Hyper-Personalization at Scale: How AI is Revolutionizing Inbound Marketing in 2025
The world is changing fast, and the digital marketing landscape is changing along with it. In the ever-evolving digital landscape, consumers have increasingly higher standards, something we can all learn from when considering our dating history. Buyers are no longer responding to one-size-fits-all approaches to marketing; they want brands to read their minds and predict what they want and when they want it, akin to demanding lovers of romantic rendezvous past. This has given rise to hyper-personalization, a strategy that leverages advanced technologies to recognize individual preferences, behaviors, and needs of every customer instantly, and across every touchpoint. In this blog, we’ll tell you everything you need to know about utilizing AI in your marketing strategies to deliver authentic and relevant information to customers and maximize conversions.
What Is Hyper-Personalization?
So, what exactly is hyper-personalization? It’s exactly what the name suggests, an extra personalized approach to marketing that leverages tools like AI, real-time data, and machine learning to further tailor already personalized messages to meet the needs of each individual customer. Traditional personalization methods rely on static data such as a customer’s past purchases or demographics, whereas hyper-personalization is more dynamic, using data that is more behavioral and contextual. Examples of this type of data could include a customer’s browsing location, behavior, the device that they’re using, and even the time of day — all of which are subject to change at any given moment.
Still confused? Let’s break it down even further. A clear way to demonstrate this concept is via email marketing campaigns. Basic personalization could be to include your customer’s name in the subject line, such as Hey Jane, check out these new arrivals! Hyper-personalization would take it a step further and utilize the customer’s name, location, and previous purchase history as illustrated in a more complex subject line like Hey Jane! It’s chilly in London, here’s a jacket that suits your style! This includes real-time information pulled from the customer’s wish list, the weather in their current location, and predictive data for their fashion preferences. The body of this type of email wouldn’t simply include new arrivals; it would highlight a specific jacket that the customer browses and include real-time inventory of their size, as well as complementary items based on what other users bought.
Why is Hyper-personalization important?
As the world gets increasingly smarter, so should our marketing strategies. Nowadays, customers have higher expectations when it comes to meeting their needs, requiring brands to immediately understand their desires and offer relevant experiences across channels. By meeting these needs and giving customers what they want, businesses reduce churn rates and are more likely to retain these customers as opposed to losing them to competitors. Not only does hyperpersonalization increase brand loyalty, but it also makes a business likely to outperform its competitors because they are more responsive and customer-centric, thus increasing conversions. Finally, hyperpersonalization is more efficient than personalized marketing efforts alone since everything is automated at scale, reducing the need for manual intervention and the possibility of human error.
What are the Key AI Technologies Driving Hyper-Personalization?
There are three major technologies at the core of hyper-personalization, enabling businesses to process large amounts of data in real-time to better analyze patterns and predict customer behavior. These technologies also work to consolidate the data taken from various platforms and put it into one place, offering a comprehensive view of a customer profile that goes beyond basic segmentation.
Machine Learning (ML) and Predictive Analytics
Machine Learning, or ML, is a subset of artificial intelligence where algorithms learn patterns from data and improve their performance over time without being explicitly programmed. Utilizing predictive analytics, ML models analyze customer behavior to predict how they will behave in the future. This proves useful for businesses when they want to know the best time to send an offer and who to target based on which users are likely to convert.
So, if you’re curious to know whether or not a customer will buy what you’re selling or if they’re likely to cancel an existing subscription, ML models will supply that information. If we want to understand ML models, we can look at streaming services like Netflix. If you’ve ever been watching something and you receive a recommendation about what to watch next, that's ML; those recommendations are based on what users with similar viewing habits are currently watching, and Netflix actually credits this personalization with saving over $1 billion annually by reducing subscriber churn. The best thing about these ML models? They naturally improve algorithm performance over time without having to be explicitly programmed.
Natural Language Processing (NLP) and Conversational AI
Natural Language Processing or NLP enables machines to not only interpret human language, but respond to it as well, which comes in handy when identifying potential frustration in a customer before routing them to a live customer service representative. Basically, NLP models take all customer-facing interactions via email, live chats, and reviews and analyze their sentiment or intent. This, combined with conversational AI, is used to provide tailored responses and proactive solutions that mimic those of human responses. Have you ever raised a query in a business’s chatbox and had a conversation with a “live customer service representative?” In some cases, that might have actually been conversational AI.
Generative AI for dynamic content creation
Generative AI, such as DALL-E or the popular ChatGPT, creates new content like text, images, or even videos based on patterns learned from large datasets. This can be used in hyper-personalization to generate custom emails, ads, product descriptions, or landing pages that target specific audiences. Want to write an email in a particular tone catered to the preferences of a specific user? You can do just that with the help of generative AI and highlight the information that the user is most likely to care about.
How these key AI technologies work together
You might be wondering how all of these models work in conjunction with one another to enhance the customer journey. To demonstrate this, here’s a quick example:
Let’s imagine that you’re a global fashion retailer who is launching a new seasonal collection and you want to target existing customers using hyper-personalization through email, social media, and the content on your website.
First, you’ll use predictive analytics and machine learning to predict which customers are likely to buy and their preferred platforms, as well as the optimal time to send them promotional offers. After your customers are segmented, you’ll analyze customer reviews using NLP. Through this, you can identify the tone that they respond best to, their frustrations, buying intent, and general queries in order to know which products to highlight and which tone to use when doing so. Conversational AI can then incorporate the preferred tone into the predicted query responses and also upsell products based on customer history, increasing customer satisfaction. Finally, you’ll use generative AI to create deliverables and assets related to your campaign, and voila, you’ve just hyper-personalized your marketing strategy.
Real-Time Personalization and Customer Experience
Let’s skip the hypothetical and get right down to reality, and the reality is that many well-known brands are already incorporating hyper-personalization into their customer experience.
Amazon
Amazon is renowned for its real-time personalized product recommendations, driven by AI, which, according to their own reports, drive approximately 35% of its total sales. This is a prime example (pun intended) of the profound effect of AI-driven marketing strategies and the money that is to be made when they are implemented correctly.
Spotify
Similar to Netflix’s viewing recommendations, Spotify leverages AI-driven personalization extensively in its curated playlists like "Discover Weekly." These playlists have significantly increased user engagement, with listeners spending more time on the platform, demonstrating increased loyalty.
Starbucks
If you’ve ever used Starbucks’s loyalty app for your daily caffeine fix, you’ll have noticed that it’s very intuitive, predicting almost exactly what you want, when you want it. This is because Starbucks utilizes AI for hyper-personalized marketing within the app, creating real-time offers based on customer purchase history, preferences, and behaviors. They attribute a notable increase in sales to these AI strategies, particularly via their mobile app promotions.
Sephora
Make-up lovers know that Sephora uses AI-driven personalization to offer tailored beauty recommendations through both online interactions and its virtual assistant chatbot. Since these efforts have been realized, Sephora has experienced increased customer satisfaction and boosted sales, which they attribute to AI-driven virtual try-ons and personalized beauty consultations.
Benefits of AI-Driven Hyper-Personalization
As we’ve already said, not only do customers want hyper-personalization, but it’s now become the norm. One of the major benefits is improved customer satisfaction, making customers feel understood and valued. This approach fosters stronger emotional connections and brand loyalty as your deliverables are more likely to resonate with customers, leading to a 10-15% increase in conversion rates.
AI also enables businesses to anticipate customer needs and preferences before they are explicitly expressed. This proactive approach allows brands to get one step ahead of competitors to deliver relevant content and offers at the right time, reducing decision fatigue and making the path to purchase feel effortless.
Challenges and ethical considerations regarding hyper-personalization
Of course, there are two sides to every coin, and with the positive comes the negative. In this case, there are some challenges to the ethical ramifications of hyper-personalization. If you’re like the majority of people, you might feel like Big Brother is watching and that robots are “taking over” while spying on you. It’s true that collecting and utilizing vast amounts of personal data to tailor marketing efforts can lead to privacy issues if not handled responsibly.
Consumers may feel their personal information is being exploited without their full understanding or consent. That’s why adhering to data protection laws such as the General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA) in the U.S. is crucial; these regulations impose strict guidelines on data collection, storage, and usage, requiring businesses to obtain explicit consent and provide transparency in their data practices.
Best practices for balancing personalization and privacy
Striking the right balance between delivering personalized experiences and respecting user privacy can be tricky. Over-personalization can lead to perceptions of intrusion, while under-personalization may result in users being bombarded with irrelevant content, both potentially harming customer trust and engagement.
Adopt a consent-first approach
Just like in many situations, consent is key! Clearly explain to the customer how their data will be used, especially for personalization. Simplify consent processes, allowing users to opt in or out easily, and provide dynamic consent management to update preferences anytime.
Prioritize first-party data
Focus on collecting data directly from your audience through interactions like website visits, surveys, and loyalty programs. This approach reduces reliance on third-party data, but conversely, it can raise compliance concerns.
Implement technology that enhances privacy
Utilize data anonymization and encryption to protect sensitive information. Advanced algorithms can identify trends without exposing individual data, ensuring compliance while maintaining personalization accuracy and trust among your customers.
Limit data collection and retention
Only collect the data necessary for your marketing goals and implement policies to delete outdated or irrelevant data. This minimizes the risk of data breaches and ensures compliance with data protection regulations.
Stay educated on hyper-personalization best practices
Remain up to date with the latest privacy regulations and ethical practices, and encourage a culture of accountability when handling customer data to foster trust and ensure compliance.
GoViral Conclusion
The pace of change in marketing is accelerating, and customer expectations are rising just as quickly. With increasing competition, shorter consumer attention spans, and growing demand for seamless digital experiences, hyper-personalization in marketing has become more important than ever. Brands that delay AI adoption risk delivering generic, disconnected experiences that turn customers away. But those that act now by building AI fluency, upgrading their tools, and prioritizing ethical, data-driven personalization will not only meet evolving demands but lead the way in customer engagement, loyalty, and growth.
Google’s AI Search Updates: What Marketers Need to Know
Let’s face it: there’s nothing worse than putting effort into creating something and feeling the anticipation and excitement of putting it online, only to have it not be seen by anyone. It’s hard not to take it personally, but this has little to do with how good your content actually is and more to do with getting ranked on search engines, Google being the big dog in the game.
It’s no secret that Google has a lot of power, and with the emergence of AI in their search experiences, it’s fundamentally reshaping the way we receive information online. After the rollout of Google’s Search Generative Experience (SGE), the traditional rules of SEO and digital visibility are evolving fast, which means that marketers need to pay attention and evolve with them. No longer limited to static links and simple keyword matching, Google's new AI search delivers more contextual, conversational, and predictive results, further streamlining the user experience. For marketers, this is a double-edged sword, presenting both new challenges and unprecedented opportunities to connect with audiences in smarter, more meaningful ways. In this blog, we’ll tell you why this shift matters and how you can stay ahead of the game and finally get the recognition you deserve.
What Is the Search Generative Experience (SGE)?
Powered by artificial intelligence, this new platform leverages advanced AI models to provide dynamic and intuitive search results in order to anticipate user intent. Long story short, the goal of SGE is to use AI to improve our online search experience.
Google initially announced SGE in May of 2023 at their annual I/O conference as part of a push to integrate generative AI into search. Since late 2023 onwards, they have expanded search functionality with more interactive elements like coding help, definitions, and more visual results and while it continues to evolve, it still remains in an experimental phase.
What are the key features of SGE?
AI Summaries (Snapshot)
Nowadays, you’ll probably notice that when you search for something in Google, oftentimes there is an AI-generated summary at the top of the results page. This is a concise, experimental summary sourced from multiple sites in response to complex or multi-part queries, and it’s often linked for credibility.
Insights Pane
This sidebar appears to offer deeper insights, definitions, or topics related to the original search, which can prove useful for exploring a topic in context without ever leaving the results page. What’s more, it’s often enhanced with visuals or other interactive elements, making it more engaging for users.
Conversational Follow-ups
The quest for knowledge doesn’t usually stop at the initial search. If you’re like the majority of users, you’ll have follow-up questions, and SGE has the answers. After an initial query, users can ask follow-up questions in a chat-like interface, keeping the context of the conversation contained in a chatbot and enabling a deeper search journey.
How does SGE differ from traditional SERPs?
There are many differences in the format and user flow of SGE versus those of traditional SERPs. Firstly, the links and snippets of information published in the search results of SGE are AI-generated and, as opposed to being ranked by the search engine, they are aggregated from multiple sources and displayed transparently. Additionally, the focus of a traditional SERP is to navigate users to a website, whereas the goal of SGE is information synthesis. Naturally, this makes the interactivity with the user more conversational and contextual, unlike a traditional SERP experience,e which is more static.
Still confusing? Here’s an example. In traditional search results, users typically see a list of blue links accompanied by brief snippets of text. This format requires users to click through multiple links to gather comprehensive information. The need for multiple clicks is reduced with SGE since the interface integrates AI-generated summaries directly into the search results, putting concise information at the top of the page. SGE aims to move away from a former link-centric approach to search and streamline information retrieval, making it more intuitive and context-aware.
AI‑Powered Answer Blocks and Their Impact on Click Behavior
Google AIOs are designed with the user in mind, providing immediate, synthesized information on the search results page directly. Components like text snippets and generated summaries address the user’s queries first-hand, citing and linking sources referenced in the text.
AIOs have addressed user pain points by placing ready-made information at their fingertips. Consequently, there’s been a shift in user behavior and since users no longer have to navigate from the results page to obtain relevant information, we’ve seen a reduction in website traffic.
Various studies, including one by Search Engine Journal, have noted a decline in click-through rates for organic listings, and informational queries dropped by over 7% in the last quarter of 2024. Similarly, other sources like the CEO of SparkToro, Rand Fishkin, suggest that in 2024, nearly 60% of all Google searches resulted in zero clicks due to all of the relevant information being on the results page.
While this trend has certainly caused a decline in organic traffic, paid search ads still continue to capture user attention, suggesting that businesses might need to start allocating more of the budget to SEA in order to maintain traffic.
What is Long‑Tail Content and Why is it Back in the Spotlight?
Up until now, SEO has been crucial for content to rank and reach the user, but since they’re no longer scrolling down the list of blue links to obtain information, your content won’t reach them even if it is ranked highly on Google. If your content isn’t showing up in these AI responses or isn’t structured in a way that AI can easily digest, it risks becoming invisible.
However, in the midst of this seismic change, there is one silver lining: long-tail content is becoming increasingly more valuable. But what is long-tail content? It’s any content that targets highly specific keywords or phrases that users enter into search engines. While these queries typically have less search volume, they are more precise in their intent, and because they are less competitive than broad words, it is easier to rank for them. An example of long-tail content would be “How to use Google Analytics for tracking SEO performance in 2025.” This targets a specific niche in the digital marketing world and users who want updated, advanced strategies. The benefit to long-tail content is that it targets users like these with very niche interests, connecting them with exactly what they’re looking for, which leads to more relevant traffic and an increase in conversions.
Since Google’s SGE encourages users to ask more natural, conversational queries, content that addresses these specific questions has a better chance of being featured in AI-generated results. The key is creating high-quality, intent-driven content that mirrors how real people ask real questions.
The Challenges of Broad Queries in Google’s SGE
As Google continues to evolve its search experience with AI-powered summaries, marketers face a new challenge: broad queries can sometimes lead to oversimplified or generalized answers that fail to fully address a user's specific needs. Although SGE offers quick, concise summaries, it risks leaving out crucial nuances or deeper insights that users are often searching for. For broad or vague queries, SGE pulls from various sources to produce high-level summaries that provide a general answer. This is great for users who need a quick overview, but doesn't necessarily serve those looking for detailed, authoritative content.
Here’s an example: If a user is searching for “SEO Writing Best Practices,” Google might provide a brief, AI-generated response with general tips that aren’t up to date and won’t suffice for users who are looking for in-depth case studies. This is where long-tail content comes in. By providing detailed, comprehensive answers, it can become a critical asset for cutting through these generic AI summaries and delivering the insights that users truly want. This type of content is less likely to get lost in the sea of generative summaries and attract users who want more comprehensive insights. And as Google increasingly values authoritative, well-researched content, it also has a higher chance of being featured in AI-generated responses.
Tactical Takeaways for Marketers: Adapting to Google’s GSE
As Google’s Search Generative Experience (SGE) reshapes the future of search results and how they are displayed, marketers need to take a new approach to creating content that stands out in a new, AI-driven environment. Here are some key tactics to help marketers stay ahead.
Optimizing Headings and Structured Data for AI Pull-Through
Google’s SGE relies on structured data and well-organized content to generate AI-powered summaries and rich snippets. To increase the likelihood of your content being featured in these AI summaries:
- Use clear, descriptive headings: To help search engines and AI systems easily understand and pull relevant information, structure your content using H1, H2, and H3 headings that accurately reflect what the content covers. As opposed to using a vague heading like "Tips," use something more specific like "10 Expert Tips for setting up a remote office in a small workspace.”
- Incorporate structured data (Schema Markup): Add structured data like FAQs, how-to schemas, and product reviews to your content to make it easier for Google’s AI to extract the most relevant answers for search queries. The richer the results, the greater the likelihood of appearing directly in AI summaries or answer blocks. To implement this, you can use Google's Structured Data Markup Helper.
- Write answer-focused sections: Format your content with clear, concise answers directly under headings. This increases the likelihood of your content being featured as a direct answer or snippet within SGE. For example, in a guide about "How to start a blog," include a section that clearly answers the question in a short paragraph or bulleted list, kind of like what we're doing in this blog right now!
Balancing Short “Snackable” Answers with Long-Form Deep Dives
While long-form, in-depth content is essential for comprehensive topics, Google’s SGE prioritizes quick answers for short, broad queries. Therefore, marketers are tasked with striking a balance. That means creating content that caters to immediate queries as well as that for users looking for in-depth, authoritative answers.
"Snackable" content consists of short, clear, and concise answers between 40-100 words. Ensure the answer to the question is directly stated within the first few sentences of the content, as SGE may pull this information into its AI-powered summary. A prime example could be a search query like “What is SEO?” which could lead to a quick definition presented in a short paragraph at the top of a page.
For more complex topics, long-form content of 2000 words or more is essential for a deep dive into the subject matter. Use long-form content to provide thorough explanations, case studies, or step-by-step guides to establish authority because, above all, Google still values authoritative, well-researched content.
One way to create balanced content is to use a layered content strategy where short, snappy answers serve as an introduction or summary, followed by a detailed, long-form explanation, catering to the needs of all users in one go.
Measure Performance by Combining Google Search Console, Analytics, & AI-Metric Tools
As AI-powered search continues to dominate, traditional metrics like clicks and impressions may not provide a full picture of how your content is performing. Here’s how you can leverage a combination of tools to effectively measure success and optimize your strategy:
- Google Search Console: Use this tool to track rankings, impressions, and click-through rates in SGE and look out for increases in zero-click searches. This means users are engaging directly with Google’s AI-generated responses instead of clicking through to your website. If you see high impressions but low CTR, your content may need to be optimized for better AI pull-through.
- Google Analytics: Analytics helps to monitor user behavior, including how long people are staying on your pages, bounce rates, and conversion rates. If your content is being featured in an AI-generated snippet, users may not visit your site, but they may interact with your brand in other ways, like through your social channels or brand searches. You can gauge effectiveness by measuring engagement beyond click-throughs.
- Other AI-driven performance tools: The best way to beat AI is to leverage it. AI-driven content optimization tools like Clearscope or SurferSEO provide information about how Google’s AI evaluates content, with insights into keyword relevance, content structure, and readability. You can also keep an eye on AI-powered SERPs with tools like RankRanger or SEMrush that track how often your content is being featured in AI-generated summaries.
GoViral Conclusion
Just like life, search behavior changes and continues to change every day. This also means that if marketers want their content to be seen, they have to change with it and stay on top of search behavior trends. The emergence of Google’s SGE requires marketers to regularly audit content and optimize it for AI visibility by focusing on creating niche, long-tail content that addresses specific user needs. By staying agile and creating high-quality, targeted content, marketers can thrive in this new, AI-driven search landscape. You know the saying, if you can’t beat ‘em, join ‘em!
5 Signs to Identify AI Content on Instagram
When you want to find out an answer quickly, which do you go to first – Google or Chat GPT? Gemini?
Artificial Intelligence is integrating itself into every aspect of life and speeding up processes that used to be time-consuming.
The marketing space has been overtaken with AI as many are using it for project management tools, scheduling, and the mundane tasks that come with doing admin. A huge area that AI has slid into is the content creator space, especially on Instagram.
Meta has unlocked many features this year, from chatbots to AI-Generated Images.
AI is helping businesses and creators create content in a quicker timeframe but what are the concerns around AI? How do we know what’s real and what’s AI-Generated?
In this month’s blog, we’re going to discuss the advancement of AI, 5 signs that an Instagram post was made using AI, and what does “made with AI” mean attached to an image.
Let’s dive into it.
The Advancement of AI on Instagram
Did you know AI integration on Instagram began in 2010?
The filters to adjust your images brightness or add a different tone to the image is all done by AI.
The suggested accounts that pop up in your feed exist because of Instagram’s machine-learning algorithm.
When you hear people talk about keywords, SEO, hashtags, this is the reason. In 2010, the algorithm was at its most basic, using image recognition to categorize photos based on content.
As Instagram switched from its basic capabilities to more advanced, machine learning techniques like “word embedding” analyze the content and categorize it in terms of relatedness.
Who said words don’t matter? Your bio (the small description you have on your instagram), the captions, the hooks, the copy under the post is all being scanned to categorize your account.
So pay attention to your words!!
The transformation of AI capabilities since 2010 to the present moment in 2024 have become far more advanced at a rapid pace.
Let’s take a look at the timeline.
Between 2010 and 2014, AI was focused mainly on image enhancement like brightness and contrast, cropping your photos, personalized content based on your interactions and preferences.
From 2015 to 2019, the features began to roll out quicker.
- The explore tab was enhanced using a more advanced machine learning algorithm, improving personalization and content discovery.
- “Word embedding”, keywords and hashtags, helped analyze the content to recommend accounts based on interactions.
- Seed accounts, created by instagram,used to test new features, algorithms, or functionalities before rolling them out to the general public.
- AR and Virtual reality began to be rolled out as well as the use of NLP, Natural Language Processing, machines to scan for offensive comments and limit spam and misinformation.
- The birth of IGTV with AI recommendations personalized video content for each user based on interactions and preferences.
- AI-Enhanced shopping features were created to recommend products and target ads based on user behavior.
From 2020 onwards, the creation of tools for content creation like automated caption suggestions and hashtag recommendations began to roll out as well as conversational AI assistants that would answer queries, provide content recommendations and help in generating posts.
In 2024, the rollout of Meta AI on Instagram is even more powerful compared to what it was in 2010.
The implementation of AI on the platform has saved content creators, businesses, brands hours of work but a major issue that has caused controversy is when people use it for potential harmful reasons.
Because of the advancement of AI, it now has the ability to create images that look eerily real, making it harder to know what’s AI created and what’s REAL content.
This is down to generative AI which works by creating new data that mimics human creations.
If you struggle to distinguish between superficiality and reality, never fear because here are our top five signs that an image was AI-Generated.
The 5 signs of an AI-Generated Image
We all want to know whether an Instagram post is made with AI because Instagram is known to be the “highlight” reel, the best bits.
Ensuring the user is informed about what’s AI-Generated and what’s not is something Mark Zuckerberg, head of Meta, wants to make sure is maintained.
Here’s five pointers to spot AI going from the easiest to hardest to spot.
1. Distorted Backgrounds:
If a door looks like it has a bump in it or is twisted into another shape, it’s a clear indication something was done to the image. This is one of the easiest ways to spot an image was
2. Something feels.. Off
We wrote about AI influencers in our last blog (read more on it here) and there’s a slight unease when looking at the image.
They say you can read a person by looking into their eyes. When you see an AI-Generated image of a “person”, there’s nothing.
Almost like a blank stare. Like it’s not… real. AI influencers on Instagram have risen in popularity with many brands like Chanel and Red Bull using them for online promotions.
3. Pay attention to the small details
We’re scrolling 24/7 (hopefully not!), and are scanning images and text at a quicker pace. This means we miss the finer details.
Don’t take an image at face value if you feel like something isn’t right. AI images are created by taking data from other photos and creating a new image.
AI programs often struggle with details you may see less frequently in other pictures.
AI tends to get the smaller details like logos and text wrong as you can see below.
4. If it looks too good to be true, it probably is
As much as we want to look perfect all the time, we don’t. We’re humans, imperfection is perfection.
You can tell if something is too good to be true if the image looks smooth and no texture because well, we have texture.
So don’t be fooled by an image looking picture perfect because it most likely isn’t real.
5. Reverse Search
You know the TV show “Catfish”? If you don’t, it’s a programme where the hosts bring on a person who writes to the show about their relationship.
They write in because they’ve never met the person and want to make sure they are the real deal. One of the first things the hosts do is reverse search the image.
They do this because they want to see if the person is using someone else’s photos.
This is the same when it comes to images on Instagram.
If in doubt, reverse image search because if it is AI-Generated, there won’t be a lot of information which tells us something is fishy.
“Made with AI”
You might have spotted on Instagram recently under some images and videos are a tag saying “Made with AI”.
This feature was recently introduced however, there has been some controversy especially for photographers.
How it works
When you upload an image to Instagram, the algorithm will read the metadata of the image to clarify whether it’s real or AI-generated.
This is great BUT what if you just enhanced the brightness? Enhanced the sound so it’s clearer? Removed the blemish from your face that was annoying you?
Instagram smacked the tag of “made with AI” on it. The problem for photographers is the wording.
Saying an image was made with AI sounds like it was superficial, that they didn’t put the hard work into capturing a beautiful moment like a wedding or someone’s birthday.
Some argued for it to be changed to “edited with AI” which clarifies that the image has been enhanced but it was taken professionally, not created superficially.
Because of the backlash, from July this year, the made with AI feature has been changed to AI Info.
This is a step in the right direction to distinguish the difference between an image being AI-Generated and one that’s a real-life photo.
GoViral Conclusion
Our advice is to always be cautious about images that are posted online.
If something isn’t sitting right with you, there’s a reason why. Our five pointers will give you more clarity around AI-Generated photos and with the introduction of “AI Info” on images,
It can help you distinguish between superficial and reality.
As many new features are being added to social media platforms every day, it’s important to stay up-to-date with the new trends and implementations.
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