility based on a company's capabilities. The concept is the result of nine years of working on data-driven innovations with companies from various domains. Furthermore, we share some lessons learned from its practical applications. Even though a lot of technical publications can be found in the literature regarding the development of AI models and many consultancy companies provide corresponding services for building AI innovations, we found very few publications sharing details about what an end-to-end framework could look like.