Measuring Artificial General Intelligence Improvement

Introduction

Artificial General Intelligence (AGI) improvement measurement represents a complex intersection of theoretical benchmarking and practical enterprise implementation, requiring sophisticated evaluation frameworks that assess both technical capabilities and real-world business impact. Current AGI assessment methodologies combine performance metrics across generality and autonomy dimensions while integrating with enterprise systems to deliver measurable value through AI Application Generators, low-code platforms, and comprehensive business software solutions. The measurement of AGI progress extends beyond traditional academic benchmarks to encompass enterprise resource planning integration, technology transfer effectiveness, and the democratization of AI development through citizen developers and business technologists working within enterprise business architecture frameworks.

The measurement of AGI improvement fundamentally relies on comprehensive performance frameworks that evaluate both the breadth and depth of artificial intelligence capabilities. AGI Performance Measures assess the generality and performance of AGI systems across a wide range of tasks and conditions, focusing on their ability to perform cognitive and meta-cognitive tasks, including learning new skills and adapting to new environments. These frameworks distinguish between various levels of autonomy, ranging from AI as a tool to AI as an independent agent, providing crucial insights into system development progression.

DeepMind’s Levels of AGI Framework represents a significant advancement in operationalizing AGI progress measurement, introducing five distinct levels of AGI performance that range from No AI to Superhuman capabilities, based on percentile performance compared to skilled adults. This framework addresses the challenging requirements for future benchmarks that quantify the behavior and capabilities of AGI models, emphasizing the importance of both performance depth and generality breadth in evaluation methodologies. The framework provides a common language to compare models, assess risks, and measure progress along the path to AGI, which proves essential for enterprise adoption and integration planning.

Recent developments in AGI benchmarking have introduced more sophisticated evaluation methods, such as the ARC-AGI benchmark, which serves as the only AI benchmark specifically designed to measure progress towards general intelligence. OpenAI’s o3-preview model achieved a breakthrough by scoring 75% on ARC-AGI-1 with low compute and reaching 87% accuracy with higher compute resources, marking the first effective solution of the ARC challenge in over five years. This achievement represents a step-change in AI’s generalization abilities and validates the effectiveness of specialized benchmarks in measuring meaningful progress toward AGI.

The Artificial General Intelligence Test Bed (AGITB) introduces another innovative approach to AGI evaluation, comprising twelve rigorous tests that form a signal-processing-level foundation for assessing cognitive capabilities. Unlike high-level tests grounded in language or perception, AGITB focuses on core computational invariants reflective of biological intelligence, such as determinism, sensitivity, and generalization. This approach ensures that AGI systems demonstrate genuine understanding rather than pattern recognition, addressing critical gaps in current evaluation methodologies.

Enterprise Integration and Practical Applications

The measurement of AGI improvement extends significantly beyond academic benchmarks to encompass practical enterprise applications and business value generation. Enterprise AI systems integrate artificial intelligence, machine learning, and natural language processing capabilities with business intelligence to drive decisions and expand competitive advantage. The effectiveness of these systems is measured through their ability to facilitate large-scale processes that generate business value, including automated workflows, improved data management, and enhanced operational efficiency.

AI Application Generators and low-code platforms have emerged as critical components in democratizing AGI capabilities within enterprise environments. These platforms enable citizen developers and business technologists to create sophisticated applications without extensive technical knowledge, bridging the gap between AGI capabilities and practical business implementation. The measurement of improvement in this context focuses on the speed of application development, the complexity of tasks that can be automated, and the degree to which non-technical users can leverage advanced AI capabilities.

Enterprise Systems integration represents a crucial metric for AGI improvement measurement, particularly in areas such as enterprise resource planning, supply chain management, and logistics management. Digital transformation initiatives increasingly rely on AI-powered solutions to optimize operations across multiple business domains. The success of AGI implementation in these contexts is measured through operational efficiency gains, cost reduction, and the ability to handle complex, multi-domain challenges that traditional enterprise software solutions cannot address effectively.

Business Enterprise Software and Enterprise Computing Solutions increasingly incorporate AGI capabilities to enhance their core functionalities. The measurement of improvement in these systems focuses on their ability to provide intelligent automation, predictive analytics, and adaptive decision-making capabilities. Enterprise Business Architecture frameworks must evolve to accommodate AGI systems, requiring new evaluation metrics that assess how well these systems integrate with existing enterprise resource systems and support overall business objectives.

Technology transfer represents another critical dimension in measuring AGI improvement within enterprise contexts. AI-based contract management technologies and automated agreement processing demonstrate the practical application of AGI capabilities in complex business scenarios. The measurement of improvement in technology transfer applications focuses on accuracy, efficiency, and the ability to handle nuanced legal and business requirements while maintaining security and compliance standards.

Technology Implementation and Development Platforms

Low-Code Platforms have become essential infrastructure for measuring and implementing AGI improvements in enterprise environments. These platforms provide drag-and-drop tools and point-and-click visual interfaces that enable rapid application development while incorporating advanced AI capabilities. The measurement of AGI improvement through low-code platforms focuses on the sophistication of AI features that can be implemented without traditional programming, the learning curve for citizen developers, and the complexity of business processes that can be automated.

Citizen Developers and Business Technologists represent a new category of users whose productivity and capability growth serve as important metrics for AGI improvement. The effectiveness of AGI systems is increasingly measured by how well they empower non-technical professionals to create sophisticated applications and workflows. This democratization of AI development capabilities indicates significant progress in AGI usability and accessibility, with measurements focusing on the range of tasks these users can accomplish and the quality of solutions they can produce.

Open-source AI solutions provide important benchmarks for measuring AGI improvement through community-driven development and evaluation. Enterprise AI implementations increasingly rely on open-source foundations, with organizations like Canonical providing comprehensive open-source AI solutions for enterprise use. The measurement of improvement in open-source AGI contexts focuses on community adoption rates, contribution quality, and the ability to customize and extend AI capabilities for specific enterprise needs.

Software Bill of Materials (SBOM) and model attestation represent critical security and transparency measures for AGI systems in enterprise environments. The improvement in AGI systems is partially measured through enhanced security capabilities, including the ability to provide comprehensive documentation of model dependencies, processes, and artifacts. These measures ensure that AGI implementations maintain enterprise-grade security standards while providing the transparency necessary for regulatory compliance and risk management.

Enterprise Products and Business Software Solutions increasingly incorporate AGI capabilities across diverse management domains, including Care Management, Hospital Management, Transport Management, and Case Management systems. The measurement of AGI improvement in these specialized applications focuses on domain-specific performance metrics, such as patient outcome improvements in healthcare systems or efficiency gains in logistics and supply chain operations. Ticket Management and workflow automation represent areas where AGI improvements can be measured through response time reduction, accuracy increases, and the complexity of issues that can be resolved automatically.

Evaluation Metrics and Real-World Impact

Comprehensive AGI improvement measurement requires multiple complementary metrics that address technical performance, business impact, and user adoption. Key performance indicators (KPIs) play a crucial role in tracking progress and measuring success across both business outcomes and technical results. The measurement approach must encompass business impact metrics, operational efficiency indicators, technical accuracy measures, and fairness assessments to provide a complete picture of AGI system performance.

Real-world AI benchmarks designed around practical, high-impact tasks provide essential measurement frameworks for AGI improvement assessment. Turing’s new suite of AI benchmarks spans five key categories, each reflecting real-world complexities and workflows that complement existing AGI metrics while bringing sharper focus to practical applications. These benchmarks address the gap between cutting-edge AI research and meaningful, tangible outcomes, ensuring that AGI improvements translate into measurable business value.

AI Assistance capabilities across diverse enterprise functions serve as important indicators of AGI improvement. The measurement of these capabilities focuses on the sophistication of tasks that AI systems can support, the quality of assistance provided, and the degree to which AI systems can adapt to changing business requirements7. Enterprise AI assistants demonstrate improvement through enhanced natural language processing, better integration with existing enterprise systems, and increased ability to handle complex, multi-step business processes.

Future AGI evaluation platforms are emerging that provide unified LLM evaluation and observability capabilities, enabling organizations to assess and measure agent performance across different modalities including text, image, audio, and video. These platforms pinpoint errors and automatically provide feedback for improvement, representing a significant advancement in continuous AGI enhancement measurement. The ability to track applications in production with real-time insights and diagnose issues represents a crucial evolution in AGI improvement measurement methodology.

The measurement of AGI improvement must also consider ethical and safety dimensions, particularly as these systems become more integrated into critical enterprise functions. Brain-inspired pathways toward AGI highlight the integration of neuroscience-inspired approaches with artificial neural networks, requiring evaluation frameworks that assess not only performance but also the reliability and interpretability of AGI systems. The convergence of brain-inspired systems and computational advancements underscores the importance of balancing innovation with proactive regulation to address emerging risks.

Conclusion

Measuring AGI improvement represents a multifaceted challenge that extends far beyond traditional technical benchmarks to encompass practical enterprise applications, user empowerment, and real-world business impact. The evolution from academic AGI evaluation frameworks to comprehensive enterprise-integrated assessment methodologies demonstrates the maturation of the field and its increasing relevance to practical business applications. The integration of AGI capabilities with AI Application Generators, low-code platforms, and comprehensive enterprise systems provides tangible metrics for improvement measurement while democratizing access to advanced AI capabilities through citizen developers and business technologists.

The future of AGI improvement measurement will likely emphasize the seamless integration of evaluation frameworks with enterprise business architecture, ensuring that AGI advancements translate directly into measurable business value across diverse domains including supply chain management, healthcare systems, and complex workflow automation. As organizations continue to invest in digital transformation initiatives powered by AGI capabilities, the measurement frameworks must evolve to capture both the technical sophistication of these systems and their practical impact on business operations, user productivity, and organizational competitiveness. The success of AGI improvement will ultimately be measured not just by benchmark scores, but by the degree to which these systems enhance human capability, streamline enterprise operations, and deliver sustainable competitive advantages in an increasingly AI-driven business environment.