Based on the NLP methodology introduced in our first study, we developed a proprietary MaaS Index to analyse the current state of cities and providers and understand key areas where improvement is needed.  With this objective, we sifted through our vast dataset, spanning from 2015 to 2023, focusing on English and German sources. We utilised the defined key constructs and variables essential for ecosystem-building activities and used them to develop the index. For it, we combined external interest ("hype") and internal ecosystem readiness, providing a two-dimensional view of the progress and challenges in MaaS implementation.

The key results of our second study reveal a stagnation in the maturity of MaaS ecosystems over the past eight years, despite varying levels of external interest. Our analysis identified three critical strategies for overcoming these challenges:

• Moving beyond app-based solutions to include substantial investments in advertising and asset ownership.
• Incorporating personalised services to drive user engagement and platform traffic.
• Enhancing public-private partnerships to navigate regulatory landscapes and promote interoperability. 

These findings underscore the need for a systemic reset in how MaaS ecosystems are approached, highlighting the importance of strategic collaboration and innovative solutions to achieve sustainable and profitable urban mobility.

In this first comprehensive analysis, we examine the global landscape of existing MaaS solutions. We developed an AI-based Natural Language Processing (NLP) methodology to identify trends and essential constructs within MaaS ecosystems, such as the overarching value proposition, the technical platform, value drivers and governance mechanisms. Furthermore, we delve into the specifics of the operating model of actors, relationships and governance mechanisms to outline a preliminary blueprint for a profitable MaaS solution of the future, even though there is no one-size-fits-all solution to address the multifaceted challenges within MaaS ecosystems.

The MaaS-NLP (Natural Language Processing (NLP) Dataset and blueprint serve as first valuable resources for understanding the current MaaS landscape, several concepts for making MaaS ecosystems work and their future trajectory. We therefore offer a foundational steppingstone for further enquiries and discussions in this fast-paced, ever-changing field, while also acting as a roadmap for the next generation of MaaS providers.