That is the astonishing reality facing corporate leaders today, but this frustrating paradigm is about to shatter. Technology veterans, like Sisense CEO Ariel Katz—a leader with decades of experience defining innovation at Microsoft—believe a momentous shift is finally underway, promising to unlock AI’s glorious potential for dependable analysis.
This inability to scale AI successfully, where almost 90% of organizations deploy the technology yet only one-third achieve widespread usage, reveals a fundamental flaw—not in the models themselves, but in the organizational plumbing. We are entering an era where AI becomes useful because enterprises are finally fixing something far more essential than processor speed: the meaning layer. Semantics, definitions, governance, and data lineage are not tedious necessities; they are the absolute core of reliable insights.
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McKinsey’s recent State of AI Survey found that a staggering 88% of organizations are actively using AI in some operational capacity, demonstrating unprecedented commitment to this technology, despite facing deployment hurdles.
We must demonstrate deep empathy for the profoundly frustrating journey leaders have endured, deploying expensive, advanced systems only to realize that dependable insights never truly materialized in the daily workflow. The investment was colossal. The return was often negligible. This struggle for dependable intelligence is why organizations are now radically abandoning the pursuit of the mythical universal AI model. Generic systems struggle profoundly with the delicate nuance that actually drives decisive action in complex industries.
This leads to a peculiar and confusing strategic realization: powerful enterprises are now intentionally pursuing smaller, more focused systems. Why would the most sophisticated organizations seek constraints? Because models trained specifically on domain semantics—risk logic for Fintech, diagnostic reasoning for Healthtech, argument structure for Legaltech—consistently deliver superior performance over generalized systems. Specialized knowledge truly dictates utility.
Apparently, these incredibly sophisticated learning algorithms are entirely reliant on basic agreement; if your global enterprise cannot confidently define what ‘customer churn’ signifies, the powerful AI simply shrugs its digital shoulders.
This move toward vertical, specialized AI confirms Gartner’s outlook that context and semantic understanding far outweigh brute computational scale. Risk logic defines success in Fintech. Legal systems demand rigorous precedent mapping. The future involves sharp, specialized AIs working in concert, not one overstretched giant attempting to manage every single complexity in the business universe. This shift is not merely technological; it is an overdue acceptance of reality, promising that reliable, governed insights will soon flow seamlessly into every critical workflow.
Ariel Katz is the CEO of Sisense with 30 years of experience in technology, cloud products and leading innovation at Microsoft.Related perspectives: See here
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