AI content searches on Google increased in 2023 as businesses and authors capitalized on new scaling prospects.

In March, 60% of writers employed AI, according to a Hackernoon poll.

Generative AI has content production and optimization potential, but its strengths and limits must be balanced.

Marketers are using AI to summarize long texts and write attractive social media copy.

As we approach 2024, generative AI content’s shortcomings are as essential as its successes.

Brands face risks and obstacles from inaccurate SEO research, factual accuracy, plagiarism, and the lack of human experience and logic.

Where marketers are getting AI content right

Used properly, generative AI can streamline operations, improve productivity, and improve content quality.

Here are some ways marketers use AI for content development.

Summarizing long passages of text

Generative AI can swiftly and reliably summarize long papers, reports, and articles.

This lets us extract and communicate vital information with our audience.

Generating landing page copy, titles, and meta descriptions

Effective landing page language and meta descriptions are essential for SEO and customer conversion.

AI can analyze your main content and develop captivating titles, meta descriptions, and landing page text.

Creating social media copy for promotion

Promoting long-form material on social media can be difficult, especially if you need to grab attention quickly.

Create interesting social media posts for your target demographic with AI.

Turn long-form material insights into captivating captions and tweets to drive attention to your in-depth articles and blog entries.

Enhancing storytelling with use cases and examples

Marketing storytelling is powerful, and AI may enhance it.

Create realistic, persuasive stories about your product’s benefits. It helps research by anticipating objections and queries.

Producing copy and title variations for testing

AI can improve A/B testing with varied material and headlines.

Analyze content and audience preferences to provide email, website, and ad alternatives. This lets you test and improve conversion and engagement rates quickly.

Although AI is useful, it is being exploited and mistreated like other new toys.

In the next section, we’ll examine some of the less effective and possibly more harmful AI content production methods.

Where AI content falls down

Using generative AI in these ways can lead to bad content, disinformation, and misleading recommendations.

Generative AI failures stem from data. The data that enables ChatGPT, Bing AI, Google Bard, and third-party solutions must be high-quality and connected.

Rapid engineering and fine-tuning of data from large language models (LLM) are being used to obtain outstanding outcomes. Generative AI needs these methods to thrive.

SEO and keyword research

Relying solely on AI for SEO research can result in:

• Misguided keyword choices.
• Incorrect optimization tactics.
• Missed opportunities to connect with your target audience effectively.

Human skill is needed to interpret AI discoveries and make judgments.

AI content creators rarely have real-time data.

Tools recommend keywords and optimization tactics using past data and patterns. They can’t cope with real-time search trends or user behavior. This restriction can waste time and money.

Excessive AI technologies can overwhelm marketers, with over 70% experiencing confusion.

Factual accuracy

AI-generated material cannot verify facts. Relying on it may result in disinformation, harming your brand’s credibility. To provide accurate and current content, human oversight is essential.

AI-generated material often needs human editing to enhance, fact-check, and fulfill quality requirements.

AI can draft, but it may struggle with tone, context, and nuances.

Human editors improve content coherence, engagement, and brand voice.

The risk of plagiarism

AI can accidentally copy web content. Unintentional plagiarism can occur without supervision.

To avoid this, content providers must thoroughly check their AI-generated content for originality.

Perspective and subject matter expertise

AI is useful for short-form material like product descriptions and social media posts but may struggle with complicated, in-depth content like articles and research papers.

AI struggles to provide in-depth analysis, storytelling, and investigation because of its limited knowledge and subject matter competence compared to human writers and experts.

It produces shallow, incorrect content because it cannot comprehend complex or niche topics. It lacks humans’ creativity and logic.

While it can generate material using patterns and statistics, it may struggle to create new or emotionally engaging content.

Human writers excel in storytelling, humor, and emotion.

Amplifying bias and stereotyping

AI algorithms learn from historical data, which can contain biases.

If not carefully monitored and trained, AI content generators can perpetuate biases, stereotypes or discriminatory language, harming your brand’s reputation.

Trying to cut costs with AI can backfire

These factors make AI a dangerous cost-cutting option.

AI lacks creativity, adaptability, and nuanced decision-making, thus overusing it can hurt quality, consumer trust, and employee morale.

It can cause errors, impersonal encounters, and a loss of competitive advantage in sectors that value human expertise and consumer experience.

Sustainable cost management while maintaining quality and customer happiness requires a balance between AI’s efficiency and human oversight and inventiveness.

Content development should involve humans and editors.

This team can ensure that your material matches your brand’s voice and tone, preserving its identity and meeting your business goals.

Finding machine balance in 2024: Humans in the loop

A good content strategy requires human-machine harmony.

AI technologies provide amazing economies and opportunities, but understanding how humans and machines can work together is key.

See how people and AI interact during content development and how we might improve.

1. Editorial process balance

In creating captivating content, people and machines can achieve balance at various editorial stages.

AI is useful for initial research, data analysis, and first writings.

However, humans must drive, especially during later editing stages to polish context, style, and authenticity.

2. Iteration and Collaboration

Improvement is ongoing. Optimizing content output requires human-AI collaboration.

Human feedback helps AI systems discover nuances, preferences, and trends.

AI can help people by automating tedious chores and providing data-driven content recommendations.

3. Search experiences begin and end with humans

In SEO, it’s essential to remember that search engines ultimately serve humans. Content optimization must begin with an understanding of human intent and user behavior.

Once the content is optimized, humans will engage with it, so a human-centric approach ensures that the content:

• Meets their expectations.
• Answers their questions.
• Provides value.

4. Local and cultural relevance

Content must appeal to varied regional and cultural audiences.

Businesses targeting worldwide audiences can localize content with AI translation solutions.

However, human translators and cultural experts must ensure accurate, culturally relevant, and sensitive information.

Humans can adjust material to local demographics and audiences with cultural expertise and contextual understanding.

5. Creative ideation and strategy

AI helps generate content ideas based on data analysis, while human creativity generates creative content concepts and strategic direction.

Humans can think creatively, link concepts, and create audience-friendly content angles.

6. Personalization and user segmentation

AI can analyze vast amounts of data to segment audiences effectively.

However, humans play a critical role in interpreting these segments and tailoring content to meet each audience group’s needs and preferences.

Human insights help create personalized content that resonates on a personal level.

7. Content distribution and optimization

AI can automate content distribution across platforms and improve posting schedules, but humans give strategic oversight to ensure material meets marketing goals.

The content strategy can also be adjusted in real-time on social media and other platforms.

8. Content strategy growth and maturation

Developing a content strategy should be ongoing. Humans can:

• Evaluate the success of content initiatives.
• Understand audience feedback.
• Adjust the content strategy accordingly.

AI can assist by providing data-driven recommendations, but human strategists ensure the strategy aligns with overarching business objectives.

9. Content repurposing and diversification

When growing material or diversifying formats, humans can contribute new ideas and strategic guidance.

From there, AI helps rapidly generate blog posts, videos, infographics, and podcasts.

10. Content accessibility and inclusivity

All audiences, including those with impairments, must have access to content. Accessibility ensures equitable access to digital content for all, regardless of physical or cognitive abilities.

Digital accessibility rules in several nations require compliance, with non-compliance resulting in legal consequences, fines, or litigation. AI can discover accessibility concerns, but people must implement remedies and ensure material fulfills requirements.

The future of content strategy involves integrating AI, humans, and processes.

AI automates, analyzes, and generates content, while humans add originality, authenticity, and emotion.

Organizations must also balance cost reduction and optimization. Reduction typically isolates content and innovation in the near term.

Optimization helps humans and machines collaborate and cut expenses without sacrificing efficiency or results.

Human intent, AI for efficiency, and human-centric optimization will create a content strategy that thrives in 2024 and beyond.