When most people think of AI, generative tools like ChatGPT come to mind, but there’s so much more beneath the surface – including tools to gain better insights into your audience and help develop your social media presence as a whole, with the AI market in social media set to rise from $11.17 billion to $14.12 billion in just one year.
However, users are beginning to use social media more passively than in the last two years. They post less and are becoming more selective of what they trust and consume, which is why marketers must use AI in social media to help understand and advertise to their audience, rather than utilising the wrong tools and flooding pages with more automated noise.
How AI is used in social media algorithms
You may be wondering how AI use in social media actually works. Online platforms utilise subsets of AI to sort and learn from data collected from algorithms, using:
- Machine Learning (ML) architectures
- Deep Learning
- Natural Language Processing (NLP)
Algorithms track what users like, watch and consume; by doing this, they are not only able to provide these users with personalised feeds, but they can also pinpoint subtle behaviours like:
- When users pause content
- Which users skip content
- How long users view content for
- What content users engage/interact with most
This data can then be used to understand users better and engage with them. Deep Learning recommendation loops are making platforms like YouTube the primary viewing choice for audiences, replacing television streaming and the way we consume content in general.
The Telematics and Informatics Reports from June 2024 found that AI use on social media could be even more complex. As users become more selective with the content they choose to engage with, these systems need to adjust the methods they use to suggest and deliver content.
This can be done by analysing inferred emotions through audio and textual cues, as well as user response, to increase overall engagement and satisfaction whilst still understanding ‘user sentiment’ and ‘emotional nuance’ – reducing the amount of emotionally challenging content shown to audiences.
To find out more about using social media to stay on trend, check out our blog post here!
Visual social listening & brand monitoring
So what can AI use in social media do to help your marketing strategy and brand? The first step is to understand how users view your brand overall. This can be achieved by ‘social listening’.
This method was developed because gauging public opinion isn’t just about reading text tags or sweeping through a few comment sections; it’s about looking deeper and using learning architectures that can scan and identify trends in text, audio and images.
Keeping track of your brand
One of these developments is AI-powered logo detection. Rather than having to pore over posts yourself, logo detection utilises AI technology to scan images and short-form videos for any kind of brand or product appearance, meaning you can find out:
- Who is mentioning your brand
- How often your brand appears in the algorithm on average
- Which posts are using your products/logo without explicitly mentioning or tagging you
This way you can receive valuable insights on brand mentions and keep track of your performance. These insights can be improved through Multimodal Sentiment Analysis, which uses Machine Learning to understand human emotions better.
The analysis offers:
- Textual features: Analyses words, phrases and concepts in text. Detects different nuances in comments (like sarcastic, angry or otherwise emotional wording/emojis).
- Audio features: Analyses and extracts audio, pinpointing different emotional inferences in speech and other sounds.
- Visual features: Analyses pictures and videos directly for emotional cues, such as facial expressions.
This gives companies a better insight into the emotional variance in comments and videos surrounding their brand, meaning there is much less chance of an emerging PR crisis.
Real-time performance tracking & ad optimisation
Now you know how AI is used in social media algorithms; the next step is learning how to make it work for you. AI use in social media can be used throughout marketing to stretch your budgets and streamline advertising, so you can spend more time paying attention to what matters.
Personalising your advert campaigns
AI is now being deployed to create personalised adverts to hook consumers; for example, the NFL broke global barriers by using automated systems to create highly personalised content for fans worldwide. By using this technology, the NFL can gain a single, unified view of each fan, generating tailored content such as:
- NFL+ offers for app users
- Tailored content for ticket buyers
- Push notifications and behind-the-scenes content for regional fans
- Personalised messaging about games for international fans
The Vice President of Fan Engagement and Product Marketing for the NFL explained that “when fans feel understood, their passion for the game grows”.
This can be easily translated into your marketing by using AI to run automated split tests and gain a better understanding of your audience, so you can personalise their experience with your brand without overextending your budget.
Supercharged content creation
While AI use in social media is a groundbreaking development, it is important to remember that we mustn’t remove or replace human intervention. AI tools are not a new creative director; they are there to support that role, acting as an assembly line assistant.
Text-to-image and image-to-video models can generate content to help your brand move forward smoothly, instantly generating campaign assets and ideas, switching out imagery and even editing content to suit different social media feeds.
This technology can help your brand’s voice become more consistent, with Large Language Models (LLM) being leveraged to improve copywriting across your platforms.
An LLM can help you:
- Generate variations of a single caption
- Analyse competitor copy
- Polish existing text
- Brainstorm copy ideas
- Sift through and repurpose long-form content into platform-specific snippets
- An LLM can help you:
Efficiency vs authenticity
You can enhance your productivity and creativity while also saving time and resources. But overreliance on AI use in social media is something to avoid, because, when overused, AI can produce generic, formulaic content, or ‘AI slop’. Ofcom’s 2026 data show that people using social media are becoming more passive and selective about what they interact with online, so automated noise won’t hook them into your brand.
Users want to be able to trust the brands they interact with, which is why unique and human-driven storytelling is what makes them pause their scrolling.
Coca-Cola’s Create Real Magic campaign combined the efficiency of AI technology while encouraging user interaction, bringing people together to create unique, AI-generated artwork using new technology and their own ideas.
To learn more about the importance of authentic social media content and how to use it to increase engagement, check out our blog post here.
Crucial guardrails
As with anything, there are plenty of risks to using AI to collate data and create content; while it can be a great asset, there are things you need to look out for, such as:
- AI collecting data from unreliable sources
- AI collecting data with historical biases
- Deepfakes
- AI generating misinformation based on disinformation
Without the necessary human elements of fact-checking and proofreading, these risks can badly damage your brand’s reputation as well as your privacy.
Navigating the dark side of social AI
MIT Sloan researchers asked experts to evaluate and outline 24 AI risks and their severity, and one of the main concerns surrounded competitive dynamics. The AI ‘race’ is urging developers to create and deploy systems to ‘maximise strategic or economic advantage’.
However, due to how quickly and frequently these systems are deployed, critical checks can be incomplete, meaning the AI model itself is more likely to be unsafe and more prone to errors. This shows how producing more doesn’t necessarily mean producing better.
AI technology is the future of social media marketing, so it’s crucial to learn how to effectively collaborate with its capabilities without relying on it. Brands that truly understand how to use AI on social media don’t flood consumers’ feeds with generic, automated noise; they work alongside AI to create a better user experience.
Ask yourself: are you using your AI tools simply to make things quicker and easier, or to create a deeper, more complex connection with your audience? AI is capable of so much more than just image or caption generation – integrate our tips and see how they improve the overall quality of your workflow and help you think outside the box.
If you’re ready to up your social media game, contact Rawww today for support. From expert insights on working with AI tools to content optimisation tips, we’re here to help your brand be the best it can be.
FAQs
Q: What is the most common AI use in social media?
A: While text and image generation (like writing captions or resizing graphics) are the most visible uses, the most impactful AI use in social media happens behind the scenes. Platforms and brands rely heavily on AI for predictive data analysis, real-time social listening, automated ad budget optimisation, and instant 24/7 customer support via conversational agents.
Q: How to use AI in social media marketing without losing authenticity?
A: The secret to ethical AI use in social media marketing is treating AI as your research assistant and production line, never your primary brand voice. Use AI to:
- Generate initial content outlines or creative variations
- Handle tedious video editing tasks (e.g., removing backgrounds or resizing assets)
- Analyse audience sentiment trends
Always keep a human in the loop to write the final hook, inject unique brand storytelling, and provide the empathetic oversight that an algorithm lacks.
Q: How is AI used in social media algorithms to determine what I see?
A: To understand how AI is used in social media algorithms, you have to look past simple likes and shares. Modern machine learning systems analyse micro-behaviours, like:
- How many seconds you pause on a video while scrolling
- Your interaction history with similar visual textures
- What type of content you tend to avoid
- What content elicits an emotional reaction from you
According to recent 2024–2026 data, algorithms are even beginning to evaluate the emotional resonance of content to curate hyper-personalised, immersive feeds that match a user’s current behavioural patterns.
Q: What are the main business benefits of integrating AI into social media management?
A: Beyond speed, the business value breaks down into four main pillars:
- Automating customer service loops to handle high-volume FAQs instantly.
- Using visual logo detection and sentiment mining to spot potential PR crises before they trend.
- Real-time machine learning tools continuously shift budgets toward the highest-performing audience segments.
- Shifting from reactive planning to proactive positioning by capturing emerging trends early.
