--- tytul: "How to Use AI for Marketing Personalisation" opis: "AI-driven personalisation isn't just a first name in the subject line. See what the model actually decides, and how to label AI-generated content from 2 August 2026." adres: https://justidea.agency/en/blog/ai-personalisation-in-marketing/ serwis: "JustIdea Agency" jezyk: pl zrodlo: automatyczny odpowiednik strony HTML, generowany przy każdym buildzie --- Article # How to Use AI for Personalisation in Marketing Communication AI-driven personalisation isn't just a first name in the subject line. See what the model actually decides, and how to label AI-generated content from 2 August 2026. [![Wiktoria Władarz](https://justidea.agency/obrazy/wiktoria-wladarz-e1767793660464-7c4ecd3a.webp) Wiktoria Władarz Marketing Communications Specialist](https://justidea.agency/en/author/wiktoria-wladarz/) 13 August 2026 14 min read 14 sections ![How to use AI for personalisation in marketing communication?](https://justidea.agency/_astro/frame-514-2-9bf16fd4.DW4Gyvg_.webp) Marketing Trusted by ![LPP](https://justidea.agency/obrazy/lpp-logo-2-b64d3a02.svg) ![House](https://justidea.agency/obrazy/logo-house-c706eeae.svg) ![Toyota](https://justidea.agency/obrazy/toyota2-1-63c6570a.webp) ![Fame MMA](https://justidea.agency/obrazy/fame-ffb4e0d0.webp) ![Surf Inc.](https://justidea.agency/obrazy/surfinc-91e5b73d.webp) ![LyoFood](https://justidea.agency/obrazy/lyo-cb899d46.webp) ![ExpertSender](https://justidea.agency/obrazy/expert-ca00548d.webp) ![Purinova](https://justidea.agency/obrazy/purinova-217da6ab.webp) ![Mennica Gdańska](https://justidea.agency/obrazy/mennica-98166c12.webp) ![Group IB](https://justidea.agency/obrazy/group-ib-b711171e.webp) ![Smart Kids Planet](https://justidea.agency/obrazy/smart-kids-151311c5.webp) ![SuperSonic](https://justidea.agency/obrazy/supersonic-a35404fe.webp) ![Wierzynek](https://justidea.agency/obrazy/wierzynek-1-d027e7cc.webp) ![RegoBis](https://justidea.agency/obrazy/regobis-a8b446e3.webp) **In this article** 1. 01In short 2. 02What is AI-based communication personalisation? 3. 03Decisions the model takes over 4. 04Touchpoints where AI changes the message 5. 05The data the model works on 6. 06Generative content personalisation at scale 7. 07Chat and voice assistants: personalisation in real time 8. 08Personalisation on the website and in the product 9. 09Personalisation on the advertising platform side 10. 10Examples of AI personalisation in practice 11. 11Labelling AI in communication: new rules from 2 August 2026 12. 12Where AI personalisation stops 13. 13AI chooses the form, the content is still the brand's decision 14. 14FAQ: AI personalisation in marketing Share ## In short - The model doesn't invent the offer. The brand sets the facts, scope and tone, and AI decides which argument, in what length and at which touchpoint, reaches a specific person. - Personalisation is built on events, not on form declarations: what matters is what the recipient did, not what they once ticked on a form. - From 2 August 2026, AI-based content and assistants must be labelled, so labelling rules belong in the brief, not just the footer. A first name in the subject line alone is no longer enough to call something real personalisation. Recipients quickly sense whether a message has actually been tailored to their needs or is just another mass mailing. AI lets you approach personalisation much more broadly. It can support the choice of content, format, timing and channel depending on the recipient's behaviour and situation. The question is: which of these applications actually deliver value today, and which still remain mostly a nice-sounding promise? ## What is AI-based communication personalisation? **AI-based communication personalisation means adapting content, form and timing using models that learn from the recipient's behaviour. It covers generating message variants, choosing content on a website, conversational assistant replies, and the order of messages within a product.** Classic personalisation was based on declarations: a first name, a city, a category ticked on a form. The model works on different material, namely a sequence of events. What matters is what the user browsed, where they stopped, what they reacted to, and what they consistently skip. The difference is a practical one. Segmentation splits a database into groups and treats everyone in a group the same way. A model ranks recipients within a group, so two people from the same segment can see two different messages. ## Decisions the model takes over AI personalisation can cover various elements of communication: from the content itself, through its form and language, to the placement and sequence of contact with the recipient. Not every one of these areas has to be automated to the same degree, though. The table below shows what to leave to the marketer and what the model can take over. | Decision | What the marketer sets | What the model decides | | --- | --- | --- | | Content | offer scope, facts, brand guidelines | which argument and variant reaches a given person | | Form and language | register, tone, excluded phrasing | length, level of detail, language version | | Touchpoint | available contact points and their role | where to show the message or what to reply with | | Sequence | journey goal and checkpoints | which step to take next | What matters most is the data the model bases its decisions on. Without up-to-date context, behavioural history and clearly defined rules, personalisation quickly becomes random. ## Touchpoints where AI changes the message Personalisation can work in far more places than just email. In practice, it covers every touchpoint where the system has data about the user and can use it to tailor the message, the offer or the next step: - website and store: product order, section selection, search results, - product or app: messages that depend on what the user has already done, - chat and voice assistants: replies based on a specific person's context, - email and messaging sends: choosing the content variant and the moment of contact, - advertising: creative and audience selection on the platform side, - sales: materials and arguments tailored to a specific account. [Marketing automation](https://justidea.agency/en/services/marketing-agency/marketing-automation/) remains the most mature area, where personalisation has been based on user behaviour data for years. AI extends these mechanisms with, among other things, predicting the next action, choosing the moment of contact, and creating content variants. We describe [how marketing automation works in practice](https://justidea.agency/en/blog/what-is-marketing-automation/) in more detail in a separate article. ## The data the model works on Personalisation quality isn't determined by the tool alone, but above all by the data that feeds into it. ### Events, not just declarations Behavioural data is the foundation. It's worth collecting things like content and product views, clicks in key places, search queries, moments when a form was abandoned, and stages completed within the product. What matters isn't just what the user did, but also when and in what order. Negative signals matter just as much. What someone regularly doesn't open, doesn't click or skips also helps you understand their preferences better. ### One recipient identity across multiple systems A store, a CRM, an email tool and a website chat often record the same person differently. As a result, instead of one complete history, the model gets several disconnected fragments. So set a shared identifier, such as an internal customer number or, where it makes sense, an email address, and use it to connect data from different sources. The lack of that connection quickly becomes visible to the customer. The chat assistant doesn't know about an order placed an hour earlier, and the site recommends a product the user has just bought. ![The data you need for AI-driven communication personalisation](https://justidea.agency/_astro/chatgpt-image-31-lip-2026-08-29-18-1024x576-9190af9d.DLwpjB2Z_ZhYi16.webp) The data you need for AI-driven communication personalisation ## Generative content personalisation at scale A model can produce five versions of the same message in a few minutes. The key question is: when are these versions created, and who sees them before the recipient does? ### When the text is created There are two main approaches. They differ mainly in the level of control involved. In the first, content is created on the fly, at the moment of contact. The user opens the page, and the model puts the message together based on the available data and context. No one has seen exactly this version before. You get maximum tailoring, but you also take on the risk that any mistake is seen by the customer straight away. In the second, the model prepares variants in advance. The team reviews them, rejects the weaker ones and approves those that can reach recipients. The system then only picks from that pool. Personalisation is less flexible, but you know exactly what can be shown. For messages that create a specific commitment, such as offers, prices, deadlines or complaint terms, the second approach is safer. Generating on the fly works better where the content is descriptive in nature: answering questions, summaries, or search suggestions. Whichever way you work, record which version a specific recipient saw. Without that, it's hard to later reconstruct exactly what was communicated to them. ### Personalise a fragment, not the whole message Rewriting the entire content for every group usually isn't necessary. It's better to break the message down into parts and check which of them actually need to change. The brand's core promise usually stays the same. What can change is the evidence, the example or the argument. The same service described through a manufacturing implementation and through an online store implementation will be perceived differently, even though its core value hasn't changed. Headlines on landing pages need particular care, especially in paid campaigns. The user clicks the ad and, in the first instant, checks whether they've landed in the right place. A headline matched to the earlier message reinforces that continuity. One generated from scratch can break it. ![Generative content personalisation](https://justidea.agency/_astro/chatgpt-image-31-lip-2026-08-33-15-1024x683-034b2bcd.BGA4CBz5_WbLp8.webp) Generative content personalisation ### Guidelines that actually help the model General phrases like “friendly but professional” don't give you much. They're too broad, and everyone can interpret them differently. Concrete examples work much better: a few excerpts that capture the brand's style well, and a few that the team wouldn't approve for publication, with a short explanation of why. On top of that, it's worth adding a list of excluded phrasing, how products and features are named, the form of address used with recipients, and rules for writing units and dates. ## Chat and voice assistants: personalisation in real time Chat and voice assistants differ from most other touchpoints in that the reply is created during the conversation itself. There's no time to edit it or approve another version. That's why communication quality is determined above all by the context the model has about the customer, and by clearly defined limits on what it can say. ### Context shortens the conversation An assistant with no access to data starts with questions: order number, address, purchase date. The same assistant connected to order history, shipment status and previous tickets can ask straight away whether this is about Tuesday's order. The customer says “yes”, and several unnecessary exchanges disappear. This is one of the simplest ways to check whether personalisation in this channel actually works. Check how many messages it takes to resolve a case, and how often a conversation needs to be handed over to a human agent. Data like this shows the effect faster than a rating left after the chat ends. ### Where the assistant's role ends Set the scope of what the assistant can say before launch. Decide what it can talk about freely, at which topics it hands the conversation over to a human, and what it should never confirm: discounts outside the price list, deadlines not in the system, or interpretations of a complaint. Plan the handover to a human agent as a normal step, not a failure. A customer who has received an evasive answer three times rarely comes back for a fourth. ## Personalisation on the website and in the product On a website, the effect of personalisation is visible almost immediately, because the system can react to what the user is doing right now. Based on recent views, clicks or searches, the model can change the order of products, which sections appear on the homepage, or search results. Campaign landing pages are a separate area. The headline, examples or evidence section can be tailored to the traffic source, the search query, or the industry the user comes from. With a handful of campaigns, you can prepare such variants by hand. With a dozen or more, it turns into a separate process that AI can speed up considerably. ![Personalisation on a website](https://justidea.agency/_astro/chatgpt-image-31-lip-2026-08-35-38-1024x512-d2aea1e1.BPxb2Bn0_Z1YGGfh.webp) Personalisation on a website In digital products, personalisation goes even deeper, because it affects the interface itself. A message doesn't have to depend on how many days have passed since sign-up, but on what the user has actually done. If they haven't connected their data yet, they'll see a prompt about importing it instead of an invitation to a reporting webinar. This way, the next message follows from the stage they're actually at. ## Personalisation on the advertising platform side In paid channels, a large part of personalisation today happens on the advertising platforms' side. The advertiser supplies the creative, the data, and the campaign goal, and the system decides who to show the ad to, where, and in what combination. What's left for the team, then, is mainly the quality of the input materials and control over what the algorithm can draw on. We cover this area in more detail in separate articles. You'll find more in our pieces [on AI features in Google Ads](https://justidea.agency/en/blog/ai-features-in-google-ads/) and [on Meta Ads formats](https://justidea.agency/en/blog/meta-ads-ad-types-formats-2026/). ## Examples of AI personalisation in practice The examples below show how personalisation can work across different business models and at different stages of contact with the recipient. ### Insurance A policy is hard to describe with a single message, because some recipients are mainly looking for the scope of cover, and others for price and paperwork. The model can adjust the level of detail based on which pages the user viewed and for how long. Someone comparing terms will see more detailed information about coverage, while someone returning to the calculator will see a shorter message with the next step. ### Education platform In online courses, actual user progress matters more than the schedule itself. The model can spot where someone slowed down or stopped learning, and tailor supportive content accordingly. Someone who's completed half the material needs a different message from someone who hasn't opened the first lesson yet. ### A brand in several markets In [e-commerce](https://justidea.agency/en/services/e-commerce/) operating across several countries, personalisation starts with language, but doesn't stop there. Generative models speed up preparing versions for individual markets, and behavioural data helps you check which arguments and examples work best in a given country. ### B2B: sales materials tailored to industry In B2B sales, personalisation often starts even before the conversation with the client. The model can gather public information about the company, select implementation examples from a similar industry, and prepare a version of the offer with arguments relevant to that specific organisation. The salesperson then walks into the meeting with material prepared for that client, not a generic presentation. ## Labelling AI in communication: new rules from 2 August 2026 Model-based personalisation falls within the scope of new obligations. Article 50 of the EU AI Act applies from 2 August 2026 and covers three issues that matter for customer communication: telling recipients they're dealing with an AI system, technically labelling content generated or modified by AI, and disclosing the use of emotion recognition systems. The scope of these requirements is set out in the [European Commission's guidelines](https://digital-strategy.ec.europa.eu/pl/policies/guidelines-transparency-ai-generated-content). For the marketing team, this means concrete housekeeping. A website assistant has to clearly communicate that a system, not a human agent, is on the other side. The obligations apply not only to AI system providers but, in certain cases, also to the companies that use them, so a vendor's assurance that a tool is compliant isn't always enough on its own. ![Labelling AI in communication](https://justidea.agency/_astro/chatgpt-image-31-lip-2026-08-38-14-1024x683-1f133c56.DDfbf6Ih_Z1yVPDM.webp) Labelling AI in communication ## Where AI personalisation stops The first boundary is brand tone. Generated text can be correct and yet completely without character. With a dozen or several dozen variants, it's also easy to let through phrasing the brand would never use in normal communication. The second boundary is factual accuracy. A model can very convincingly describe a feature that doesn't exist, or state a deadline that's already changed. In customer communication, a mistake like that can cost far more than an error in a blog post. The third boundary is the feeling of being watched. A message that reproduces the user's behaviour too precisely can feel off-putting, even if everything was technically done correctly. A simple rule works well here: show the conclusion, not the data it was based on. Finally, there's measurement. It's best to compare personalisation against a control group and judge it against a single business metric, such as [conversion rate](https://justidea.agency/en/services/marketing-agency/web-analytics/), revenue per recipient, or the number of closed tickets. Engagement metrics alone aren't enough, because they can rise even when communication grabs attention but doesn't lead to action. ## AI chooses the form, the content is still the brand's decision AI personalisation today does what there was often no time for before: it creates many variants of a message, adapts website content in real time, and replies in chat taking the context of a specific case into account. The model won't decide for the brand what it wants to say, or where its promises end, though. It can indicate that a recipient is ready for the next step, but it won't determine whether that step actually makes sense. The best implementations share a similar foundation: well-organised data on the company's side, decisions about content and boundaries made by people, and a clearly defined scope within which the model can operate. **Also worth checking:** [What are the AI features in Google Ads?](https://justidea.agency/en/blog/ai-features-in-google-ads/) [What are the Meta Ads formats? Formats for 2026](https://justidea.agency/en/blog/meta-ads-ad-types-formats-2026/) [What is marketing automation and how does it work in practice?](https://justidea.agency/en/blog/what-is-marketing-automation/) ## FAQ: AI personalisation in marketing **How does AI personalisation differ from classic segmentation?** Segmentation splits a database into groups based on conditions set by a person, and treats everyone in a group the same way. AI personalisation ranks recipients within those groups based on behaviour and predictions. As a result, two people from the same segment can see different content and a different form of message. **What data does AI-based personalisation need?** Timestamped events are key: views, clicks, on-site search queries, stages completed in the product. On top of that, you need a single field connecting these events to that person's profile in other systems. Declared data from a form alone isn't enough, because it says nothing about the recipient's current situation. **Do you have to tell recipients they're talking to an AI system?** Yes, and from 2 August 2026 this follows from the transparency obligations in Article 50 of the AI Act. The provision also covers technically labelling certain AI-generated content and disclosing emotion recognition systems. The scope depends on the type of material and how it's used, so each application is assessed separately. **Will AI keep the brand's tone across a large number of variants?** Only if it's given documented rules: register, sentence length, excluded phrasing and how products are named. 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