AI in marketing is virtually common-place today. It's possible for small businesses to access AI tools and incorporate them into their marketing activities. For example, Amazon is making a real-time product recommendation engine available to sellers who use the AWS console. This and other tools make it possible to use data to make better decisions and drive conversions.
The promise of AI in marketing is that it will offer greater personalization, better marketing insights, and higher returns from advertising campaigns. Today, we use AI to detect patterns from enormous data sets of users' behavior. It helps optimize ad campaigns to maximize clicks and automates content personalization.
However, some of the very promises that AI makes can prove to be potentially damaging to a business's reputation and image. While AI is growing and already in place in many businesses' marketing campaigns, it’s important to be cautious. As a business owner, you need to be aware of the limitations and pitfalls of AI when investing in it.
In the recent news, there have been a number of concerning incidents featuring the inadequate state of AI. They indicate that you can't afford to rely on AI without supervision. Here are some examples of AI disasters that reflected badly on large companies. Smaller companies may find AI gaffes insurmountable, which is why it's important to use it with an awareness of its potential.
Microsoft’s Tay: Microsoft announced its new chatbot "Tay," which was meant to learn from human interactions and create conversations on Twitter. The problems started when Tay started to create sentences that didn’t make sense. However, it took a drastic turn when online trolls began to "teach" it to spout obscenities and discriminatory phrases. It was soon pulled from social media.
Facebook's Year in Review: AI still needs to grow in its ability to read contextual changes. Facebook's Year in Review feature shows people algorithmically-created videos featuring the highlights of the year. In one case, it forced a parent to relive the death of a child inadvertently. While this isn't a deliberately malicious attack, such experiences can damage a business's relationship with a client.
Amazon's AI recruiting tool: Amazon built a machine learning tool with the aim to simplify and improve its recruitment process. It was built by studying past recruitment policies and once applied, it penalized women based on different factors. It lowered the rank of women who participated in women's clubs of any kind, as well as graduates from specific universities.
These are some of the real-world examples of how AI can be problematic. The data that an AI model is built from are fed by humans and from existing data sources. AI also learns in real-time from interactions with people, as in the case of Microsoft’s Tay.
AI is not able to understand and identify human prejudices and biases. When it's built on data that reflects human error, the results are bound to be error-prone as well.
Without supervision, AI can lead to a PR disaster and affect your business negatively. It's also important to note that your use of AI is restricted in a few ways due to the rules of the GDPR act.
The GDPR act aims to protect people’s privacy. One way that this has affected business’s marketing is that tracking users' behaviors online without consent is illegal. You have to gain direct and explicit consent before you can track what a user does on your site. It’s also illegal to use 3rd party tools because you don’t have direct consent.
Another important rule is that where decisions impact human lives, people have the right to ask for humans to oversee the decision and not AI. This rule impacts credit lendings, loans, and other key decisions that businesses make with the help of machine learning algorithms. Your ability to use AI in marketing is limited by such rules, and when you can use AI you need to make sure that it works well.
There are two potential problem areas in AI that every business should be aware of. Let’s dive into them in brief.
The literal nature of AI
You may imagine that a perfect tool is one that does exactly what it’s told. However, since AI deals with people and shifting information, this literal nature of AI can be very problematic.
For example, one of the early uses of AI in generating content was developed by asking it to identify the most popular content and to feature similar ones. The algorithms began to recommend clickbait content because such headlines generated the most clicks.
It ended up leading to poor user experience and also reflected badly on the platforms producing such content. The inputs and conditions you set up with an AI model need to reflect the true goal of producing quality content and not ones that create the most engagement with bad practices.
Due to AI being literal, it’s important to test and understand your instructions and parameters to a high degree.
AI is a black box
The reason why AI is valuable is that it can compute information from large sets of data humans are incapable of comprehending. However, the greater the complexity, the less likely we can find out why it makes the decision it does. This is the black box element of AI. You can set up your inputs and see the results, but it’s not possible to understand why the program has made a specific decision.
This can be problematic if you want to replicate such results and leverage the process in other processes. Also, if things go wrong, such as a bad product recommendation or an incorrect email sent to the wrong customer, you need to understand why it happened. When you understand the reason for a poor decision it’s possible to ensure that it’s not repeated.
The black-box nature of AI and its literal feature means that you need to be cautious when incorporating AI into marketing campaigns.
As an answer to the problems posed by AI, Explainable AI (XAI) has emerged as a way to get transparency without forfeiting accuracy.
Explainable AI has to do with design decisions and systems to turn AI from a black box into a glass box. XAI will help you determine the factors that lead to a specific outcome so that you can tinker with the processes to improve the outcome. You could also transfer what you've learned and apply it to other marketing processes.
XAI aims to explain how and why an AI model makes a decision without reducing the amount of data it needs to process to get accurate results. You can work with XAI tools to support segmentation, content personalization, campaign measurements, and more. Use AI to help reduce membership churn rates on your membership site or build a more robust understanding of your customer.
It's not possible to succeed in today's digital marketing landscape without the use of AI. It plays an essential role in making sense of information gathered from millions of interactions and user activities.
However, it's not at a stage where it’s completely reliable and it's important to be aware of its limitations. You need to test your AI products continuously learn from the mistakes made by other businesses and research bodies.
You'll be able to benefit from what AI can do your brand. But you'll also be aware of its limitations, which will help you clickbait and your relationship with your customers.