We often say AI’s mistakes are "by design," but they’re really not. AI wasn’t built to fail in these specific ways—its errors emerge as a byproduct of how it learns.
But what if we actively use them as a tool instead of just tolerating AI’s weird mistakes or trying to eliminate them?
Here are some unexpected but potentially valuable use cases where treating AI mistakes as a form of bias—rather than just failure—could lead to new insights and innovations.
Read MoreDigital coworkers are no longer hypothetical. AI-driven agents—agentics—are creeping into every function, every decision process, and every interaction within organizations. In some ways, they are the executive dream—they don’t need coffee breaks, demand raises, or call in sick. And yet, they’re reshaping work in ways few leaders are prepared to handle.
Read MoreToday, I'm sharing news of an initiative occupying my thoughts and research: the Global Student & AI Rights Pledge & Declaration. These are two initiatives I architected as part of presentations I gave this year at ICTE 2024. But before I detail these efforts, I want to emphasize something important: this isn't just another policy document destined to gather digital dust in some institutional repository.
Read MoreIn the insurance industry, data governance best practices are not just buzzwords – they're critical safeguards against potentially catastrophic breaches. The 2015 Anthem Blue Cross Blue Shield data breach serves as a stark reminder of why robust data governance is crucial.
Read MoreFor decades, we've heard the same refrain: “Change management is crucial for project success.” Yet leaders have nodded politely and ignored this advice, particularly in data and technology initiatives. The result? According to McKinsey, a staggering 70% of change programs fail to achieve their goals. So why do we keep making the same mistake, and more importantly, why should we care now?
Read MoreReal estate focuses on governance and value derivation from assets. The Marketplace facilitates value-driven transactions while maintaining order, quality, and trust throughout the ecosystem. BOTH must work TOGETHER.
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Data analytics is filled with complexity. Anyone saying otherwise is selling products. Knowing the data sources, data sets, general lineage, and behavior of the numbers are table stakes for the average data consumer. We must know where our data comes from. Much like we need to know where our food comes from and how it's processed. Is it safe to consume?
Lately, I’ve heard many stories about early career folks with data analyst titles turning to ChatGBT for help because they don't know where to go with questions. ChatGBT should only be used when the output can be rigorously challenged, which can only happen if you have the foundational knowledge of how the output was generated. Here are some handy Do’s and Don’ts to remember before turning to ChatGBT.
The synergy between analytics and Information Technology (IT) is more crucial than ever. As organizations strive for digital transformation, understanding the complex dynamic between these domains is critical to achieving strategic objectives. However, this relationship is not static; it's evolving in response to new tools and methodologies, governance requirements, and ethical considerations.
Understanding the tools facilitating this translation is critical to driving successful digital transformations and achieving strategic objectives. Key Performance Indicator (KPI) reports are often a misunderstood yet critical bridge between analytics vision and IT execution.
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