AI Ready – Towards a standardized readiness framework

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AI Ready – Towards a standardized readiness framework

Artificial Intelligence (AI) has become a pervasive tool in multiple facets of human life, showcasing an extensive array of applications and solving numerous real-world challenges through innovative algorithms and increased computing availability. 

While the rewards of AI integration are clear, integrating it within societal and professional areas presents numerous hurdles. What are the impactful enablers for AI integration? How can we make the benefits of AI more accessible to different sections of society? Are there standardized frameworks which can help us to measure the level of AI integration? How do we make sure that societies are ready for AI integration, while keeping our focus on practical benefits?  

The analysis of use cases related to (a) traffic safety and (b) agriculture may serve as an example. One of many use cases within the domain of traffic safety is pedestrian safety. Studying the different actors (vehicles, sensors, roadside units, networks, controllers) and characteristics (regulations, infrastructure, technology, interoperability, human factors, data types, data handling) for pedestrian safety allows to find common patterns, metrics, and evaluation mechanisms for the integration of AI in the domain of traffic safety. Similarly, analysis on use cases related to smart irrigation in the domain of agriculture helps us to generalize this study across various domain scenarios. The goal is to develop a framework assessing AI readiness to indicate the ability to reap the benefits of AI integration. Efforts could then be extended to scale this research across different regions of the world and other domains and use cases.  

This case-based approach has the advantage of studying the challenges of AI integration from various perspectives, ensuring that our findings are rooted in practical scenarios. This approach encourages dialogue among researchers in various fields around AI integration and precipitates requirements for a standard framework for measuring and enabling AI readiness for future use cases. 

Standard frameworks may (a) offer clear metrics for measuring readiness levels in terms of enabling factors, (b) empower organizations, regions, and countries to evaluate their preparedness to benefit from AI effectively, (c) study the various risk factors in simulated scenarios so as to make informed decisions and (d) apply regional and domain specific preferences while deploying AI based solutions. 

The approach requires a collaborative effort among domain experts analyzing the specific characteristics of use cases, regional actors understanding the local preferences and priorities, and data scientists simulating various scenarios for potential risks vs. benefits. 

The workshop will describe case studies with a focus on characteristics, potential metrics, and measurement mechanisms.  

Partner studies explaining ongoing research or industry studies in specific domains while integrating AI in use cases, with a clear focus on metrics, frameworks and standards for AI readiness.

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