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.
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.
Frugal AI: Principles, Use Cases & Real-life Examples ['26]
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.
Large Quantitative Models: Applications & Challenges ['26]
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.
Time Series Foundation Models: Use Cases & Benefits ['26]
Time series foundation models (TSFMs) build on advances in foundation models from natural language processing and vision. Using transformer-based architectures and large-scale training data, they achieve zero-shot performance and adapt across sectors such as finance, retail, energy, and healthcare.
5 AI Training Steps & Best Practices in 2026
AI can boost business performance, but 85% of AI projects fail, often due to poor model training. Challenges such as poor data quality, limited scalability, and compliance issues hinder success. Check out the top 5 steps in AI training to help businesses and developers train AI models more effectively.
Demand Forecasting in the Age of AI & Machine Learning
Businesses face different inventory challenges when they are dealing with supply chains. Addressing supply chain issues is paramount. Demand forecasting enables businesses to reduce supply chain costs and achieve significant improvements in financial planning, capacity planning, profit margins, and risk assessment decisions.
How to Measure AI Performance: Key Metrics & Best Practices
Measuring AI performance is crucial to ensuring that AI systems deliver accurate, reliable, and fair outcomes that align with business objectives. It helps organizations validate the effectiveness of their AI investments, detect issues like bias or model drift early, and continuously optimize for better decision-making, operational efficiency, and user satisfaction.
AI Fail: 4 Root Causes & Real-life Examples in 2026
Whether it’s a self-driving car crash, a biased algorithm, or a breakdown in a customer service chatbot, failures in deployed AI systems can have serious consequences and raise important ethical and societal questions.
Artificial Superintelligence: Opinions, Benefits & Challenges
The prospect of artificial superintelligence (ASI), a form of intelligence that would exceed human capabilities across all domains, presents both opportunities and significant challenges. Unlike current narrow AI systems, ASI could independently enhance its capabilities, potentially outpacing human oversight and control. This development raises concerns regarding governance, safety, and the distribution of power in society.
Google's AI Strategy and 11 Key Developments in 2026
Google’s current artificial intelligence strategy emphasizes advanced integration of AI models into enterprise solutions, with significant investments focused on improving functionality, accuracy, and efficiency. Check out 10 recent developments for businesses considering implementing Google’s AI tools and platforms into their workflows. 1. Gemini 2.5: Google’s advanced multimodal AI Google DeepMind launched Gemini 2.