The benefits and challenges of using AI in marketing
As with other new technologies, there are both benefits and challenges to using AI for marketing purposes. From maintaining the quality of the large data sets needed to train AI to complying with the field’s ever-expanding privacy laws, organizations that haven’t used AI before are understandably cautious. But enterprises who have made the investment and identified an AI marketing solution tailored to their needs are enjoying many advantages.

Benefits
Faster, smarter decision-making: Marketing teams equipped with cutting-edge AI tools can see the impact of their marketing efforts in near real-time and adjust their tactics accordingly. AI marketing platforms can create AI marketing strategies and analyze data faster than humans using ML algorithms and recommend actions informed by sentiment analysis from historical customer data.
Improved return on investment (ROI) on marketing initiatives: AI marketing tools can help marketers identify actionable insights from data generated by a campaign in near real-time. Additionally, the same tools can help identify the right channels for a media buy and even the optimal placement of an ad based on customer behavior. Modern AI marketing solutions help stakeholders ensure that they are getting the most out of their investment in a campaign.
More accurate measurement of KPIs: Digital campaigns generate more data than humans can keep up with, which can make measuring the success of marketing initiatives difficult. AI-enhanced dashboards help marketers link the success of their efforts to specific tactics they’ve deployed, helping them better understand what’s working and what could be improved.
Enhanced customer relationship management (CRM) capabilities: AI technologies help marketing teams improve their customer relationship management (CRM) programs by automating routine tasks like the preparation of customer data. They can also reduce the likelihood of human error, deliver more personalized customer messages and identify at-risk customers.
More meaningful insights from customer data: Today, many marketers struggle with the sheer amount of data available to them when they’re planning a campaign. AI can help by performing predictive analytics on customer data, analyzing huge amounts in seconds using fast, efficient machine learning (ML) algorithms. It uses the data to generate insights about future customer behavior, suggest more personalized content and spot patterns in large data sets for marketers to act on.
Challenges
Training AI solutions: Just like humans, AI requires significant training to learn a new task. For example, if you want an AI solution that will talk to your customers in an engaging way, you’ll need to invest the time and resources necessary to teach it. To build an application like this, you will need a large amount of data about customers’ preferences and, potentially, data scientists who specialize in this kind of training.
Ensuring the quality and accuracy of data: AI solutions are only as strong as the quality of the data they are trained on. This is true no matter how technically advanced a tool is—if the data it’s been trained on isn’t accurate and representative, the answers and decisions it generates will be low quality and ineffective.
Complying with privacy laws: Because AI is trained on personal customer information, the laws surrounding what can be used must be strictly followed. Companies who deploy AI for marketing purposes need to be able to adhere to consumer data regulations or they risk incurring heavy fines and reputational damage. According to HFS Research, reputational damage from errant AI deployments appearing in the media has increased the pressure in both Europe and North America for greater regulation.