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FULL TEXTS BOOK
10. INTERNATIONAL
EUROPEAN CONGRESS ON ADVANCED STUDIES IN BASIC
SCIENCES
26-28 July 2024 / Amsterdam, Netherlands
BENEFİTS AND RİSKS OF GENERATİVE AI İN CONTENT MARKETİNG
Khalil Israfilzade
Asst. Prof. at Computer and Information Sciences, ADA University, Baku, Azerbaijan
ORCID: 0000-0001-8228-4024
ABSTRACT
In a short period of time since it was launched, Generative AI has caused widespread interest in the
content marketing landscape, with significant benefits and notable risks. Undoubtedly, this is the
primary reason for marketers, managers, and experts to have a tool that generates unlimited content on
any topic. This article examines generative AI in content marketing by providing a holistic view of its
benefits and challenges. The benefits of generative AI involve efficiency in content creation, better
personalization, cost savings, creativity, and further improvements. Nevertheless, there are risks related
to these advantages, including quality of the content, ethical concerns, dependence on technology, the
potential for spreading misinformation, and diminished content value. This article attempts to provide a
balanced perspective by observing both sides. Furthermore, the article highlights some future trends and
developments in generative AI, providing an understanding of its potential impact on content marketing.
Keywords: Digital Marketing, Generative AI, AI in Marketing, Ethical AI, Content Marketing, AI-
Generated Content
INTRODUCTION
Particularly with the fast development of digital technologies and the rise of artificial intelligence (AI),
the field of marketing has experienced major changes recently. The integration of Generative AI, which
has fundamentally reshaped the limits of customer engagement and interaction, is essential to this
transformation.
Generative AI, a branch of artificial intelligence, possesses the ability to independently generate content,
leading to significant improvements in various fields, including content marketing. This technology
applies sophisticated algorithms to create text, images, videos, and other types of media, allowing
marketers to generate content on a large scale and quickly (Israfilzade & Sadili, 2024). The emergence
of generative AI presents a viable solution to the escalating need for digital content, as businesses
progressively embrace digital marketing tactics to efficiently connect with their target audiences.
Generative AI has a complex and wide impact on content marketing. On one side, it offers possibilities
for improved productivity, creativity, and personalisation that were previously unachievable. However,
the rapid integration of this technology brings forth various challenges, such as concerns about the
quality of content, ethical implications and the possibility of over-dependence on automated systems.
This paper aims to review the benefits as well as potential drawbacks of generative AI in content
marketing, offering an in-depth view of its role and implications in the industry.
Benefits of Generative AI in Content Marketing
Generative artificial intelligence is a subfield of artificial intelligence whereby new data from an initial
set of input data is produced using machine learning algorithms. Basically, it lets machines produce
original and unique content as well as ones that match the training data. Training the AI models on large
volumes helps them to learn and replicate the patterns, subtleties, and complexity of the data
(Goodfellow et al., 2014; Houde et al., 2020; Dwivedi et al., 2023).
Whereas classical artificial intelligence relies on patterns, decision-making, analytics, and fraud
detection, generative artificial intelligence generates new content, chat responses, designs, synthetic
data, or deepfakes. While classical artificial intelligence uses new data to generate simple results,
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FULL TEXTS BOOK
10. INTERNATIONAL
EUROPEAN CONGRESS ON ADVANCED STUDIES IN BASIC
SCIENCES
26-28 July 2024 / Amsterdam, Netherlands
generative artificial intelligence begins with user input and explores content variants (Israfilzade &
Sadili, 2024).
Efficiency in Content Creation. One of the most significant advantages of generative AI is its ability to
streamline the content creation process. Traditional content production can be time-consuming and
labour-intensive, requiring substantial human effort in brainstorming, drafting, and editing (Mbotake,
2024). Generative AI automates these processes, allowing marketers to generate vast amounts of content
quickly and efficiently. This efficiency not only saves time but also enables marketers to maintain a
consistent flow of content, which is crucial for keeping audiences engaged and optimizing search engine
performance.
For instance, AI-powered tools like GPT, and Gemini can generate articles, blog posts, social media
updates, and other forms of content within minutes (Nalini et al., 2021, Israfilzade, 2023). These tools
can produce content on a wide range of topics, making them valuable assets for content marketing teams
looking to scale their output without compromising quality. By automating repetitive and time-
consuming tasks, generative AI allows marketers to focus on more strategic and creative aspects of their
work.
Enhanced Personalization. Personalization is a key driver of effective content marketing. Consumers
today expect personalized experiences that cater to their specific preferences and needs (Terho et al.,
2022). Generative AI can analyse vast amounts of data to understand user behaviour and preferences,
allowing marketers to create highly targeted content.
By leveraging AI, marketers can tailor content to individual users, enhancing the relevance and impact
of their messaging. This level of personalization can significantly improve customer engagement and
satisfaction, leading to higher conversion rates and stronger brand loyalty (Babayev & Israfilzade,
2023). For example, AI can customize email campaigns based on user interactions with previous emails,
ensuring that each message resonates with the recipient.
Generative AI also enables dynamic content personalization, where the content changes in real-time
based on user interactions. This capability allows marketers to deliver a more engaging and interactive
experience, further boosting customer engagement and retention.
Cost Savings. Generative AI can lead to substantial cost savings for businesses (Kshetri et al., 2023).
Traditionally, creating high-quality content requires a team of writers, editors, designers, and other
professionals, which can be expensive. AI can reduce the need for large content creation teams by
automating much of the process, thereby lowering production costs (Soni, 2023).
These savings can be reallocated to other strategic areas such as customer acquisition, product
development, or advanced marketing initiatives. By optimizing their budgets, companies can achieve
greater efficiency and competitiveness in the market. Moreover, the cost-effectiveness of AI-generated
content allows smaller businesses to compete with larger enterprises by producing high-quality content
without a significant financial burden.
Enhanced Creativity. AI has the potential to enhance creativity in content marketing by offering new
ideas and perspectives. While AI-generated content can serve as a starting point, human creators can
refine and build upon these ideas, leading to innovative and engaging content.
AI tools can generate diverse content formats, from written articles to visual designs, providing a rich
source of inspiration for marketers (Ameen, 2022; Israfilzade, 2023). For instance, AI can create
multiple variations of a marketing campaign, allowing marketers to test different approaches and select
the most effective one. This collaborative approach between AI and human creativity can result in more
dynamic and compelling marketing content.
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10. INTERNATIONAL
EUROPEAN CONGRESS ON ADVANCED STUDIES IN BASIC
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26-28 July 2024 / Amsterdam, Netherlands
Generative artificial intelligence can also help with brainstorming by producing a large spectrum of
ideas dependent on input parameters. This capacity will enable advertisers to go in novel directions for
their campaigns and breakthrough creative limitations. Adding AI-generated ideas to human creativity
will help marketers create more original and powerful content strategies.
Risks of Generative AI in Content Marketing
With marketing not excluded, artificial intelligence has fundamentally changed many fields. From data
analysis and customer segmentation to content creation and personalized advertising, artificial
intelligence technologies have influenced every aspect of marketing during the past ten years
(Israfilzade, 2023). On the other hand, generative artificial intelligence, despite its many benefits,
presents significant risks.
Quality Concerns. It is important to note that the quality of the content is one of the significant risks
that are associated with generative artificial intelligence. AI-generated content may lack the depth, tone,
and originality that human creators can provide. While AI can produce grammatically correct and
coherent text, it often struggles with context, tone, and cultural relevance.
Human control is required in order to reduce the impact of these problems. Editors and content creators
must review and refine AI-generated content to ensure it meets quality standards and aligns with the
brand's story. Without this crucial stage, company faces the danger of releasing poor quality work that
might damage the brand's reputation.
Moreover, AI-generated content may accidentally contain factual inaccuracies or outdated information.
Ensuring the accuracy and reliability of the content is essential to maintain the trust and credibility of
the audience. Strict quality control procedures and such as subject-matter experts in the content review
process help to solve these problems.
Ethical Concerns. The use of AI in content marketing raises several ethical concerns. One major issue
is bias in AI algorithms (Ntoutsi et al., 2020). AI systems are trained on large datasets that may contain
inherent biases, which can be reflected in the generated content. This can lead to discriminatory or
offensive content that could damage the brand's image and alienate audiences.
Privacy is another ethical concern. AI relies on vast amounts of data to function effectively, raising
questions about how this data is collected, stored, and used (Manheim & Kaplan, 2019). Marketers must
ensure that they comply with data protection regulations and respect user privacy to avoid legal
repercussions and maintain consumer trust.
Transparency and accountability are crucial in addressing these ethical concerns. Companies should be
transparent about their use of AI and the data it relies on. Implementing ethical guidelines and standards
for AI-generated content can help ensure that the technology is used responsibly and ethically.
Dependence on Technology. Over-reliance on AI tools can lead to a decline in human creativity and
critical thinking (George, Baskar & Srikaanth, 2024). While AI can automate many aspects of content
creation, it cannot replace the unique insights and emotional intelligence that humans bring to the table.
Marketers must strike a balance between leveraging AI for efficiency and maintaining human input for
creativity and strategic thinking. This balance is crucial for producing content that resonates with
audiences on a deeper level and maintains the authenticity of the brand.
Furthermore, the dependence on technology can create vulnerabilities. Technical issues, such as system
failures or cybersecurity threats, can disrupt the content creation process. Developing contingency plans
and ensuring the robustness of AI systems are essential to mitigate these risks.
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10. INTERNATIONAL
EUROPEAN CONGRESS ON ADVANCED STUDIES IN BASIC
SCIENCES
26-28 July 2024 / Amsterdam, Netherlands
Potential for Misinformation. AI-generated content can accidentally spread misinformation if not
properly monitored. AI systems generate content based on patterns and data, which may include
inaccuracies or outdated information (Zhou et al., 2023). Without thorough fact-checking, there is a risk
of disseminating false information that could mislead audiences and damage the brand's credibility.
To address this risk, companies must implement robust verification processes to ensure the accuracy of
AI-generated content. This includes cross-referencing information with reliable sources and involving
subject matter experts in the content review process.
Moreover, the potential for misuse of AI to deliberately create and spread misinformation is a significant
concern. Ensuring the ethical use of AI and promoting digital literacy among consumers are critical
steps in combating the spread of misinformation.
Diminishing Content Value Over Time. Another significant risk associated with generative AI in
content marketing is the potential for diminishing content value over time (Reisenbichler et al., 2022;
Chui et al., 2023). As more businesses adopt AI-generated content strategies, the market could become
saturated with similar types of content. This saturation can lead to content fatigue among consumers,
reducing the overall impact and effectiveness of marketing efforts.
Additionally, if AI-generated content is not regularly updated and refreshed, it can become repetitive
and lose its appeal. Audiences may begin to recognize and disregard AI-generated content, perceiving
it as less authentic and engaging compared to human-created content. This diminishing value can
undermine the long-term success of content marketing strategies.
To respond to this risk, marketers must continuously innovate and ensure that AI-generated content
remains fresh, relevant, and valuable to their audiences. Combining AI capabilities with human
creativity and strategic planning can help maintain the uniqueness and impact of marketing content.
Future Trends and Developments
The future of generative AI in content marketing holds exciting possibilities. Advances in AI capabilities
will enable even more sophisticated personalization and creativity. For example, AI could analyse real-
time user data to generate hyper-personalized content that adapts to changing consumer preferences.
Integration with other technologies, such as augmented reality (AR) and virtual reality (VR), could
further enhance the impact of AI-generated content. These immersive technologies can create engaging
and interactive experiences that captivate audiences and drive brand engagement.
Additionally, ongoing research and development will focus on addressing the ethical and quality
concerns associated with AI. Efforts to reduce bias in AI algorithms and improve the transparency and
accountability of AI systems will be crucial for building trust and
CONCLUSION
Generative AI offers a transformative approach to content marketing, presenting a blend of substantial
benefits and notable risks. The efficiency in content creation, enhanced personalization, cost savings,
and creative inspiration that generative AI provides can significantly sustain marketing strategies,
enabling businesses to produce high-quality content at scale. However, the integration of this technology
also brings forth challenges that require careful consideration and management.
Quality concerns are predominant, as AI-generated content often lacks the nuanced understanding and
contextual relevance that human creators inherently possess. Ethical issues, including biases in AI
algorithms and data privacy, necessitate the implementation of healthy ethical guidelines and
transparency measures. The over-reliance on technology can diminish human creativity and critical
thinking, underscoring the need for a balanced approach that leverages both AI capabilities and human
insights.
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10. INTERNATIONAL
EUROPEAN CONGRESS ON ADVANCED STUDIES IN BASIC
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26-28 July 2024 / Amsterdam, Netherlands
The potential for misinformation and the risk of diminishing content value over time further complicates
the landscape. Ensuring accurate, up-to-date information and maintaining the freshness and uniqueness
of content is crucial to sustaining audience engagement and trust.
Future trends in generative AI point towards even more sophisticated personalization and integration
with immersive technologies like AR and VR, promising to enhance the impact of AI-generated content.
Continuous advancements in AI capabilities, coupled with ethical research and development, will be
essential in addressing current challenges and building a more reliable and trustworthy framework for
AI in content marketing.
In conclusion, while generative AI holds huge promise for revolutionizing content marketing, its
successful and sustainable implementation hinges on a balanced approach that combines technological
innovation with human oversight and ethical considerations. By navigating the benefits and risks
carefully, marketers can employ the full potential of generative AI to create compelling, effective, and
responsible content that resonates with their audiences.
REFERENCES
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y.
(2014). Generative adversarial nets. Advances in neural information processing systems, 27.
Houde, S., Liao, V., Martino, J., Muller, M., Piorkowski, D., Richards, J., ... & Zhang, Y. (2020).
Business (mis) use cases of generative ai. arXiv preprint arXiv:2003.07679.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023).
“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and
implications of generative conversational AI for research, practice and policy. International Journal of
Information Management, 71, 102642.
Israfilzade, K., & Sadili, N. (2024). Beyond interaction: Generative AI in conversational marketing-
foundations, developments, and future directions. JOURNAL OF LIFE ECONOMICS, 11(1), 13-29.
Israfılzade, K. (2023). The Role of Generative AI and Anthropomorphism in Shaping Conversational
Marketing: Creating a Matrix for Future Research. The Eurasia Proceedings of Educational and Social
Sciences, 32, 132-142.
Mbotake, S. G. (2024). A POST-EDITING TURN IN TRANSLATOR EDUCATION: TOWARDS A
CREATIVE CRAFTMANSHIP IN TRANSLATION. GPH-International Journal of Social Science
and Humanities Research, 7(05), 137-170.
Nalini, M., Radhakrishnan, D. P., Yogi, G., Santhiya, S., & Harivardhini, V. (2021). Impact of artificial
intelligence (AI) on marketing. Int. J. of Aquatic Science, 12(2), 3159-3167.
Terho, H., Mero, J., Siutla, L., & Jaakkola, E. (2022). Digital content marketing in business markets:
Activities, consequences, and contingencies along the customer journey. Industrial Marketing
Management, 105, 294-310.
Babayev, N., & Israfilzade, K. (2023). Creating complexity matrix for classifying artificial intelligence
applications in e-commerce: New perspectives on value creation. Journal of Life Economics, 10(3), 141-
156.
Kshetri, N., Dwivedi, Y. K., Davenport, T. H., & Panteli, N. (2023). Generative artificial intelligence in
marketing: Applications, opportunities, challenges, and research agenda. International Journal of
Information Management, 102716.
Soni, V. (2023). Adopting generative ai in digital marketing campaigns: An empirical study of drivers
and barriers. Sage Science Review of Applied Machine Learning, 6(8), 1-15.
Ameen, N., Sharma, G. D., Tarba, S., Rao, A., & Chopra, R. (2022). Toward advancing theory on
creativity in marketing and artificial intelligence. Psychology & marketing, 39(9), 1802-1825.
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Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E., ... & Staab, S. (2020). Bias
in data‐driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews:
Data Mining and Knowledge Discovery, 10(3), e1356.
Manheim, K., & Kaplan, L. (2019). Artificial intelligence: Risks to privacy and democracy. Yale JL &
Tech., 21, 106.
George, A. S., Baskar, T., & Srikaanth, P. B. (2024). The Erosion of Cognitive Skills in the
Technological Age: How Reliance on Technology Impacts Critical Thinking, Problem-Solving, and
Creativity.
Zhou, J., Zhang, Y., Luo, Q., Parker, A. G., & De Choudhury, M. (2023). Synthetic lies: Understanding
ai-generated misinformation and evaluating algorithmic and human solutions. In Proceedings of the
2023 CHI Conference on Human Factors in Computing Systems (pp. 1-20).
Reisenbichler, M., Reutterer, T., Schweidel, D. A., & Dan, D. (2022). Frontiers: Supporting content
marketing with natural language generation. Marketing Science, 41(3), 441-452.
Chui, M., Hazan, E., Roberts, R., Singla, A., & Smaje, K. (2023). The economic potential of generative
AI.
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