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Untangling the Gordian Knot: The HFS Dummies’ Guide to Enterprise AI
AI is one of the most hyped, yet truly most valuable, technologies available to enterprises as they look to optimize and future-proof their businesses. Any form of AI is powered by algorithms, formulaic code that instructs the software how to process and learn from input data. These algorithms can only learn by ingesting vast quantities of data and then deriving patterns from it. Algorithms use these patterns and implicit instructions to interpret future data, which can be internal company data or external market data.
AI’s value and the excitement it inspires lie in its ability to process data in far greater volumes, at far greater speed, and with the promise of infinitely better accuracy, than humans can. By design, AI doesn’t just mimic human analytical capabilities; it outstrips them. As such, organizations are actively looking at AI to drive cost savings by cutting down on FTEs, to better leverage their internal and external market data to generate deeper competitive insights, and to equip themselves to become predictive rather than reactive businesses. Multiple studies show that AI is already better than humans at certain tasks, for example predicting turnover rates and profiling customers. Further testimony to AI’s power are the funding pouring into the sector and the rate at which buyers are snapping up AI start-ups.
There’s a fundamental issue stopping many enterprises from unlocking AI’s full potential. People tend to discuss AI as if it is a monolith or single category, when in fact there are many specialized AI subcategories for specific tasks. For example, machine learning (ML) doesn’t have the semantic understanding of a sophisticated natural language processing (NLP) system, and even the best NLP system wouldn’t be well equipped to identify images with the precision of a computer vision engine. If these distinctions are elided—as they so often are—then enterprises won’t find the AI technology best suited to address their unique needs.
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