AI Maturity & Evaluation

Systematically assessing AI system advancement, capability, performance, fairness, safety, and societal value through standardized frameworks and collaborative evaluation.

The Reality Check

The Investment vs. Impact Gap

Despite massive investment in generative AI, its real-world impact to date remains modest, with limited economically transformative applications and persistent issues like weak reasoning, data bias, and lack of explainability.

5%
of tasks will be profitably automated in next decade
1%
expected addition to global GDP from AI
47
Active AI standards projects in IEEE alone

The Goal: Human Augmentation, Not Replacement

Experts stress that the goal of AI should not be blind automation, but rather using AI to augment human capabilities in areas like healthcare, education, and climate resilience. True success means aligning AI capabilities with human well-being.

OpenAI's 5-Level AI Maturity Framework

1

Chatbots

AI with conversational language

2

Reasoners

Human-level problem solving

3

Agents

Systems that can take actions

4

Innovators

AI that can aid in invention

5

Organizations

AI that can do work of an organization

Currently, most AI systems operate at Level 1-2, with significant challenges in reasoning, decision-making, and autonomous action.

Current AI Challenges

Business Viability

Massive investment in GenAI far exceeds current revenue, with no breakout applications yet to justify the scale of investment.

Technical Limitations

LLMs face serious issues with data privacy, weak reasoning abilities, and lack of transparency in decision-making processes.

Lack of Standards

AI development is fragmented with minimal top-down coordination, slowing progress and industrial cohesion.

IEEE AI Levels Working Group (P3514)

Mission

Foster a collaboration ecosystem for evaluating AI benefits for human well-being through standardized capability levels, evaluation criteria, and benchmarks.

Key Components:

Valuation Framework for AI's contribution to human well-being
AI Lifecycle Capabilities assessment
Real-World Scenarios for high-impact use cases
Maturity Index for evaluating AI technologies

The Challenge

Standardized evaluation frameworks are essential but currently fragmented, making comparisons difficult and slowing progress. The effectiveness of benchmarks largely depends on their standardized approach and application.

Current Issues

  • • Different proprietary AI benchmarks
  • • Inconsistent testing methodologies
  • • Lack of standardization in evaluation
  • • Fragmented compliance requirements

International AI Testing Collaboration

Fundamental Testing Challenges

Complexity

AI testing is challenging because AI systems, unlike traditional software, do not rely solely on a fixed environment.

Decision Explainability

A key challenge is to explain the decisions made by AI systems, ensuring transparency without overcomplicating.

Core Testing Purposes

Regulatory Compliance:

Ensure systems meet legal requirements

Output Consistency:

Validate reliable results under similar conditions

Scenario Identification:

Define context-specific test scenarios

Metric Selection:

Identify appropriate evaluation metrics

Collaborative Requirements

Effective AI testing demands collaboration across stakeholders, thoughtful policy development, and investment in training and open-source tools.

Global Stakeholders

Balance input from global stakeholders and those directly impacted by AI decisions.

Context-Aware Standards

Determining acceptable performance depends on application context and use cases.

Capacity Building

Training people in AI testing to address existing gaps in expertise and tools.

Shape AI Evaluation Standards

Join the global effort to create comprehensive, standardized frameworks for evaluating AI maturity and ensuring responsible development.

Join Maturity Discussion IEEE AI Levels Working Group