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.
5 AI Training Steps & Best Practices
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.
100+ AI Use Cases with Real Life Examples
During my ~2 decades of experience of implementing advanced analytics & AI solutions at enterprises, I have seen the importance of use case selection. I analyzed 100+ AI use cases, their real-life examples and categorized them by business function and industry.
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
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
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.
Explainable AI (XAI): Guide to enterprise-ready AI
As AI tools become more advanced, more computations are done in a “black box” that humans can hardly comprehend. This approach is problematic since it prevents transparency, trust and model understanding. After all, people don’t easily trust a machine’s recommendations that they don’t thoroughly understand.
AI Explained: Trends and Applications by Industry
Artificial Intelligence (AI) allows computers to learn from experience, adapt to new inputs and perform human-like tasks. Most of the AI examples you’ve heard about –from chess-playing machines to self-driving cars–rely heavily on deep learning, a subfield of AI.
Lazarus AI: Extractive & On-Prem AI for Regulated Industries
Generating insights from unstructured data has long been a strategic aim among organizations that Lazarus AI builds foundation models (e.g. RikAI) to solve complex and urgent problems involving large amounts of unstructured private data. Lazarus is active in these industries: Government (e.g. defense), insurance (e.g. damage assessment of catastrophic events), healthcare, and banking.