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.
Specialized AI Models: Vertical AI & Horizontal AI in 2025
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.
Image Recognition vs Classification: Applications with Examples
Businesses increasingly leverage AI-powered visual data solutions, but confusion between image recognition and classification leads to inefficiencies. Understanding the key differences helps businesses optimize AI deployment in the security, healthcare, and retail fields. Explore image recognition vs classification, their key differences, and applications with real-life examples.
Top 10 Strategies for AI Improvement with Real Life 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.
Top 4 Facial Recognition Challenges & Solutions in 2025
From unlocking your smartphone to catching fugitives, facial recognition technology (FRT) is everywhere. Forecasts suggest that this growth in facial recognition usage and development will only rise in the future. While many businesses successfully leverage the technology, they probably went through various hurdles to develop or implement it.
5 AI Training Steps & Best Practices in 2025
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.
AI Center of Excellence (AI CoE): Meaning & Setup in 2025
The adoption of artificial intelligence (AI) is increasing as companies try to capture value from enterprise AI applications. However, according to an IBM survey, challenges such as limited AI expertise, increasing data complexity, and lack of tools for AI development hinder AI adoption for enterprises. To reduce AI project failures, organizations need a dedicated unit to oversee AI initiatives.
What is Composite AI? Business Impact in 2025
Artificial intelligence (AI) has opened new capabilities for businesses with a diverse set of use cases across sectors. 78% of organizations now use AI in at least one function Composite AI integrates multiple AI techniques into unified solutions that can address complex business problems more effectively than any single approach.
Top 6 Applications of Machine Learning in Process Mining
For more than a decade, machine learning has been applied to traditional process mining . Today, many vendors claim to offer AI-powered process mining software which leverages machine learning.
AI Fail: 4 Root Causes & Real-life Examples in 2025
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.
No-Code AI: Benefits, Industries & Key Differences in 2025
Many small businesses lack the necessary technical resources to implement AI effectively. No-code AI directly addresses this gap by enabling rapid AI deployment without requiring coding skills. Ideal for small businesses, entrepreneurs, and professionals in fields like marketing, education, or healthcare, no-code AI tools help simplify testing, integration, and iterative improvements.