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8 articles
This article explains the 'AI gap' – the difference between having an AI strategy and actually implementing it in business operations. Learn why this gap exists and why it matters for companies investing in AI.
Enterprise AI initiatives are stalling because organizations treat language like structured data, ignoring the need for robust content governance. Rob Hanna argues that effective content management is key to successful AI deployment.
AI agents require a strong data foundation to function effectively. Xebia's global CTO, Niels Zeilemaker, emphasizes that without proper data infrastructure, even advanced AI systems can fail.
A Bain study reveals that most companies miss their AI cost-savings targets due to human interference, with only 7% operating truly autonomous AI systems.
Day two of TechEx North America explored the challenges and opportunities of enterprise AI adoption, emphasizing the need for realistic implementation strategies and enhanced security measures.
This explainer explores the productivity gap in AI, examining how benchmark gains fail to translate into real-world economic impact due to verification overhead, metrics mismatches, and organizational barriers.
Learn how to create a cross-functional AI implementation framework that goes beyond just appointing a Chief AI Officer, following the 'magician' leadership approach that successful organizations use to maximize generative AI potential.
Poor implementation of AI may be behind workforce reduction, according to Datatonic. The consultancy warns that enterprises must adopt carefully designed human-AI collaboration to avoid undermining productivity and efficiency.
