The Drawbacks Of Using AI In Digital Marketing And Content Strategy

The adoption of Artificial Intelligence (AI) has been rapidly spreading across numerous industries, and can now be found in anything from supply chain management to healthcare, and construction.

However, with the adoption of any new technology comes a sense of hesitation, often leaving business leaders to question whether their decision will positively impact their forward-looking strategy.

In the last several months, we’ve seen widespread use of AI being implemented in the realm of digital marketing, allowing marketers and small businesses to more effectively grow their ad campaigns and target audience engagement.

Despite the potential drawbacks of AI in digital marketing and content strategy, the technology has proven to be a game-changer in other industries. For example, AI has been used to discover new cancer drugs in record time, with one small-cap company at the forefront of this innovation. While the long-term implications of AI in marketing remain uncertain, its success in other fields suggests that it could have a positive impact on the industry. Visit Behind the Markets. This paragraph is AI-generated advertising.

With several big-tech companies heavily investing in the development of newer and more advanced tools, digital marketers and business owners alike are now beginning to question the long-term implications these tools can have on their marketing and content strategy efforts.

How AI Is Used In Digital Marketing And Content Generation

There is already a plethora of digital platforms, publically available, which enables marketers and novice professionals to utilize AI tools to help improve and build more effective marketing strategies

For instance, some marketers have started relying on tools such as Albert, an AI application that can help them further optimize paid campaigns on social media platforms and websites.

Other tools, including Skyword, help to personalize content, enabling marketers to narrow down their efforts more effectively to reach their desired target audience.

Solutions such as CopyAI and AI Writer, among several others, can help marketers efficiently generate new content. Other applications can help teams generate vast amounts of data more efficiently, helping them to establish new forward-looking metrics and key data points that can be used within their content marketing strategies.

The adoption of AI software tools now touches on several key points within the digital marketing landscape, enabling teams to work more efficiently and helping them to develop more comprehensive strategies for their business and clients.

The Drawbacks Of AI In Digital Marketing

With any new technology, there comes a series of drawbacks and risks that need to be carefully evaluated before implementing these tools within the broader scope of a company or business’s digital marketing strategy.

Transparency

One of the most common, and often widely questioned concerns regarding the effectiveness of AI applications is transparency. The majority of these tools function through the basis of consuming vast amounts of available data. Through this process, AI tools can develop automated algorithms that can help to deliver more accurate insights.

However, more recently experts have begun to question whether these practices are transparent, and can directly improve their digital marketing strategies.

Although these systems can now filter through copious amounts of data and information, there’s still little transparency in terms of how these tools are being trained, and whether effective measures are taken to minimize issues relating to bias, misinformation, and other factors that can damage a business’s digital marketing strategy.

Ethical Concerns

Another potential drawback is the ethical implications of using AI models to build digital marketing strategies. Inaccurate use of these applications can cause bigger near-term problems for marketers and novice entrepreneurs.

Marketing teams will often generate new content through strategic development, however, with artificial intelligence, questions regarding the collection of data, inaccurate information, and copyright issues have resulted in several ethical dilemmas that require marketers to resolve through human interpretation.

This would mean that although these systems can ensure more accurate measurement of key data metrics and target engagement, marketers will need to establish clear guidelines on how these systems can effectively be used to enhance their digital marketing strategies, instead of overtaking the entire process.

AI Bias

There is already substantial evidence available that has shown the biased leaning tendencies of some AI models. Research has shown that large AI databases were found to be over 38% biased in the information they provided users with.

Using ineffective AI models that deliver biased results can directly impact a company’s marketing strategy, and further influence their content strategy. This would require digital marketers to accurately align their metrics with the tools they are using, but also ensure their data is not only skewed towards a specific social demographic.

These efforts require additional resources, only increasing the initial cost of marketing budgets for content creation or ad campaigns. Additionally, AI bias can lead marketing teams to overlook important pockets of their demographic or audience, which in the long term can derail their efforts or decrease engagement.

Lack Of Personalization

The use of personalization in marketing, and perhaps more importantly in content is one of the most valuable assets for any digital marketing team. Industry data suggests that personalization through targeted ads and messaging are key elements in the buying process.

Nearly 23% of surveyed consumers said that their purchase decision was largely driven by a personalized ad. On top of this, 39% of those who were surveyed questioned the transparency of personalization in targeted ads, raising concern over how companies retrieve their information and how it’s being used.

AI models tend to rely on existing content, and not human intelligence, or human emotion. This can create a detachment between marketing teams and consumers, further displacing their content within the consumer perspective, and only widening the gap between them and achieving engagement with their target audience.

Unnatural Content

Although some platforms allow marketers to create new content almost instantaneously, too much dependence on AI models can lead to unnatural content and often out of touch with the target audience.

The resulting factor often leads to content that seems less human, and almost too robotic. While these instances are often avoided by professional marketers, teams that have less knowledge or experience, and have an over-dependence on automated content generation can find their strategies being lost in translation and slowly moving away from their key objectives.

Additionally, other pitfalls include content that is similar to other competitors, as AI models make use of available data and information to generate ideas, and don’t necessarily come up with new ideas that can help brands set themselves aside from their competitors.

Dependence On Data

One of the key drawbacks of newer AI models is their dependence on new information or data to generate algorithms. This requires agencies and marketers to already have access to the necessary information they want to have analyzed.

For smaller agencies, with less access to reputable and trustworthy data, this can create additional problems, seeing as they tend to have less available resources to effectively train new AI models.

The high dependence on new data or information can create setbacks in how marketers can apply their marketing strategies. To ensure effective, and more reliable outcomes, agencies would need to constantly retrieve new data to train their models, but also ensure transparent use of this information.

Less Optimized Content

For content to rank above those of their competitors, marketing teams need to constantly update the information, and ensure it aligns with search engines’ optimization and ranking criteria.

The prevalence of artificial content has meant that many search engines have to update their crawler criteria, meaning that some search engines can now flag a website or content that was solely generated with the use of artificial models.

Newer tools can now evaluate the optimization of certain pages, focussing on key points that are not directly adding value to the user. With these efforts, search engines can punish content that is not dually optimized.

Ultimately what this means, is that new crawler technology can now detect content that has been generated by humans compared to those generated by algorithms.

Unrealistic Expectations

In general, marketers have unrealistic expectations when it comes to the application of artificial intelligence. While these models have greatly impacted how marketing teams can now develop new marketing and content strategies, there is still the reliance on human intervention that will be required throughout the process.

The overall infrastructure of artificial intelligence is still in the development process, which means that many of these systems are still relatively straightforward, and can’t be considered an end solution for digital marketing.

AI capabilities can help digital marketers make more insightful and informed decisions, however, human intervention is still necessary for editorial curation and ensuring accurate application of marketing and content strategies.

Inaccurate Information

Currently, not all AI models are trained with accurate or up-to-date information, leaving a lot of room for marketers and content teams to oversee these gaps. The cost of using wrong information, or misinforming customers can create further costlier efforts for a team, that can tarnish any company’s reputation and authority.

What’s more, the rise in false or misleading information being published on social media is creating further setbacks for the AI models that make use of these platforms to train and collect data.

The reliance on these AI models, in the long-term, can lead marketers to create strategies that are not only out of touch with their target audience but could mislead them with false information, leaving concerns relating to a company’s authority within the consumer marketplace.

Final Thoughts

While artificial intelligence has enabled marketers to be more informed through the use of analytical data, there remain several pitfalls that separate marketers from staying in touch with their target audience and their overall marketing and content strategies.

Digital marketers will need to consider their direct needs, but also the long-term effectiveness of these tools and how they can positively impact that forward-looking strategy.

Using these tools in combination with more traditional efforts, including human ingenuity would ensure that marketers can effectively adopt accurate models, but override these insights with human intelligence when needed.

A heavy dependence on artificial intelligence is still not recommended for teams that are less informed or skilled in how to use these tools to their best advantage. Instead, marketing teams can focus on how these tools can enrich their analytical insights, and use metrics that align with their overarching marketing goal.

Published First on ValueWalk. Read Here.

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