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AI is quietly reshaping how people discover products online, and the mechanism behind it is surprisingly human. When a recommendation comes from a source that already knows your preferences and budget, the psychological barrier to buying drops significantly. Retailers are taking note, and AI-driven product suggestions are fast becoming their most valuable source of inbound traffic.
That momentum in AI adoption, though, is running headlong into a sobering reality check. Ninety-five percent of enterprise AI pilot programs failed to produce measurable financial returns last year. The emerging consensus among analysts is that the missing ingredient isn't smarter models — it's richer transaction data. Without grounding AI in real purchasing behavior, the returns simply don't materialize.
On a different but related note, Dartmouth's Low Resource Computing initiative for two thousand twenty-six is drawing quiet attention in academic circles. The project explores how meaningful computation can happen at the edges of hardware capability — a question that matters enormously as AI workloads demand more power than most organizations can afford or justify.
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