What if every US citizen had a dollar for every good decision made by the president and lost a dollar for every bad decision?  How much would Americans have in their “Decision Account” now?  What about a half-good decision?  Would Americans’ Decision Account be credited for 50 cents?  Clearly, the whole matter hinges on what is defined as a good or bad decision.  

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Over the past few decades, the use of AI in business has grown tremendously. By 2030, AI may generate up to $15.7 trillion in revenue to the global economy.

From process automation to sales forecast, the potential applications for AI in business are nearly limitless. But, you’ve still got to wonder, what happens when we place so much power on fairly new technology? Will it be fair or biased? Will it try to take over the world in the future? We don’t give the fear mongers much weight about AI taking over the world, but unchecked bias in AI data or models can lead to unintended negative consequences that do real harm.  

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Artificial Intelligence (AI) is becoming a critical component of digital marketing.  AI in marketing can be particularly revelatory, often delivering valuable insights that marketers can use to develop personalized content, drive conversions, and improve customer experience. Such insights help develop effective marketing strategies, which increase Return on Investment (ROI) for AI implementations.  

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4 Ways Data Can Help Create Sustainable Revenue Growth

There is no question that year after year, the cost of revenue (COR) generation continues to climb. Look at publicly traded companies in nearly any sector and read the reported costs associated with sales and marketing. In almost every case, the costs of acquiring revenues continue to outpace the gross revenues generated. It costs more to make the same money. In today’s world of shifting digital and economic landscapes, tapping into data in real time and extruding insights gives executives a critical tool to stay ahead of the curve. Near real-time decision making is a necessity – not a luxury.  

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Building an Efficient & Effective Data Science Team

There are numerous reasons why a company may decide to develop a data science team in their organization. The skills an in-house team brings can enable organizations to turn raw data into strategic assets. Their skills lead to better use of data assets including understanding customer behavior, error detection, automation of repetitive tasks, and overall more sophisticated decision support. A data-driven organization can benefit and gain a competitive edge.

However, building an effective and efficient data science team is not as simple as adding new hires. Before we can discuss whether to rent or build a team of experts, it’s essential to understand the roles typically needed to fully staff a data science team.

The included salary estimates do not include benefits. The figures are an average of the latest available data from Salary.com, PayScale and ZipRecruiter and reflect US labor markets.  

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Artificial Intelligence(AI) has recently been integrated into marketing and is still in its early stages. It makes automated decisions based on available data and audience observations or economic trends that impact marketing. By doing so, it enables marketers to gain more insight and understanding of their target audiences.

However, a business must comprehend how AI Marketing works and its effects before adopting it. Here are seven questions every company interested in AI Marketing should ask themselves.  

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