From next-generation recommendation engines to content generation and chatbots, Artificial Intelligence (AI) is helping marketing teams deliver superior customer experiences and boost sales. Despite AI’s ability to provide real-time, personalized experiences, only 17% of marketing leaders use it. We’ll take a look at why marketers struggle to adopt AI technology.
Marketers face data scarcity
Data scarcity is one of the significant challenges preventing marketing teams from adopting AI. AI platforms require large quantities of training data to learn from and make intelligent decisions. And as 51% of organizations working on AI projects have limited data, the more data used to train an AI algorithm, the more accurate predictions and better decision-making. For instance, Walmart’s Data Café analytics hub connects to over 200 datasets of internal and external data, including 40 petabytes of recent transactional data. This data enables Walmart to offer smarter stocking, pricing, merchandising and marketing solutions in real-time.
The velocity of data required to run Artificial Intelligence (AI) platforms outstrips the capacities and expertise of many marketing departments. Without access to enough data, marketers are left with a shiny new technology that requires human intervention. To solve data scarcity problems, marketers can collect third-party data or consider using synthetic data, to support AI learning model development.
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Marketer’s may deal with poor data quality
When it comes to AI platforms, having customer data isn’t enough. Marketers need high-quality, structured data. AI algorithms learn from data input and require data that is accurate, complete, and up to date. This ensures AI platforms develop personalized offerings and deliver relevant and timely offers to consumers.
Accessing quality data is challenging for many marketers as organizations have high levels of incomplete, inaccurate, or inconsistent data. Organizations believe about a third of their customer data is inaccurate in some way.
Leveraging incomplete data in AI platforms kicks off a domino effect. A classic case of garbage out, powering AI platforms with poor-quality data damages the reliability of analytics, drains marketing resources, and limits marketing effectiveness. With the high cost of working with poor data, marketers can’t rely on AI platforms to make decisions.
Marketers can improve data quality with data enrichment tools or a Customer Data Platform (CDP). A CDP cleans up data by eliminating redundancies and reconciling missing details or incorrect data.
Companies might have the right IT infrastructure
The IT infrastructure required to handle large data volumes is a top challenge for marketers adopting AI. AI applications require robust data infrastructure that can process vast quantities of data. It should also be agile enough to respond to new market dynamics and customer demands.
For example, organizations have to build and maintain big data storage and analytics environments to deal with the massive data AI requires. Seamless networking is another essential component of AI infrastructure. Deep learning algorithms depend on communications that require a high-bandwidth, low-latency network as AI efforts expand.
AI infrastructure can be costly to set up and maintain. The steep price of AI infrastructure makes a stumbling block for companies with modest IT budgets.
To cut costs, marketers may need to find commercial AI solutions that meet their requirements. AI solutions are faster and less expensive to buy than building one in-house.
Marketers face talent shortages
To meet their AI aspirations, companies need the right mix of AI talent. But finding the right talent isn’t easy. AI skills are rare and in high demand, so there’s a shortage of skilled workers available to hire. The absence of talent prevents marketers from harnessing the full potential of AI. Only 1.9% of marketing leaders have the right talent to leverage marketing analytics which power AI applications.
Some ways brands address the skill gap include:training staff, creating academies to support upskilling, and crowdsourcing. Walmart turned to crowdsourced data science competition website Kaggle to recruit AI talent. In the recruiting competition, job-seekers were provided with historical sales data for Walmart stores located in different regions and asked to project the sales for each department in each store.
Marketers can bridge the AI skills gap by sourcing AI talent from universities and leveraging cloud-based platforms with pre-built solutions.
Ethical and privacy concerns are a common blocker
Implementing AI comes with ethical challenges. AI systems learn from real world data and can amplify pre-existing biases. 90% of organizations are aware of at least one instance where an AI system resulted in ethical issues for their business.
The cost of lagging inn AI ethics is high. Close to 60% of organizations have attracted legal scrutiny and 22% have faced a customer backlash because of decisions reached by AI systems. Marketers have to consider how to deliver highly personalized services using AI systems while avoiding discrimination. One way to do this is by using inclusive datasets. L’Oreal’s AI-powered digital skin diagnostic tool was trained on 6,000 clinical images and more than 4,500 smartphone selfies of women with different racial backgrounds.
Privacy is another ethical concern when it comes to AI adoption. AI tools use large amounts of personal information in ways that can intrude on consumers’ privacy. Customers walk away from companies whose data-privacy practices they don’t trust, don’t agree with, or don’t understand. Following consumers’ increasing uneasiness about how their data is used and the emergence of data privacy laws, marketers need to follow AI regulations and protect consumers’ data.
Trust earned over the years can be lost in an instant. Marketers have to be ethical when implementing AI and ask critical questions about the data algorithms use, transparency and how personal data is safeguarded.
No doubt, implementing AI technology in marketing can feel daunting and risky. But done correctly, AI technology is worth adopting. Marketers that go slow and take the time to adopt AI the right way will gain a competitive advantage.