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.
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 Center of Excellence (AI CoEs): Real-Life Examples
Across industries, organizations use AI Center of Excellence (AI CoEs) to solve practical problems such as scaling AI initiatives, enforcing governance, reducing duplication, and connecting AI work to measurable business outcomes. Explore what an AI Center of Excellence is, why organizations set one up, and how it operates in practice.
100+ AI Use Cases with Real Life Examples in 2026
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.
No-Code AI: Benefits, Industries & Key Differences in 2026
No-code AI tools allow users to build, train, or deploy AI applications without writing code. These platforms typically rely on drag-and-drop interfaces, natural language prompts, guided setup wizards, or visual workflow builders. This approach lowers the barrier to entry and makes AI development accessible to users without a programming background.
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.
20 Strategies for AI Improvement & Examples
AI models require continuous improvement as data, user behavior, and real-world conditions evolve. Even well-performing models can drift over time when the patterns they learned no longer match current inputs, leading to reduced accuracy and unreliable predictions.
Top 5 Facial Recognition Challenges & Solutions
Facial recognition is now part of everyday life, from unlocking phones to verifying identities in public spaces. Its reach continues to grow, bringing both convenience and new possibilities. However, this expansion also raises concerns about accuracy, privacy, and fairness that need careful attention.
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.
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
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.