To be not swept under by the huge wave of advanced technologies and to remain competitive in 2020 and beyond, businesses take the opportunities that AI brings to marketing. AI-powered tools have helped marketers with new methods of advanced advertising; understand sales cycles and their targeted consumer behaviours. An organization that neglects the importance of AI is likely to suffer from a downfall in the upcoming years. Having said that, let us not be swayed by the thought that AI marketing tools are invincible. There are associated pitfalls in regards to the utilization of AI in marketing.
A survey carried out by data analytics firm Teradata found that 80% of enterprise-level organizations were already using some form of AI in their business (32% of those in marketing). However over 90% also anticipated significant barriers to full adoption and integration.
Before the competition gets rough, let’s buckle-up to understand the challenges that AI pose in marketing.
- Deficiency in IT infrastructure
For effective implementation of AI-driven marketing, high-performing hardware and frequent updates in the software is required. The necessary high-end computer systems are expensive to set-up and maintain. Thus, this can be a hindrance for companies with modest IT budgets. And also with the changing trends, comes an upgrade required for the already employed software. If a breakdown happens while upgrading, then the organizations are at risk of losing all the data and codes and retrieving and restoring them can be costly.
Fortunately, Cloud services are available as an alternate solution to this issue. Cloud software vendors provide all the IT infrastructure and employees needed to run AI software in exchange for an affordable monthly or yearly fee.
- Lack of high-quality data
AI feeds on high quality and right data to provide results that are not skewed. And we are living in an era of big data; hence cleansing these data sets prior to providing it to the AI algorithm is required.
Furthermore, AI facilitated marketing requires high-quality data to feed recommendation engines to create personalized suggestions, customized products, email newsletter, push notifications, or chatbot content.
- Lack of talent
At present, there are significant AI skills gaps, between the available and the required skills by organizations for in-house AI marketing solutions. There is no apt talent to quickly fill the new positions coming up. Even when automated AI marketing software is used, it requires trained workers who could decipher the outcome effectively.
This puts another strain in the budget when it comes to recruiting an AI expert.
While these challenges may slow the implementation of AI solutions in certain organizations or restrict the way that data can be collected or used, there are plenty of alternative solutions available.
