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Marketing teams are increasingly turning to artificial intelligence (AI) to improve results, with marketers investing over $227 million in AI-based technologies in 2018 alone, according to Statista.

Yet many companies have yet to use AI or have just started exploring it recently. So what are the best entry-level AI projects in the CX/martech stack to establish success? Below are five recommendations.

With the rise of low-code technologies, were witnessing a Cambrian explosion of AI projects that are considered low-hanging fruit for marketers and customer experience professionals, said Jen Snell, vice president, product strategy and marketing, Intelligent Self-Service, Verint. Were now seeing immense success with automated interactions at the start of customer journeys, which is an area where most marketers are focused.

IVAs and chatbots were once major AI projects, but low-code solutions make it possible to quickly deploy and still return value to the business, Snell added.

Above all else, AI is about data, Snell said. Data is the frontier of modern marketing, yet most marketers dont have access to the type of data generated by interactions with an IVA. The depth of insights drawn from multi-dimensional interactions with IVAs is unprecedented and incredibly valuable to marketers. With this data marketers can continuously enhance and personalize experiences, identify gaps in the customer journey, and truly understand what drives their customers.

Related Article: The Future Is Multimodal: Why Voice Alone Will Never Be the Answer

AI offers a way to help serve the right type of experience to the right type of prospects, according to Adam Smartschan, chief strategy officer for Altitude Marketing. He offered three examples:

Implementing a quality management solution enhanced by AI allows companies to coach and train contact center agents more efficiently and without bias, according to Fabrice Martin, chief product officer for Clarabridge. Agents and managers can save time from training/coaching so they can focus on higher-value interactions, measure drivers of customer satisfaction and improve the customer experience across all channels.

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To successfully harness and leverage customer feedback, companies should implement solutions that incorporate natural language understanding (NLU) and natural language processing (NLP), Martin said. NLU uncovers the meaning behind text, such as tweets, emails, reviews, etc. On the other hand, NLP helps computers quickly read large amounts of text to uncover important business insights.

Both projects help businesses quickly gather insights into whats working and whats not. Incorporating AI drives efficiency, provides real-time insights and helps organizations better understand customers, which leads to an improved customer experience.

This is one of the most impressive and forward-thinking machine learning ventures I've seen. Social media platforms such as Facebook, Twitter and YouTube are overflowing with big data, said Bram Jansen, chief editor of vpn Alert. Mining the data will help explain consumer feelings and viewpoints in a variety of ways. This project may also be used for digital marketing and branding to consider a customer's view or response to a product or service.

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Getting Started With AI? Consider These Simple Marketing Projects - CMSWire