Remove AI Modeling Remove AI Strategy Remove Data Quality
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Why data quality is critical for marketing in the age of GenAI

AI News

Without that, the AI falls flat, leaving marketers grappling with a less-than-magical reality. AI-powered marketing fail Let’s take a closer look at what AI-powered marketing with poor data quality could look like. I’m excited to use the personal shopper AI to give me an experience that’s easy and customised to me.

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How to build a successful AI strategy

IBM Journey to AI blog

By giving machines the growing capacity to learn, reason and make decisions, AI is impacting nearly every industry, from manufacturing to hospitality, healthcare and academia. Without an AI strategy, organizations risk missing out on the benefits AI can offer. What is an AI strategy?

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AI Bias & Cultural Stereotypes: Effects, Limitations, & Mitigation

Unite.AI

In this article, we’ll look at what AI bias is, how it impacts our society, and briefly discuss how practitioners can mitigate it to address challenges like cultural stereotypes. What is AI Bias? AI bias occurs when AI models produce discriminatory results against certain demographics.

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Narrowing the confidence gap for wider AI adoption

AI News

In this article, we’ll examine the barriers to AI adoption, and share some measures that business leaders can take to overcome them. ” Today, only 43% of IT professionals say they’re confident about their ability to meet AI’s data demands. The best way to overcome this hurdle is to go back to data basics.

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With generative AI, don’t believe the hype (or the anti-hype)

IBM Journey to AI blog

.” “When we think about applications of AI to solve real business problems, what we find is that these specialty models are becoming more important,” says Brent Smolinksi, IBM’s Global Head of Tech, Data and AI Strategy. In this context, data quality often outweighs quantity.

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Maximizing compliance: Integrating gen AI into the financial regulatory framework

IBM Journey to AI blog

Regulatory insights: Current AI regulations in financial services Existing AI regulations in financial services are primarily focused on ensuring transparency, accountability, and data privacy. Regulators require financial institutions to implement robust governance frameworks that ensure the ethical use of AI.

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LXT’s Report ‘The Path to AI Maturity 2024’: Unmasking the Future of AI Innovation and Corporate Transformation

Unite.AI

The demand for high-quality training data is intensifying , with 66% of respondents anticipating an increase in their training data needs over the next two to five years. This underscores the critical role of data in training more sophisticated and accurate AI models.