In the Media

The AI system that works in New York need not necessarily work in Frankfurt, UK, UAE or Saudi Arabia - and a growing number of multinationals are finding that out the hard way. While the model is the same in both places, what changes at the border is simply everything around it.  

The AI system that works in New York need not necessarily work in Frankfurt, UK, UAE or Saudi Arabia - and a growing number of multinationals are finding that out the hard way. While the model is the same in both places, what changes at the border is simply everything around it.

The failure pattern of a leadership transition reveals what data strategy institutionalization requires. Without it, a data strategy framework is a dependency, not a strategy.

The UAE's bold AI vision demands transparency and trust, but cutting corners on governance risks costly fixes from preventable failures.

The protection of sensitive citizen data through uniform security and access controls is becoming the crucial foundation for trustworthy AI in the public sector. Traditional data architectures are proving too slow for agentic AI. This is shown by the results of a survey conducted by Denodo Technologies. 

Reliable AI depends not only on the power of models, the quantity of available data, or the ability to generate increasingly complex responses. It depends, even more importantly, on the quality of meaning that data can convey to the systems that use it. This is the core of the analysis proposed by Denodo , which identifies the shift from a purely data-driven to a semantic-driven approach as one of the key conditions for making AI more manageable, explainable, and consistent with the business context.

​For years, organizations have invested heavily in building data foundations. From data marts to enterprise data warehouses, then to cloud platforms and lakehouses, the technology has evolved considerably over two decades. However, the underlying business goal remained remarkably consistent: Use data and analytical techniques to better understand what has happened and why—and let those insights guide human decision making.

After two years of AI pilots, enterprises are finally diagnosing what went wrong, and the answer keeps coming back to data. Alberto Pan, CTO of Denodo, joins Craig Smith to walk through the findings of the company's AI Trust Gap Report: a survey of 850 enterprise data leaders that reveals the dominant failure modes of enterprise AI agents are almost never the model's fault. They're caused by stale data, missing context, and inconsistent semantics across the hundreds of data sources agents need to access to do real work.

​Ever since McKinsey’s 2021 report on the importance of personalization, one-to-one marketing has been the proverbial holy grail of marketing organizations everywhere. “Seventy-one percent of consumers expect companies to deliver personalized interactions," the report states. "And seventy-six percent get frustrated when this doesn’t happen."

The most effective CEOs don't simply approve budgets or sign off on messaging. They ensure marketing has the context and influence needed to contribute strategically. Here, members of Forbes Communications Council share the most valuable ways CEOs can support marketing and communications teams—and why their actions can make all the difference.