Page 9 - Preliminary Analysis Towards a Standardized Readiness Framework - Interim Report

Preliminary Analysis Towards a Standardized Readiness Framework

1. Executive Summary

This report provides a preliminary analysis of the Artificial Intelligence (AI) readiness study, the
goal of which is to develop a framework assessing AI readiness to indicate the ability to reap
the benefits of AI integration. By studying the different actors and characteristics in different
domains, the bottom-up approach allows us to find common patterns, metrics, and evaluation
mechanisms for the integration of AI.

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 domain-specific research and advanced 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. Examples of physical infrastructure are speed bumps, barricades, banners, and
advertisements for speed control (see clause 4.1.15) in the context of transportation safety. Or
a greenhouse, artificial light, and air moisturizer (see clause 4.1.12) to provide an appropriate
environment for plants in agricultural settings. Physical infrastructure elements play an important
role in the integration and application of AI not only in data collection, aggregation - at the
edge or core, training – federated or centralized, or application of Artificial Intelligence and
Machine Learning (AI/ML) inference via actuators.

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 requires 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.

Developer ecosystem bootstraps reference implementations of algorithms, with baseline
and open-source toolsets. Third-party applications, Application Programmer Interfaces
(API), and Software Development Kits (SDK) along with crowd-sourced solutions increase

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