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.
Hands-On Top 10 AI-Generated Text Detector Comparison
We conducted a benchmark of the most commonly used 10 AI-generated text detector.
World Foundation Models: 10 Use Cases & Examples ['26]
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.
Top 9 AI Infrastructure Companies & Applications in 2026
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.
AGI Benchmark: Can AI Generate Economic Value in 2026
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.
Deepseek: Features, Pricing & Accessibility in 2026
A Chinese hedge fund spent $294,000 training an AI model that beats OpenAI’s O1 on reasoning benchmarks. Then they open-sourced it. DeepSeek isn’t your typical AI startup. High-Flyer, an $8 billion quantitative hedge fund, funds the entire operation. No venture capital. No fundraising rounds.
AI Scientist: Automating the Future of Scientific Discovery
AI scientists mark a major advance toward fully automatic scientific discovery, aiming to perform the entire research process independently. Unlike traditional tools, these automated labs can expedite research processes by generating hypotheses, designing and executing experiments, interpreting results, and communicating findings.
Custom AI: When to Build Your Own Solutions in 2026
While ready-made AI tools can meet many business needs, they often fall short in areas that require deep data understanding or specialized workflows. Organizations working in complex or niche industries may find that generic systems don’t fully align with their operations or leverage their proprietary data.
10 Steps to Developing AI Systems in 2026
IBM identifies the top AI adoption challenges as concerns over data bias (45%), lack of proprietary data (42%), insufficient generative AI expertise (42%), unclear business value (42%), and data privacy risks (40%).These obstacles can hinder AI implementation, slow innovation, and reduce the return on investment for organizations adopting AI technologies.
Top 5 AI Services to Enhance Business Efficiency in 2026
AI adoption is rapidly increasing. Around 98% of companies are experimenting with AI, reflecting its growing accessibility and potential to improve operations. Yet only 26% have advanced beyond trials to achieve measurable business value, showing that many are still building the capabilities needed to scale AI effectively.
Composite AI: Techniques & Use Cases in 2026
Despite significant investments in generative AI, many organizations are still struggling to demonstrate its tangible business impact. In 2024, companies spent an average of $1.9 million on GenAI initiatives, yet fewer than one in three AI leaders say their CEOs are satisfied with the return on those investments.