Page 8 - AI Ready – Analysis Towards a Standardized Readiness Framework

AI Ready – Analysis Towards a Standardized Readiness Framework

1 Executive Summary

This report provides an analysis of the Artificial Intelligence (AI) Readiness study aimed at
developing a framework for assessing AI Readiness which indicates the ability to reap the
benefits of AI integration. By studying the actors and characteristics in different domains,
a bottom-up approach is followed which allows us to find common patterns, metrics, and
evaluation mechanisms for the integration of AI in these domains.

The analysis of characteristics of use cases led us to the main AI readiness factors:
1) Availability of open data

The availability of data is crucial in training, modeling, and applications of AI irrespective of
the domain. Data availability for analysis may be private or public. Metadata for private data
may be published (e.g. data types and structures). However, public data, open for analysis by
anyone, requires cleaning and anonymization to remove confidential or personal information.

2) Access to Research

Balancing the two main aspects of research, namely advancements in domain-specific
research and advancements in AI research requires collaboration between domain experts
and AI researchers. Providing a platform for collaboration with experts from different realms
of knowledge, facilitating cooperation, and exchange of information among them is key to
creating a sustainable ecosystem for AI-based innovation.

3) Deployment capability along with Infrastructure

Two major categories of infrastructure are studied – physical infrastructure and communication
infrastructure. Considering the context of transportation safety, examples of physical
infrastructure are speed barriers and other regulatory mechanisms for speed control (see
clause 4.2.4). Other examples are greenhouses, moisturizers (see clause 4.3.6), and sensors
that provide an appropriate environment and monitor plants in agricultural use cases. Physical
infrastructure elements play an important role in the integration and application of AI in data
collection, aggregation - at the edge or core, training – federated or centralized, and in the
application of Artificial Intelligence and Machine Learning (AI/ML) inference using actuators.

In addition, there is backend infrastructure, such as compute availability, storage availability,
fiber/wireless availability for the last mile, and high-speed wide area network capabilities, which
would democratize AI/ML solutions and create scalability for innovations.
4) Stakeholders buy-in enabled by Standards – trust, interoperability, security

Interoperability and compliance with standards build trust. Secure standards lead to AI Readiness,
as global participation and consensus decide whether pre-standard research could be adopted
into the real world. Vendor ecosystems, including open source, are diverse in different domains
of use cases. Going back to transportation use cases, for example, pedestrian safety and driver
safety are important considerations. Adoption of AI-based solutions that involve humans such
as pedestrians and drivers require their trust and perception of using AI-based solutions.

5) Developer Ecosystem created via Opensource

An energized third-party developer ecosystem not only fast-tracks adoption but also enables
revenue generation.

1