AI
Explore practical insights, research, and benchmarks on artificial intelligence, including generative AI, large language models, RAG, governance frameworks, MLOps practices, and AI hardware. Gain an understanding of key tools, implementation strategies, and enterprise use cases shaping the AI landscape.
Supervised Fine-Tuning vs Reinforcement Learning
Can large language models internalize decision rules that are never stated explicitly? To examine this, we designed an experiment in which a 14B parameter model was trained on a hidden “VIP override” rule within a credit decisioning task, without any prompt-level description of the rule itself.
10 Risks of Generative AI & How to Mitigate Them
With industries prioritizing generative AI for innovation and automation, its potential grows. However, risks of generative AI like accuracy and ethical concerns remain. Addressing these challenges is key to ensuring AI benefits humanity. Explore the top 10 risks of generative AI and steps to mitigate them: Model reliability & output integrity risks 1.
Top 7 Open-Source Vector Databases: Faiss vs. Chroma
As AI Agents and models increasingly rely on high-dimensional data retrieval, selecting an open-source vector database becomes critical for enterprise deployment.
Large Language Model Training
Integrating existing LLMs into enterprise workflows is increasingly common. However, some enterprises develop custom models trained on proprietary data to improve performance for specific tasks. Building and maintaining such models requires significant resources, including specialized AI talent, large training datasets, and computing infrastructure, which can increase costs to millions of dollars.
Compare Google Dialogflow and Its Competitors
Tech giants such as Google, IBM, Microsoft, Amazon, and Facebook are investing in conversational AI to enable developers to build chatbots easily. These AI-powered chatbots can automate various routine tasks such as sending emails, searching for information on search engines, etc.
Enterprise AI Companies: Landscape Breakdown in 2026
Artificial intelligence is revolutionizing every industry with various use cases. Demand for AI products grows as more companies shift their legacy systems to digital products to survive in the competitive business landscape. However, the AI vendor landscape is crowded, and most executives or decision-makers have limited knowledge of the AI landscape.
CPFR: TOP 21 Tools, 6 Case Studies & 5 Benefits
The global market for demand planning solutions, including CPFR (collaborative planning, forecasting, and replenishment) software is growing with the need for real-time data sharing, cloud platforms, and AI-driven forecasting to build more integrated and resilient supply chains.
Generative AI in Retail: 7 Use Cases & Examples
Retail businesses strive to enhance customer experiences and loyalty. This requires producing attractive content in various formats, effective marketing efforts, and exceptional customer service. With generative AI, retailers can address most of these issues through automation, particularly by enhancing their ability to analyze customer data to deliver more personalized experiences.
Generative AI ERP Systems: 10 Use Cases & Benefits
Enterprise resource planning (ERP) software helps businesses see the process across different departments so they can make smarter decisions faster. Generative AI, alongside technologies like RPA, has the potential to enhance ERP processes.
Enterprise Generative AI: 11 Use Cases & Best Practices
Generative AI (GenAI) presents novel opportunities for enterprises compared to middle-market companies or startups, including: However, generative AI brings challenges unique to large organizations. For example: Explore our practical enterprise AI use cases to learn how large companies can build, deploy, and govern their own generative AI models effectively.