Is Siloed Data Sabotaging Your AI ROI?

Artificial Intelligence

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In 1973, Motorola engineer Martin Cooper placed the very first public call from a handheld portable cell phone to his counterpart at competitor Bell Labs. This was a groundbreaking moment in the history of technology, but it still took decades before cell phones advanced from brick-like, single-purpose devices to the powerful smartphones we rely on today. Unlike cell phones, artificial intelligence (AI) has advanced much faster, compressing decades of progress into just a few years, with coinciding, rapid adoption. In fact, according to a recent survey from my company, Prosper Insights & Analytics, 54% of executives and business owners now use generative AI applications, such as ChatGPT and CoPilot.

Prosper – Heard of Generative AI

Prosper Insights & Analytics

Basic task automation has quickly evolved into sophisticated applications generating human-like text, images, code, and more. Advancements in large language models (LLMs), cloud computing, and data availability have only accelerated innovation. With countless more applications on the horizon, business leaders are looking to profit from the potential of AI to automate workflows, improve decision-making, and uncover new revenue streams.

But beneath the surface of this transformation lies a growing, multi-faceted challenge. As AI adoption accelerates, so does the volume and complexity of data it generates. With global data creation projected to exceed 220 zettabytes in 2026 and AI-generated content on track to surpass human-created content, businesses are struggling to effectively use all of the data they’re generating. Research from Gartner highlights this gap between AI ambition and reality with less than 30% of AI leaders reporting their CEOs are happy with their AI investment return.

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