"Before AI became every board's obsession, enterprises spent a decade building analytics infrastructure. These systems handled overnight reports just fine because the economics worked: smaller data volumes, predictable workloads, and manageable costs. AI changed all that. Overnight runs became continuous processing. Sample data became complete datasets. Batch jobs became real-time inference. The analytics-era infrastructure simply can't sustain AI's pace and cost demands."
"Roughly a quarter of enterprise cloud spend is wasted on inefficient resource use, much of it tied to data processing. For a company spending $100 million a year on cloud services, that's tens of millions burned - money that could fund real AI innovation. The irony is companies are spending tens of billions of dollars on database and analytics infrastructure, yet starving the one layer that actually makes AI economically viable."
"The failure isn't happening where most executives think it is. I spent two decades at Microsoft and SAP watching enterprises make the same mistake: optimizing the wrong layer of the technology stack. Today's AI failures follow that same pattern. Companies chase the newest models and flashiest applications while the data infrastructure beneath them quietly buckles. Few can process data fast or cheaply enough to feed these models at scale."
Enterprises invested $30 billion–$40 billion in generative AI pilots in 2024, while an influential MIT study found 95% delivered zero measurable business return, equating to roughly $30 billion in destroyed shareholder value. Failures stem from optimizing models and applications instead of the underlying data infrastructure. Analytics-era systems built for overnight batches cannot support continuous processing, full-dataset training, and real-time inference. About a quarter of enterprise cloud spend is wasted on inefficient data processing, and many firms process only 20–30% of available data because processing everything would multiply compute budgets five- to tenfold. Companies spend heavily on databases yet underfund the layer that makes AI economical.
Read at Fortune
Unable to calculate read time
Collection
[
|
...
]