Through a series of 50-plus interviews with experts across academia, city government, civic organizations, nonprofits, and business, we have identified four broad vectors that are critical for success in meeting the increased speed and scope of technology disruption brought by AI.  We use the term vector deliberately: A vector has direction as well as magnitude, which makes it useful in determining relative positioning. Cities are in motion as they meet new technologies – and our research has tried to balance present standing with potential progress. 

These vectors manifest differently among cities of different size and different cultural, social, and civic models, but they point toward improvement regardless of individual characteristics.

Vision, Priorities, and Mindset

Does the city demonstrate a good understanding of the potential opportunities and risks around technology disruption and does it have a systematic and integrated plan meeting the challenges?


Are the city and its stakeholders well-positioned to carry out forward-looking plans, including the cross-stakeholder collaboration essential to governance?

Asset Base

Does the city have existing assets that will act as enablers to support enacting its vision? For example, does the city have a reservoir of talent in colleges and universities, an educated workforce, high-quality STEM education in primary and tertiary education, a track record for innovation and attracting pioneering companies, and the necessary infrastructure?

Trajectory and Development

Is the city trending in the right direction? In recent years, has the city seen itself improving its ability to execute, and have its assets become better aligned with what is needed to succeed in the future?

Each of these categories contains multiple sub-dimensions that are themselves composed of several individual metrics and data points. Our assessment considers as broad a range of relevant factors as possible and weights them appropriately given their impact on a city’s state of readiness.

In addition, these vectors do not exist in isolation.  All the cities we researched have advantages and disadvantages along each of these vectors: Some have inherited assets that they have leveraged (or need to leverage), while others need to acquire them or make up for their absence; some have the capability of commanding unity among stakeholders to achieve their goals, while others struggle with building consensus among their constituents.  How cities are able to advance along these metrics, while at the same time managing the other important aspects of city life and health, will have a significant impact on how they fare in the coming age of AI.

The methodology employs a variety of city-level data (and country-level data, where appropriate) drawn from both public and private sources, as well as Oliver Wyman Forum proprietary analyses. Methodologies for data standardization, normalization, and weighting are all proprietary.

Data Sources

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