The Latest in Data and AI
Category: Artificial Intelligence
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How to Build an AI-Ready Data Foundation
The rise of large language models (LLMs) has given some businesses the impression that training data is no longer a critical factor. After all, these models come pretrained with vast amounts of knowledge. However, to gain a true competitive edge, companies need AI that understands their specific domain, customers, and internal processes—which means high-quality, proprietary…
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UX: The missing ingredient to AI Adoption
In the race to integrate AI into businesses, many leaders focus on state-of-the-art language models, advanced prompt engineering, and fine-tuned Retrieval-Augmented Generation (RAG) systems. While these are undeniably crucial elements, there’s a factor often overlooked—User Experience (UX) Design. The most powerful AI tool will fail to deliver impact if users don’t integrate it into their…
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Key Insights from The State of Generative AI, 2024
Generative AI is reshaping business landscapes as we approach 2024. Forrester’s latest report unveils crucial insights for decision-makers. Here’s a concise breakdown: Top Insights 1. AI Adoption Trends: 2. Business Impact: 3. Key Use Cases: 4. Challenges: Strategic Recommendations: By addressing these challenges head-on, organizations can effectively harness the power of generative AI while ensuring…
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Why AI/ML Projects Fail: A Practical Guide for High-Level Executives
Artificial Intelligence (AI) and Machine Learning (ML) have been hailed as transformative technologies that can unlock new business opportunities, drive operational efficiencies, and generate significant ROI. Despite the hype, a substantial number of AI/ML initiatives fail to see completion or fail to deliver the expected value. For decision-makers, CXOs, and VPs, understanding why these projects…
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Where is Your Organization in the Data Science Hierarchy of Needs?
Introduction In today’s data-driven world, businesses are increasingly relying on data science to gain insights and make informed decisions. However, the application of data science within an organization is not a one-size-fits-all approach. Understanding where your organization stands in the Data Science Hierarchy of Needs is crucial for optimizing processes and making strategic advancements. This…