У 2026 виграє той, у кого є система маркетингу і продажів (gtm).
За 4 роки ринок під агенціями перевернувся.
Вакансій розробників у 2026 вдвічі менше, ніж у 2022. Агенцій стало на 25% більше. А ціна за лід виросла у 2–3 рази.
І що роблять більшість фаундерів у відповідь?
Шлють ще більше холодного аутрічу в нікуди. Зрізають ціну, бо «треба хоч якось закрити квартал».
Копіюють те, що в конкурента на сайті.
«Нам терміново потрібні ліди» → рандомні активності → через три місяці «маркетинг не працює».
Я бачив це в десятках агенцій.
Проблема майже ніколи не в каналі. Проблема в тому, що немає системи.
- Не визначений сегмент. - Не описаний ICP і хто реально ухвалює рішення. - Не знайдена «дорога» проблема, за яку клієнт готовий платити. - Немає портфоліо оферів.
А лідген б'є мимо: 95% активностей повз.
GTM-стратегія працює інакше.
Це каркас, який фокусує ваші й так обмежені ресурси на найефективніших діях. І дає скейлити ревеню без роздування бюджету й команди.
Я зібрав цей каркас у 9 кроків: від вибору сегментів до каналів лідгену.
Звіртеся зі своєю агенцією щодо нього. На якому кроці у вас зараз найбільша діра?
Лояльність еволюціонує: бренди відходять від суто транзакційних винагород і переходять до персоналізованої, орієнтованої на досвід взаємодії. Такі нові вектори, як агентний ШІ, імерсивний мікроконтент, винагороди, побудовані на виборі, та велнес-орієнтовані стимули, переосмислюють те, як створюється й доставляється цінність.
Водночас зростає фокус на споживачів із високим рівнем добробуту, які очікують індивідуальних, преміальних і максимально релевантних рішень. У сукупності ці зрушення дають брендам змогу вибудовувати глибші емоційні зв’язки, підвищувати релевантність і забезпечувати довгострокове утримання клієнтів.
Euromonitor окреслює ключові тренди, що формуватимуть ландшафт лояльності у 2026 році, та пояснює, які сили стоять за цими змінами.
1. «Loyalgentic»: агентний ШІ перетворює лояльність на взаємодію в реальному часі
Штучний інтелект радикально змінює лояльність, перетворюючи потоки даних на прикладні інсайти в режимі реального часу. Лояльнісні агенти на базі ШІ — як-от Sparky від Walmart, запущений у 2025 році, — автоматизують нарахування переваг, погашення бонусів і бронювання, роблячи досвід участі безшовним. Цю динаміку підкріплює той факт, що 70% глобальних споживачів щотижня користуються голосовими асистентами, а 41% уже віддавали голосову команду для здійснення покупки (згідно з «Voice of the Consumer: Loyalty Survey» від Euromonitor; далі — «Loyalty Survey 2025»).
У міру того як розмовні інтерфейси стають ключовими воротами до взаємодії з брендом, компанії з відкритою API-інтеграцією отримуватимуть додаткову частку ринку.
2. Лояльність як велнес-подорож
Програми лояльності зміщуються від транзакційних винагород до цілісних екосистем добробуту, інтегруючи фітнес, харчування та ментальне здоров’я в повсякденні рутини.
Наприклад, компанія приватного медичного страхування Vitality у 2025 році посилила свою програму за допомогою Google Cloud AI та цілей, пов’язаних зі сном, через платформу Aura, винагороджуючи здорову поведінку й стимулюючи довгострокову залученість. Це знаходить відгук у споживачів: у 2025 році 60% глобальних респондентів долучилися до кількох платних підписних програм різних торговців, а велнес став загальноприйнятим очікуванням.
3. Сила винагород, побудованих на виборі
Персоналізовані траєкторії накопичення та гнучке погашення винагород повертають контроль споживачам. Згідно з «Loyalty Survey 2025», 54% опитаних у світі погашають винагороди щонайменше раз на місяць, причому понад 60% серед покоління Z та міленіалів є лідерами за цим показником.
Програма лояльності Atmos від Alaska Airlines, запущена в серпні 2025 року, дає учасникам змогу самостійно налаштовувати способи нарахування та використання балів, тоді як United Overseas Bank пропонує миттєве транскордонне погашення бонусів. Така гнучкість поглиблює залученість і формує екосистеми, з яких складно вийти.
4. Мікроконтент — макролояльність
Короткий цифровий контент і мікродрами стають потужними інструментами лояльності, захоплюючи увагу та стимулюючи емоційну залученість.
Taobao — одна з найбільших онлайн-платформ Китаю — інтегрує цей формат у свою членську екосистему. Через платформу DianTao (Taobao Live) користувачі можуть переглядати безкоштовні мікродрами й отримувати винагороди, які можна вивести через Alipay або витратити на Taobao, перетворюючи пасивне споживання контенту на відчутну цінність.
Водночас у жовтні 2025 року Douyin (китайський TikTok) у співпраці з C-beauty-брендом Winona провів спеціальний стримінговий епізод мікродрами «Great Grandma 3» з можливістю миттєвої покупки продуктів, прив’язаних до сюжету.
Наведені приклади ілюструють, як мікродрами та лайвстрими стають моментами активації лояльності. У результаті лояльність дедалі частіше формується в точці уваги — там, де імерсивний контент конвертує перегляд у вимірювану залученість, комерцію та повторну поведінку.
Загалом у 2025 році 27% споживачів у всьому світі купували товари або послуги безпосередньо з відеороликів TikTok, що свідчить про силу цифрових точок контакту в стратегіях лояльності.
5. Лояльність для споживачів із високим рівнем статків
Програми лояльності посилюють фокус на заможних клієнтах, пропонуючи ексклюзивність, персоналізовані винагороди та кастомізований досвід.
Запуск у липні 2025 року Kotak Solitaire від індійського Kotak Mahindra Bank — запрошувальної програми для ультрапреміальних банківських клієнтів — призвів до зростання середніх щомісячних витрат на 38%, а 80% нарахованих авіамиль було використано на подорожі. У міру того як глобальний люксовий ринок зміщується в бік досвідної цінності, бренди використовують технологічно підсилену ексклюзивність для поглиблення взаємодії з клієнтами з високою цінністю, особливо на швидкозростаючих ринках, таких як Індія.
Стратегії успіху в умовах трансформації лояльності
У міру ускладнення стратегій лояльності конкуренція посилюється, а способи створення довгострокової цінності множаться. Перехід до безшовної, персоналізованої взаємодії — підживлений даними, цифровим контентом та інтеграцією велнесу — підвищує очікування клієнтів.
Бізнесу необхідно балансувати між приватністю та персоналізацією, управляти складними міжекосистемними партнерствами й забезпечувати, щоб лояльність залишалася активом бренду, а не «товаром», контрольованим агентами. Успіх залежить від здатності надавати безперешкодний, емоційно резонансний досвід, який адаптується до змін споживчої поведінки.
Компанії, що інвестують в інтероперабельність, гнучкі винагороди та lifestyle-орієнтовані ціннісні пропозиції, матимуть найкращі позиції для закріплення лояльності в динамічному середовищі.
Generative AI (Generative Artificial Intelligence) is a subset of artificial
intelligence technologies capable of not just analyzing existing data, but creating
entirely new, original content.
Unlike classical AI, which is trained to recognize
objects (e.g., distinguishing a cat from a dog in a photo) or predict values
(e.g., estimating real estate prices), generative AI creates unique text,
images, software code, music, 3D models, and videos.
At the core of this technology lie highly complex
mathematical models (neural networks). These networks "learn" from
gigantic volumes of human-created information, identify hidden patterns within
it, and use them to generate new objects.
🧠 The Main Difference: Discriminative
vs. Generative AI
To understand the core concept, let's compare both
approaches using a simple banking example:
Discriminative
AI (Classical):
Analyzes a user's USDC transactions and states: "This $10,000
operation looks like fraud with an 87% probability." It
classifies and separates data.
Generative
AI (New): Receives
a task and writes: "Generate a detailed, step-by-step suspicious
activity report for this client to be sent to the financial regulator in
PDF format." It creates content from scratch.
📊 How It Works "Under the
Hood" (In Simple Terms)
Generative AI does not copy other people's work from
the internet (like a search engine) and does not assemble collages. It operates
based on statistical probability.
The
Training Stage (Pre-training): Neural networks are "shown" millions
of examples (texts, pictures, or code). The model does not memorize them
character by character; instead, it builds an internal map of connections
and meanings. It learns how human speech is structured or the laws of
composition in painting.
Generation
on Demand (Inference): When you write a request (a prompt), the AI begins to predict what
the next element should be.
In text (LLMs), the model literally guesses
each subsequent word (token) based on the context of the previous
ones.
In images (Diffusion models), the model starts
with a baseline of random digital noise (blurred pixels) and step-by-step
"manifests" a clear picture out of it that matches the
description.
🎨 Main Modalities (Data Types) of
Generative AI
Technologies are categorized by the type of content
they generate:
Text
(Text-to-Text): Large
Language Models (LLMs) such as GPT-4, Claude, and Gemini. They write
articles, summarize documents, hold dialogues, and translate languages.
Code
(Text-to-Code): Models
trained on programming languages (GitHub Copilot, specialized versions of
GPT/Claude). They write smart contracts for blockchain, find bugs, and
convert code from Python to C++.
Images
(Text-to-Image):
Midjourney, Stable Diffusion, DALL-E 3. They create realistic photos,
website interfaces, and concept art.
Video
and Audio (Text-to-Video/Audio): AI video generators (Sora, Runway) and sound
generators (Suno, Udio). They create video clips based on text
descriptions or write full songs with vocals.
💼 Applications in Business and
Fintech (RWA and BFSI)
When mapping Generative AI onto your previous
questions about tokenization platforms and trends in BFSI,
generative AI fundamentally shifts how companies operate:
Automating
Legal Routine (LegalTech): Generative AI can draft an investment memorandum
(Offering Memorandum) for building tokenization in seconds, relying on
asset parameters and regulatory templates.
Synthesis
and Analysis of Reporting: A financial analyst uploads a 500-page annual
report from a custodian bank, and the generative model provides an
Executive Summary with key risks and charts in 2 minutes.
Hyper-Personalized
Marketing:
Automatically creating unique advertising banners and pitch emails to buy
tokenized government bonds for each specific investor based on their
profile and investment behavior.
⚠️ Challenges and Limitations of
Generative AI
The technology is not yet perfect, and it is crucial
to consider three main risks when implementing it into commercial products:
Hallucinations: AI is wired to always try to
give an answer. If it lacks precise data, it might generate a fictional
but highly convincing "fact" (e.g., inventing a non-existent legal
article). To combat this in business, developers use RAG
architecture (connecting the AI to a verified database).
Data
Privacy: If you
send confidential financial statements of your tokenization platform to a
public API (like the free version of ChatGPT), this data can be used by
OpenAI to train future models, leading to data leaks. For commercial
tasks, businesses always use closed Enterprise accounts or deploy
Open-Source models on their own servers.
Copyright: Since AI was trained on
human-created images and texts, major copyright holders frequently sue AI
developers. Using AI-generated content in commercial products requires
careful legal analysis.
The generative AI trends defining 2026 are agentic workflows that act rather than answer, multimodal models as the default interface, cheaper inference pushing generation to the edge, open-weight models closing the capability gap with frontier systems, and enforced regulation replacing voluntary guidelines. Below, each trend gets a plain explanation, a current example, and what it changes about your build decisions this year.
Generative AI produces original text, images, audio, code and video by learning patterns from large training datasets. Unlike classification or prediction models, it creates new output rather than labelling existing input.
Understanding Generative AI
A subfield of artificial intelligence known as generative, AI uses patterns found in enormous databases to produce original material, including text, photos, music, and movies. Generative AI aims to provide creative and human-like outputs, in contrast to classical AI, which mostly makes predictions or classifies data. Generative AI, made popular by models like open AI’s ChatGPT and DALL-E, uses sophisticated neural networks, specifically transformer architecture to produce content that is logical and sensitive.
According to a PwC analysis, Generative AI is expected to account for a sizeable amount of $15.7 trillion that artificial intelligence could provide to the world economy by 2030, driving the need for legacy modernization tools to integrate AI into existing systems seamlessly.
Industries are transforming with the help of generative AI, which marketers use to automate campaigns and creation, authors to create articles, and medical experts to investigate its possibilities in diagnostics generative, AI is essentially an entirely novel technology that is changing how we invent and produce. As a developed, appropriately utilizing its potential requires an awareness of its advantages, disadvantages, and social effects.
Top Generative AI Trends For 2026
In 2026, generative AI is rapidly transforming from a promising technology to a value-adding asset. Let’s examine how a major telecommunications company in the Pacific region successfully harnessed this potential.
The company appointed a Chief Data and AI Officer to lead the initiative, recognizing the strategic importance of data and AI for business growth. In collaboration with the organization, the officer developed a comprehensive roadmap, identifying home servicing and maintenance as a priority domain. The goal was to create a generative AI tool that would enhance the abilities of dispatchers and service operators to accurately predict service requirements.
To bring this vision to life, cross-functional teams were assembled, combining expertise from various departments. Additionally, an academy was established to provide employees with the necessary data and AI skills. The officer strategically selected a large language model and a cloud provider to support these endeavors. A robust data architecture was implemented to ensure reliable and timely data delivery, which was crucial for achieving tangible business benefits.
Looking ahead, we anticipate that the following generative AI trends will significantly contribute to enterprises unlocking value from data:
1. AI for Creativity
Marking a milestone in AI art generation, Dall-e, an AI tool, demonstrated the unexpected ability to create artwork from minimal inputs. Despite the initial limitations of its early version in terms of quality, the current iteration has shown significant improvement, delivering results that closely align with user requests.
The capabilities of such AI tools are not limited to visual art; they also include generating real-time animations, music, and audio for a wide range of applications. This field is set to experience ongoing expansion, empowering creative professionals and enthusiasts alike, such as musicians, songwriters, artists, and sound effects specialists, to fully utilize generative AI technologies for artistic expression and innovation.
Coca-Cola and Dall-E have partnered to launch “Create Real Magic,” a platform that uses AI technology to improve marketing campaigns. This partnership is an intriguing illustration of novel advertising tactics meant to capture customers’ attention while utilizing the most recent developments in generative AI to enhance consumer interactions with engaging content.
2. GenAI for Hyper-Personalization
In several industries, hyper-personalization has emerged as a crucial aspect of Generative AI. In the Pharmaceutical & Life Sciences industry, where drug launch campaigns are paramount, hyper-personalization is essential for success.
Commercial pharma teams engage with healthcare professionals (HCPs) on a personal level to promote new drugs. This requires extensive research on the HCP’s domain and mapping the drug with their specialization.
Generative AI lets commercial pharma teams produce individualized content for each healthcare professional at scale. By analyzing vast amounts of data, AI can tailor messages and materials to individual preferences and needs. This enables more targeted and effective communication strategies, leading to improved engagement and outcomes of GenAI in the healthcare industry.
Beyond the pharmaceutical industry, hyper-personalization extends to various sectors such as e-commerce and entertainment. In these domains, AI algorithms analyze vast amounts of data to predict and adapt to user preferences, enhancing the user experience and driving customer satisfaction.
3. Conversational AI
Conversational AI is where generative models have landed most visibly in production.
Generative AI makes it possible to have natural language interactions with AI. Using sophisticated natural language processing and machine learning methods, generative AI models like GPT can comprehend context, produce coherent and pertinent responses, and tailor discussions based on a user’s history and preferences.
GenAI enhances Conversational AI, making it more intuitive, interactive, and capable of flawlessly handling intricate interactions.
4. How Is Generative AI Being Used in Scientific Research?
Generative artificial intelligence (Gen AI) is revolutionizing the way research papers are summarized, particularly in the medical and pharmaceutical fields. It offers a more efficient and accurate approach to extracting key information from complex documents.
This technology leverages the power of large language models (LLMs) to condense lengthy documents into concise, comprehensible summaries. Researchers, practitioners, and industry professionals can quickly grasp key findings, methodologies, and implications without delving into the full text.
Gen AI-driven summarization tools streamline the literature review process by significantly reducing the time and effort required to extract vital data. This enhances research productivity, facilitates more informed decision-making, and accelerates the development of new treatments and drugs. Ultimately, it contributes to advancements in healthcare and improved patient outcomes.
5. Human in the Generative AI Loop
In 2026, Human-in-the-Loop (HITL) emerged as a noteworthy trend in Generative AI, emphasizing the symbiotic relationship between AI progress and human supervision. As Generative AI systems gained complexity, integrating human input into the training process became crucial to ensure alignment with ethical standards, cultural sensitivities, and practical applications.
This approach not only enhances the accuracy and reliability of AI-generated outputs but also fosters a collaborative environment where human expertise guides the evolution of AI.
Organizations that leverage HITL can harness the creativity and efficiency of generative AI while maintaining control over the output, ensuring that it meets the diverse and nuanced demands of various applications.
6. Multimodal Generative AI
Generative AI is shifting from single-domain proficiency to multimodal models that handle text, image, audio and video in one system.
Pioneering models like CLIP for text-to-image and Wave2Vec for speech-to-text have paved the way. However, recent advancements target more versatile models that can seamlessly transition between tasks like natural language processing (NLP) and computer vision, even incorporating video processing capabilities as seen in Lumiere by Google.
This new wave of AI encompasses proprietary models like OpenAI’s GPT-4V and open-source options like LLaVa. These models aim to create more intuitive and adaptable applications, allowing users to interact with AI in intricate ways, such as receiving visual aids alongside verbal instructions.
Moreover, by handling a broader spectrum of data inputs, multimodal models can enhance their comprehension, generating more accurate outputs. This significantly expands the utility of AI across various fields.
7. Opensource Wave in Generative AI
Generative AI (GenAI) offers a myriad of prospects, from crafting intricate art to composing music, designing pharmaceuticals, and replicating human speech. It has become a focal point for both excitement and critical analysis.
Open-source projects play a vital role in GenAI’s progression. They democratize access, invite contributions from diverse backgrounds, drive innovation, and help identify and address biases during development.
This collaborative approach fosters an inclusive environment for innovation, encourages knowledge and resource sharing, and facilitates the prompt identification and correction of biases and errors.
Moreover, open-source initiatives in GenAI are essential for ensuring transparency, building trust, and ensuring ethical considerations are at the forefront of AI development.
As a result, open source is not merely a trend but a fundamental component in the sustainable growth of generative AI. Examples of GenAI in Open Sources include TensorFlow and TensorFlow Models, PyTorch and Hugging Face’s Transformers, GPT-Neo and GPT-J, Stable Diffusion, and more.
8. What Regulations Now Apply to Generative AI?
The trend towards regulatory compliance in Generative AI is gaining momentum, particularly in response to the proposed Artificial Intelligence Act by the EU. This is driven by growing concerns over privacy and bias as multimodal AI becomes more accessible.
The absence of clear regulatory frameworks could hinder the adoption of AI technology. Businesses may hesitate to invest due to fears that future regulations could render their current investments obsolete or illegal.
In the United States, the leading hub for AI innovation, regulatory efforts are still evolving. While government bodies and developers have taken steps to establish standards and pledge ethical practices, a comprehensive regulatory framework remains elusive.
GenAI, a prominent application in the pharmaceutical industry, is addressing regulatory compliance challenges by producing compliant-ready materials for various purposes, including drug launches, promotions, and HCP outreach. By automating document creation according to stringent industry standards, GenAI facilitates rapid and error-free preparation for market entry and ongoing compliance. This enhances efficiency, reduces the risk of regulatory violations, and supports regulatory affairs by streamlining the document creation process.
9. What Is Bring Your Own AI (BYOAI)
Bring Your Own AI (BYOAI) is the integration of custom or preferred artificial intelligence (AI) models into existing platforms, systems, or services by individuals or organizations. This approach offers greater customization, efficiency, and alignment with specific needs or goals, although real-world examples of BYOAI are limited. In healthcare, providers are implementing AI algorithms they have developed or tailored to analyze patient data, predict disease outcomes, and customize treatment plans, demonstrating the potential benefits of BYOAI in healthcare. Even while they aren’t dubbed BYOAI, banks like JPMorgan Chase have invested in creating their own artificial intelligence (AI) systems, termed Index GPT, to improve risk management and customer service.
10. AI-Augmented Apps and Services
In 2026, Generative AI trends are led by AI-augmented applications and services, signifying a notable shift in how technology empowers individuals across diverse domains. This trend entails incorporating advanced AI algorithms into various software and platforms, enhancing user experiences with tailored, intelligent capabilities.
AI-augmented solutions are redefining efficiency and personalization, encompassing content creation tools adapting to individual writing styles and smart healthcare apps delivering customized treatment recommendations.
Conclusion
As we explore the top Generative AI trends for 2026, it’s evident that the future holds remarkable advancements in Generative Artificial Intelligence. However, these innovations come with challenges, such as ethical concerns and the need for robust regulatory frameworks. Addressing these issues is crucial for the responsible development and deployment of Generative AI solutions.
To navigate the complexities of Generative AI trends, businesses must collaborate with experienced Generative AI development companies. SoluLab offers AI services and solutions, backed by a team of expert Generative AI developers. By partnering with SoluLab, you can leverage the full potential of GenAI trends in 2026, ensuring your business stays ahead.
FAQs
AI will probably change schooling at all levels, training, and instructional materials will be provided to the students based on their individualized needs. AI will also identify the best techniques and methods to improve every field.
Future Generative AI trends will enable businesses to innovate rapidly, streamline operations, and create personalized customer experiences, ultimately driving growth and competitive advantage.
Challenges include ethical considerations, data privacy concerns, and the need for robust regulatory frameworks to ensure the responsible use of Generative AI technologies.
A Generative AI development company can provide tailored AI solutions, leveraging the latest GenAI trends in 2026 to help your business innovate, automate processes, and enhance decision-making capabilities.
Generative AI developers design, build, and optimize AI models and applications, ensuring they meet specific business needs and harness the full potential of Generative artificial intelligence.
Industries such as healthcare, finance, entertainment, and manufacturing will benefit significantly from Generative AI trends, experiencing improvements in efficiency, innovation, and customer engagement.
In 2026, people are searching across Google, ChatGPT, and other AI-powered tools, not just scrolling through traditional search results. Your content needs to be discoverable in all these places.
In this guide, you'll learn what SEO writing is, why it matters, and 12 tips for creating content that gets found everywhere it counts.
What is SEO writing?
SEO writing is the process of writing content to earn visibility in search engines like Google and AI platforms like ChatGPT, Perplexity, and Gemini.
At its core, SEO writing combines two things: genuinely helpful content and smart optimization. If you focus only on optimization, people won't engage with your content. If you write amazing content but ignore optimization, nobody will find it in the first place.
Key optimization practices include:
Finding and targeting the right keywords your audience searches for
Matching search intent so your content format aligns with what users want
Structuring content with clear headings so search engines and AI platforms can surface your content properly
Using internal and external links to show relationships between different pages and establish credibility
Why is SEO writing important?
SEO writing is important because it helps you increase the reach and visibility of your content.
People are likely searching for topics you have expertise in on Google and AI chatbots. When you write with SEO in mind, your content has a higher chance of being discovered through these platforms, driving consistent organic (free) traffic month after month.
Unlike paid advertising that stops working the moment you stop paying, well-optimized content keeps delivering results. It's an investment that appreciates over time.
Let's look at a concrete example.
It also shows up in answers to 157 prompts on AI platforms, according to our AI Visibility Toolkit.
A lot of that success comes from SEO writing best practices we'll cover below.
Before you start writing
Before you start, take some time to do research. The research and planning you do upfront will shape how well your content performs in search engines and AI platforms. Skipping these steps often leads to content that struggles to rank or get cited in AI platforms.
1. Find your primary keyword
Choosing the right primary keyword is important because it tells search engines and AI systems what your content is about and when to show it.
Each piece of content you write should be optimized for one primary keyword. This is the main term (or phrase) you want your content to rank for in search engines and be associated with in AI answers.
To find your primary keyword, use Semrush’s Keyword Magic Tool.
Type your topic into the search bar and enter your domain name in the “AI-powered feature” space. Then, select your target location and click “Search.”
The tool will show you potential candidates for your primary keyword.
Having access to all these keywords is great. But how do you know which one to choose as your primary keyword?
Here are some tips to consider.
Your primary keyword should:
Be relevant to your content. It should reflect the main topic of your content and be relevant to your industry.
Have a decent search volume. Enough people should be searching for that keyword. What constitutes a decent search volume depends on the niche of your website. Look at the “Volume” column to see how many people search for each keyword.
Be within your reach. It shouldn't be too difficult for you to compete for. Look to the Personal Keyword Difficulty (PKD %) column to see how challenging this keyword would be for your specific website. The lower the percentage, the easier it'll be to achieve visibility.
For example, you might choose “dog behavior training” as your primary keyword if you’re writing content about this topic.
This keyword has a search volume of 2,900 searches per month and a Personal Keyword Difficulty score of 46%, meaning it's competitive but within reach.
So, it’s definitely a good primary keyword to target.
(Targeting a keyword means using it in your content. We’ll see how to do that correctly later in this guide.)
2. Choose your secondary keywords
Secondary keywords help you gain visibility for multiple related terms and usually have less competition than primary keywords.
What counts as a secondary keyword:
Synonyms of your primary keyword
Related subtopics
Long-tail variations (highly specific terms of your primary keyword)
Find secondary keywords using Semrush’s Keyword Magic Tool.
First, see whether your primary keyword has a default grouping on the left-hand side.
If it does, click on it. And you’ll see all the related keywords belonging to that group.
These keywords are close variations of your primary keyword. And they can make for great secondary keywords to target in your content.
Another effective method is to use the "Questions" tab on the left-hand side. This will show you all the questions that people are asking related to your topic.
Some of these questions (secondary keywords) might be worth addressing as subtopics in your content.
You can also discover secondary keywords by analyzing competitors in Organic Research.
Enter a specific page's URL and click "Search." (Make sure that “Exact URL” is selected from the drop-down in the next screen.)
In the "Overview" report, look at "Top Keywords." These are terms the competitor's content appears for.
Click "View all keywords" to see the complete list and identify secondary keywords worth targeting in your own content.
3. Analyze search intent
Analyze search intent to figure out what kind of content users want when they search your keyword.
For blog posts, your primary and secondary keywords will likely have informational intent. This means users want to learn something about a topic.
And the best way to teach them about a topic is to use the right content format.
Some common content formats include:
How-to guides
List posts
Step-by-step tutorials
Definition posts
Comparison posts
You can find out which content format works best for your topic by looking at the search results for your query.
For example, we see that the top results for "dog training for beginners" are how-to guides that share dog training techniques and tips.
Similarly, when you ask ChatGPT about this topic, it provides structured how-to guidance with step-by-step training methods and beginner-friendly tips.
So, if you were to target this keyword (and related secondary keywords), you need to structure your content to loosely match this format. Doing so will improve your chances of ranking well and being cited by AI platforms.
The writing stage
Now, it’s time to start creating your content.
The following tips will help you create content that’s both helpful for readers and optimized for visibility in search engines and AI platforms.
4. Make an outline
Create an outline before you start writing because it helps you organize your ideas, cover important subtopics, and structure your content in a way that's easy for both readers and machines to understand.
Without an outline, it’s easy to miss important points, repeat yourself, or create a confusing flow.
A strong outline also helps ensure your content aligns with search intent. It forces you to think about how to integrate the questions readers want answered and the order in which to address them.
For SEO blog writing, your outline should include:
Your main title (H1)
Primary sections (H2s)
Supporting subsections (H3s and H4s where necessary)
Key talking points for each section
As an example, if you're writing about "dog training for beginners," your outline might look like this:
5. Create quality content
Create quality content because that’s what search engines and AI platforms want to surface in search results.
But what is quality content? It's content that:
Is accurate
Provides value to your audience
Is original and unique
Is up-to-date
Don't just summarize what other articles say on the topic. Bring something new to the table that proves you know it.
Draw on firsthand experience: If you've done the thing you're writing about, share specifics: what worked, what didn't, what surprised you.
Cite credible sources: Link to original research or studies to support ideas that could be challenged.
Share original data or examples: Run a small experiment to test ideas you're writing about, and then include the findings in your article.
Quote subject-matter experts: If a topic falls outside your expertise, interview someone who has expertise or first-hand experience with the topic. Even a few quotes can lift your content’s quality in a meaningful way.
By prioritizing quality this way, you build trust with your audience, establish yourself as an authority in your niche, and increase your chances of being cited by AI platforms as a reliable source.
6. Leverage keywords in your content
Work your researched keywords into your content naturally. Search engines and AI platforms read those words to figure out what your page covers and when to surface it.
But avoid keyword stuffing at all costs.
Keyword stuffing is the practice of repeating keywords excessively throughout the content in an unnatural way. It’s a tactic some use to try to manipulate their way to better visibility.
See how the example below is doing it wrong:
Keyword stuffing makes your content look spammy to both search engines and readers. Worse, it can trigger penalties that tank your rankings and make AI platforms less likely to cite your work.
To check whether you’re using keywords correctly, try Semrush’s SEO Writing Assistant.
It highlights when any of your keywords are used in an unnatural way.
Import your page content into the tool and start making changes directly in the content editor.
7. Structure content with subheadings
Well-structured content with clear subheadings helps Google, ChatGPT, and other platforms understand what each section covers, making your content easier to rank and cite.
Subheadings (H2-H6) are miniature titles that divide your content into sections. They're important for SEO blog writing because they:
Make your content easier to read and understand. Readers can quickly scan and find the information they need.
Help you use keywords naturally. Subheadings provide natural opportunities to incorporate keywords and related terms without forcing them.
See how the example on the right is much easier to scan for information?
You need to structure your content similarly.
First, write a catchy title that includes your primary keyword. This will be your H1 (the first heading in your content).
Then, organize the rest of your content with relevant H2 subheadings, H3 subheadings, and so on.
(H2 subheadings should support your H1, H3 subheadings should support your H2s, etc.)
Like this:
These subheadings are a great place to include your secondary keywords naturally.
8. Make your content easy to read
Readable content keeps users engaged longer, signals quality to search engines, and makes it easier for AI platforms to extract and cite information accurately.
If your content is difficult to read, users will bounce off your page (and likely never come back). This behavior signals to search engines that your content is low quality, hurting your rankings. And poorly structured content is harder for AI platforms to understand and cite properly.
Using subheadings is a good start. But there are other things you need to do to improve readability:
Use short sentences and paragraphs. This will make your content more scannable and digestible.
Use simple and clear language. So readers of all levels can understand your writing.
Use visuals. Images, infographics, videos, graphs, or other visual assets are powerful tools that can enhance your content and make it more engaging.
Use bullet points and numbered lists. They’re great for presenting information in a clear and concise way. They help both readers and AI platforms identify key points quickly.
Run your content through Semrush’s SEO Writing Assistant to measure its readability.
The tool tells you how easy it is to parse:
It also highlights words or sentences that could be rewritten to enhance readability.
9. Add internal and external links
Internal and external links help search engines and AI platforms understand your content’s relationship with other pages and establish trust.
Internal links point to other pages on your own website. They direct readers to other valuable, relevant content.
External links (also called outbound links) are links that point to pages outside your website, typically when citing sources or providing additional resources.
When linking, make your anchor text (the clickable link text) descriptive and relevant to the destination page.
Only link where it naturally makes sense. Forced or excessive linking hurts user experience.
For instance, in an article about dog training for beginners, you can add internal links to other pieces of content that provide more information on topics such as:
How to stop unwanted behaviors
Indoor vs outdoor training methods
How to socialize your puppy properly
Also, it’s a good idea to provide an external link any time you’re referencing:
Statistics
Reports
Surveys
Case studies
Interviews
Adding external links to authoritative websites for sourcing purposes shows that your content was created with accuracy and credibility in mind.
But statistics and research do change over time. So, make sure you link out to the most up-to-date information available.
10. Create an optimized title tag and meta description
Title tag and meta description control how your content appears in search results and often determine whether someone clicks or scrolls past your page.
Here’s what they look like in traditional organic search results:
Search engines use these elements to understand your content's topic. Users read them to decide whether to click. And AI platforms often reference them when determining what your content covers.
So, it's worth optimizing both elements carefully.
Follow these tips when crafting your title tag:
Make sure your title tag entices users to click. You can use power words, numbers, and brackets for that.
Include your primary keyword in your title tag (preferably at the beginning). This will help you rank for that keyword and show relevance to the user’s query.
Keep your title tag around 55 characters long. So Google doesn’t truncate it in search results.
And these guidelines apply to your meta description:
Use active voice. This will improve clarity by addressing users directly.
Use action verbs. This will nudge users to click through and explore your article. So, use phrases like “learn more,” “find out,” or “dive deep.”
Keep it brief. Google cuts off meta description after about 105 characters on mobile. So, keep it to one to two short sentences to stay below that threshold.
Use your target keyword. This will signal to users that your page covers the topic they want to learn about.
Here’s an example of a good title and meta description that follows these principles:
Title tag: Dog Training 101 [Complete Beginner's Guide for 2026]
Meta description: Discover effective dog training methods. Learn commands, schedules, and techniques from pro trainers.
11. Optimize your URL slug
Optimize your URL slug because search engines look at it to understand your content’s topic and AI platforms use it when evaluating and citing sources.
A URL slug is the last part of your page’s URL.
Here’s an example:
Follow these best practices when choosing your slug:
Include your target keyword. This can help search engines and AI platforms understand what your article is about and surface it for relevant queries.
Use hyphens to separate words. Hyphens are the standard way to separate words in a URL slug.
Avoid using dates. Dates in your URL slug can make your article look outdated.
Be concise and descriptive. Long or confusing slugs are harder for readers to remember and for AI platforms to parse.
By following these tips, you can create an optimized URL slug that helps your content perform better in search results and makes it easier for AI platforms to identify and cite your work.
12. Get feedback on your content
Have someone else review your content before you publish it. It's easy to overlook mistakes and weak spots when you're deep in your own draft.
At Semrush, every article we publish goes through at least one round of editorial review. You'll see this in the byline of every post, where an editor is listed as a contributor.
If you have an editor on your team, ask them to read through the content and point out:
Misalignment with the search intent
Structural mistakes
Sections where the flow feels off
Parts that need clarification
Sentences that could be tightened or simplified
Claims that need a source or example to back them up
Factual errors or outdated information
Once you've received the feedback, make the necessary changes. Your content will be in much better shape and ready to publish.
SEO writing is just the first step
The 12 tips above will help you create content that's optimized for both search engines and AI platforms. But SEO writing is just the first step of a much bigger process.
Once you've got the writing side down, the next steps are:
Promoting your content so it earns backlinks
Tracking its performance in both traditional search and AI platforms
Updating it regularly to keep it accurate and relevant
We've linked to resources to guide you on those next steps.
The right tools make all of this easier. An SEO and online visibility platform like Semrush helps you find keywords, analyze competitors, optimize your writing, and track AI visibility from one place.
