Is AI Ready To Transform The Marketing Industry?

Artificial intelligence and machine learning tools are becoming increasingly common throughout the world of business and marketing, especially since they allow unprecedented levels of efficiency due to automation.

A survey found that 80% of enterprise-level organizations are already using some form of artificial intelligence (AI) in their business — of which, 32% is related to marketing. Having said that, over 90% of the organizations surveyed also anticipated significant barriers to full adoption due to the complexity and relative unfamiliarity with the technology.

Let’s take a look at some of the biggest challenges to AI marketing success:

A Lacking IT Infrastructure

AI-driven marketing strategies require a robust IT infrastructure to be truly successful. AI and machine learning derive insights through intensive data analysis, and that requires vast quantities of data as well as powerful hardware to compute through such voluminous data.

Setting up such an infrastructure can be expensive. State-of-the-art computers and related devices that feature the latest technologies can cause a dent in your budget. Furthermore, such complex infrastructure requires frequent updates and regular maintenance to ensure smooth operations. Ensuring a separate budget for the IT infrastructure’s maintenance can be a big obstacle, particularly for small and medium-sized organizations.

Getting alignment from the C-suite and an adequate budget for the right infrastructure and its maintenance should be your organization’s priority when working toward AI marketing success.

Insufficient Data Utilization

AI marketing software can analyze a decade’s worth of data to identify trends and conduct predictive analysis. However, acquiring such data can be a major obstacle. Devoid of high-quality data in volume, AI is likely to deliver poor results.

We are living in the big data era of computing, where businesses are leveraging organizational data to go beyond standardized metrics and draw deeper insights into how markets work. However, there is a noticeable divide in the amount of data produced, as opposed to the data that is utilized effectively by organizations.

In order for AI to be successful with marketing, organizations need to be careful in extracting organizational data and make sure they’re using the right technologies to gain maximum insights out of this data.

The Past Is Not Always A Prologue To The Future

Marketing firms need to realize that, even when a company has sufficient data to create AI applications, the models and data sets their AI algorithm is trained against isn’t obligated to work forever. The ever-changing nature of the marketing arena makes it impossible for data sets to remain consistent over the years.

Take AI applications used to manage marketing campaigns, for example. IBM announced that it would use its AI software, Watson, to manage all of its programmatic campaigns. According to the company, and as reported by Ad Age, the average cost per click for IBM marketing campaigns fell by 35%, and in some cases as much as 71%.

Watson utilized advanced analytics to make the bidding process increasingly efficient. It did so by analyzing massive amounts of data sets and predicted the value of target consumers based on the time of the day, the device and browser used, as well as the language they spoke.

However, as the past has shown, the advertisement market isn’t static and previously successful models face difficulty as consumer behavior evolves. This is particularly true for AI learning, as its ability to compute and predict is heavily reliant on the data sets that it is trained on.

Changing ad formats, evolving markets and tastes, as well as the increase in the number of organizations that utilize AI in their marketing endeavors all have the potential to change marketing conditions. The marketing arena can be significantly different from the one that existed when the data was gathered for AI algorithms to train on. Businesses need to account for this risk of a gradual decrease in efficacy by allocating safeguards, such as a continuous learning curve for the AI algorithm.

Dearth Of AI Experts

Regardless of the hype that surrounds AI and how it impacts the world we live in, the truth is that only a small fraction of the population can be considered professional experts. Even if marketing organizations overcome other obstacles and develop successful in-house AI marketing solutions, they are faced with intense competition when looking for AI talent.

The market is currently facing an AI skills gap that greatly impacts the businesses that are looking to invest in the AI sector. As the number of companies that are looking to integrate AI technology into marketing increases, the existing pool of AI talent will likely fall sufficiently short on filling these new positions.

Even companies that are using readymade AI marketing software need to ensure that they have trained their employees to properly deploy and manage new technology. Failure to do this will result in software that is unable to provide accurate analysis and results.

The shortage of AI trained employees also places a burden on the already stretched marketing and human resources budgets. In short, candidates who are already familiar with newer technologies need to be offered higher salaries due to increased demand. This may be a primary reason that many small-to-midsize enterprises are reluctant to integrate AI into their marketing efforts. They just don’t have the budget to hire skilled employees or compete with large organizations in a bidding war for them.

Overall, based on what I’m seeing, modern organizations are quickly adapting to advancing technology and leveraging newer marketing avenues that are afforded by AI integration. As the market continues to be shaped by data analysis, machine learning and artificial intelligence, organizations must embrace this revolutionary technology and continue to work to overcome these obstacles for future sustainability.

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