AI Foundations
Explore foundational concepts, tools, and evaluation methods that support the effective development and deployment of AI in business settings. This section helps organizations understand how to build reliable AI systems, measure their performance, address ethical and operational risks, and select appropriate infrastructure. It also provides practical benchmarks and comparisons to guide technology choices and improve AI outcomes across use cases.
Top 7 Machine Learning Process Mining Use Cases with GenAI
For more than a decade, machine learning process mining has been used to enhance traditional methods. Today, vendors promote process mining AI with features such as predictive analytics and recent generative AI integrations, but many business leaders still struggle to see how these capabilities translate into practical benefits.
Top 9 AI Infrastructure Companies & Applications
Many organizations invest heavily in AI, yet most projects fail to scale. Only 10-20% of AI proofs of concept progress to full deployment. A key reason is that existing systems are not equipped to support the demands of large datasets, real-time processing, or complex machine learning models.
Frugal AI: Principles, Use Cases & Real-life Examples
From healthcare providers in remote regions to manufacturers optimizing production lines, many industries face limits in budget, infrastructure, and energy use when adopting artificial intelligence. Frugal AI addresses these constraints by prioritizing efficiency, sustainability, and inclusivity, enabling AI systems to deliver measurable value with minimal resources.
AGI Benchmark: Can AI Generate Economic Value
AI will have its greatest impact when AI systems start to create economic value autonomously. We benchmarked whether frontier models can generate economic value. We prompted them to build a new digital application (e.g., website or mobile app) that can be monetized with a SaaS or advertising-based model.
Top 15 Strategies for AI Improvement & Examples
AI systems achieved remarkable milestones (e.g., exceeding human performance in image and speech recognition); however, AI progress is slowing down as scaling yields fewer benefits. Additionally, AI and ML models degrade over time unless they are regularly updated or retrained.This makes it critical to utilize all levers to improve AI models continually.
Hands-On Top 10 AI-Generated Text Detector Comparison
We conducted a benchmark of the most commonly used 10 AI-generated text detector.
Specialized AI Models: Vertical AI & Horizontal AI
While ChatGPT grabbed headlines, the real business value comes from AI built for specific problems. Companies are moving beyond general-purpose AI toward systems designed for their exact needs. This shift is creating three distinct types of specialized AI – each solving different business challenges.
Large Quantitative Models: Applications & Challenges
Modern systems are becoming too complex for traditional statistical analysis, as institutions now handle massive datasets, including patient data, weather data, and financial market data. Large quantitative models (LQMs) help by processing these datasets, integrating structured and unstructured data, and applying predictive modeling to uncover patterns and provide data-driven insights that traditional methods cannot deliver.
Large World Models: Use Cases & Real-Life Examples
Artificial intelligence has advanced significantly with the development of large language models; however, these systems continue to struggle to comprehend and interact with the physical world. Text alone cannot capture spatial relationships, dynamic environments, or the causal impact of actions, thereby limiting progress in fields such as robotics, healthcare, and autonomous systems.
World Foundation Models: 10 Use Cases & Examples
Training robots and autonomous vehicles (AVs) in the physical world can be costly, time-consuming, and risky. World Foundation Models offer a scalable alternative by enabling realistic simulations of real-world environments. These models accelerate development and deployment in robotics, AVs, and other domains by reducing reliance on physical testing.