{"id":53320,"date":"2026-02-05T12:08:15","date_gmt":"2026-02-05T11:08:15","guid":{"rendered":"https:\/\/www.elaine.io\/?p=53320"},"modified":"2026-02-05T12:13:39","modified_gmt":"2026-02-05T11:13:39","slug":"martech-trends-what-marketers-need-to-know-now","status":"publish","type":"post","link":"https:\/\/www.elaine.io\/en\/blog\/martech-trends-what-marketers-need-to-know-now\/","title":{"rendered":"Martech trends for 2026: What marketers need to know now"},"content":{"rendered":"<div class=\"wpb-content-wrapper\" id=\"wpb-content-root\"><p>[vc_row][vc_column width=&#8221;1\/6&#8243;][\/vc_column][vc_column width=&#8221;2\/3&#8243;][vc_column_text css=&#8221;&#8221;]<\/p>\n<div><strong>2026 will be the year in which marketing technology becomes not only more efficient, but also more strategic. Artificial intelligence (AI) \u2013 especially generative AI and AI agents \u2013 is finding its way into operational processes, customers expect relevant communication across all channels, and at the same time, the requirements for data protection, data quality and reliable measurement are increasing.\u00a0<\/strong><strong>By 2025 at the latest, it became clear that many companies were using AI, but only a few were really integrating it into their everyday work with fixed processes, clear rules and measurable results. This is particularly evident in marketing: 85% of marketers are already using AI, but only 15% have fully integrated it into their regular workflows. (SAS, 2025). The picture is similar at the company level: 62% say their organisation is at least experimenting with AI agents (including companies that are already scaling) \u2013 yet only 39% report measurable business benefits from AI. (McKinsey, 2025).\u00a0<\/strong><strong>Many companies therefore feel that it is no longer enough to optimise individual tools. The decisive factor is whether the martech landscape interacts in such a way that customer experiences become personalised, consistent and measurable \u2013 from initial contact to loyalty. This article highlights the top 5 martech trends for 2026: technology alone is not enough. AI, data and customer engagement must work as a system.<\/strong><\/div>\n<div><\/div>\n<h2><\/h2>\n<p>&nbsp;<\/p>\n<h2><strong>1. AI agents are becoming productive \u2013 but only with \u2018human in the loop\u2019<\/strong><\/h2>\n<p>2026 is the year when AI agents will transition from pilot projects to everyday use. This does not just mean \u2018AI writes texts\u2019, but rather an agent that takes on tasks along a process chain, for example:<\/p>\n<p><strong>Find target group \u2192 Vary content \u2192 Run campaign \u2192 Test \u2192 Improve<\/strong><\/p>\n<p>For decision-makers, the question is not so much \u2018What can AI do?\u2019 but rather: How can it remain controllable, brand-compliant, data protection-compliant \u2013 and how can its benefits be measured? This is precisely why human-in-the-loop (human approval at the right points) is becoming the standard. HBR clearly shows that trust is a limiting factor here, based on current trust data. (HBR, 2025)<\/p>\n<p>&nbsp;<\/p>\n<p><strong>What this means in concrete terms:<\/strong><\/p>\n<ul>\n<li>Approvals: What can go live automatically?<\/li>\n<li>Quality assurance: Tone, brand fit, facts, errors<\/li>\n<li>Data protection &amp; compliance: Consent, target group logic, channel rules<\/li>\n<li>Emergency rules: Stop\/failover when data or results are uncertain<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><strong>What will become more important in 2026:<\/strong><\/p>\n<ul>\n<li>AI agents for clearly defined process steps (instead of \u2018AI everywhere\u2019)<\/li>\n<li>Human-in-the-loop as standard: approval, quality, compliance<\/li>\n<li>Guardrails for brand, data protection, frequency, risk<\/li>\n<li>Measurement of efficiency, impact and risks<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><strong>2. The right message at the right moment \u2013 instead of more content!<\/strong><\/h2>\n<p>The next level of personalisation is not \u2018more campaigns\u2019 but more context. In other words, status, behaviour, timing, channel rules and preferences must come together to make communication truly relevant.<\/p>\n<p>Scott Brinker calls this \u2018context engineering\u2019. In practice, this means above all: better decisions instead of more content. Who decides when which customer receives which message \u2013 and clearly explains why?<\/p>\n<p>&nbsp;<\/p>\n<p><strong>What will become more important in 2026:<\/strong><\/p>\n<ul>\n<li>From campaign thinking to situation thinking: What does the customer need right now \u2013 at this moment and on this channel?<\/li>\n<li>Bundle signals instead of evaluating them separately: Consolidate behaviour and status from email, apps, the web, CRM and service so that all channels work on the same basis.<\/li>\n<li>AI only optimises if the basis is right: AI can improve timing, content and frequency \u2013 but only if data, consent and definitions are consistent.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><strong>3. Breaking down data silos, improving data quality: the foundation for scalable AI<\/strong><\/h2>\n<p>Many companies invest in new tools \u2013 and wonder why personalisation, cross-channel orchestration and AI still don&#8217;t scale properly. The reason is often not a lack of technology, but data silos and inconsistent foundations: teams work with different data sets and different definitions \u2013 and in the end, there is no reliable overall logic.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Typical symptoms include:<\/strong><\/p>\n<ul>\n<li>Different status definitions (e.g. \u2018active\u2019, \u201cinactive\u201d, \u2018migrated\u2019)<\/li>\n<li>Fragmented consent and preference logic<\/li>\n<li>Unclear responsibility for data quality<\/li>\n<li>No uniform \u2018customer truth\u2019 for marketing, service and sales<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p data-start=\"4564\" data-end=\"4883\">The more AI intervenes in processes, the greater the impact: AI amplifies good data \u2013 and bad data. In addition, the governance risk is growing: 54% would use AI tools even if they were not authorised (\u2018shadow AI\u2019) \u2013 at the same time, only 36% feel sufficiently trained. (BCG, 2025). Gartner also shows that a lack of foundation is a real scaling killer: according to forecasts, over 40% of agentic AI projects will be discontinued by the end of 2027 \u2013 due to costs, unclear business value or insufficient risk controls, among other reasons. (Gartner, 2025)<\/p>\n<p data-start=\"5146\" data-end=\"5174\"><strong data-start=\"5146\" data-end=\"5174\">\u00a0<\/strong><\/p>\n<p><strong>What will become more important in 2026:<\/strong><\/p>\n<ul>\n<li>Standardising data: consents, preferences, status, interests, contact frequency<\/li>\n<li>Clarifying responsibility: professional and technical<\/li>\n<li>Defining measurement logic: success criteria and verification<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><strong>4. Composable Martech: Modular Tech Stack \u2013 integration becomes a success factor<\/strong><\/h2>\n<p>In 2026, companies will buy fewer \u2018features\u2019 and more connectivity. A modular tech stack is attractive because new requirements \u2013 especially AI components \u2013 can be integrated more quickly without having to replace everything.<\/p>\n<p>But modularity is not a sure-fire success. Those who think modularly need integration as a core competence:<\/p>\n<ul>\n<li>clear API standards<\/li>\n<li>clean data models<\/li>\n<li>governance via tools and data flows<\/li>\n<li>an operating model for orchestration<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>In the context of AI agents in particular, integration determines whether agents can truly act \u2013 or only \u2018analyse\u2019.<\/p>\n<p><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><strong>What will become more important in 2026:<\/strong><\/p>\n<ul>\n<li>Integration beats features: systems must be connectable<\/li>\n<li>APIs, data models and standards are a must<\/li>\n<li>Modularity requires governance (rules, responsibility, transparency)<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><strong>5. AI consumer agents are changing access to customers<\/strong><\/h2>\n<p>In 2026, AI consumer agents will increasingly take over the pre-selection of information, offers and news for end customers. Users will no longer see every marketing message directly, but only what their agent considers relevant, trustworthy or useful.<\/p>\n<p>For marketers, this is a clear shift: relevance is partly decided before the message is delivered. Therefore, content, offers and signals must be prepared in such a way that they are correctly understood and classified by agent-based filters.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>What will become more important in 2026:<\/strong><\/p>\n<ul>\n<li>structured, machine-readable content (clear offers, metadata, value propositions)<\/li>\n<li>First-party data, preferences and intent signals as a basis<\/li>\n<li>Close integration of CRM, content and messaging systems for consistent signals<\/li>\n<li>Systems that evaluate and prioritise relevance \u2013 not just send content<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><strong>Conclusion<\/strong><\/h2>\n<p>In 2026, the winners will be those who set up martech as a complete system: using AI sensibly, keeping data clean and truly bringing channels together. AI agents can speed up processes \u2013 but only with clear guidelines and responsibility. Context determines relevance. Data quality and integration determine whether the whole thing scales. And AI consumer agents are also shifting access to customers: structure, clarity and consistent signals are becoming more important than \u2018even more campaigns\u2019.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>What marketers should do now<\/strong><\/p>\n<ul>\n<li>Make AI agents productive \u2013 clear use cases, guidelines, approvals<\/li>\n<li>Prioritise data quality \u2013 break down silos, maintain consent and preferences consistently<\/li>\n<li>Think context instead of campaigns \u2013 orchestrate customer moments<\/li>\n<li>Make integration mandatory \u2013 APIs, standards, data models, governance<\/li>\n<li>Optimise for AI consumer agents \u2013 provide content, offers and signals in a structured way<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><strong>Sources:<br \/>\n<\/strong><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li data-start=\"7709\" data-end=\"7901\">\n<p data-start=\"7711\" data-end=\"7901\">SAS (2025): Marketers and AI: Navigating New Depths (Coleman Parkes Research)<br data-start=\"7794\" data-end=\"7797\" \/><a class=\"decorated-link\" href=\"https:\/\/www.sas.com\/content\/dam\/sasdam\/documents\/20250124\/marketers-and-ai-navigating-new-depths.pdf\" data-start=\"7799\" data-end=\"7899\">https:\/\/www.sas.com\/content\/dam\/sasdam\/documents\/20250124\/marketers-and-ai-navigating-new-depths.pdf<\/a><\/p>\n<\/li>\n<li data-start=\"7902\" data-end=\"8180\">\n<p data-start=\"7904\" data-end=\"8180\">McKinsey (2025): The state of AI in 2025: Agents, innovation, and transformation<br data-start=\"7994\" data-end=\"7997\" \/><a class=\"decorated-link\" href=\"https:\/\/www.mckinsey.com\/capabilities\/quantumblack\/our-insights\/the-state-of-ai\" data-start=\"7999\" data-end=\"8178\">https:\/\/www.mckinsey.com\/capabilities\/quantumblack\/our-insights\/the-state-of-ai<\/a><\/p>\n<\/li>\n<li data-start=\"8400\" data-end=\"8571\">\n<p data-start=\"8402\" data-end=\"8571\">BCG (2025): Companies Must Go Beyond AI Adoption to Realize Its Full Potential<br data-start=\"8494\" data-end=\"8497\" \/><a class=\"decorated-link\" href=\"https:\/\/www.bcg.com\/press\/26june2025-beyond-ai-adoption-full-potential\" target=\"_new\" rel=\"noopener\" data-start=\"8499\" data-end=\"8569\">https:\/\/www.bcg.com\/press\/26june2025-beyond-ai-adoption-full-potential<\/a><\/p>\n<\/li>\n<li data-start=\"8572\" data-end=\"8840\">\n<p data-start=\"8574\" data-end=\"8840\">Gartner (2025): Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027<br data-start=\"8684\" data-end=\"8687\" \/><a class=\"decorated-link\" href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027\" data-start=\"8689\" data-end=\"8838\">https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027<\/a><\/p>\n<\/li>\n<li data-start=\"8841\" data-end=\"9037\">\n<p data-start=\"8843\" data-end=\"9037\">Harvard Business Review (2025): Workers Don\u2019t Trust AI. Here\u2019s How Companies Can Change That<br data-start=\"8949\" data-end=\"8952\" \/><a href=\"https:\/\/hbr.org\/2025\/11\/workers-dont-trust-ai-heres-how-companies-can-change-that\">https:\/\/hbr.org\/2025\/11\/workers-dont-trust-ai-heres-how-companies-can-change-that<\/a><\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>[\/vc_column_text]<div class=\"ult-spacer spacer-6aa0e0edc080c\" data-id=\"6aa0e0edc080c\" data-height=\"50\" data-height-mobile=\"50\" data-height-tab=\"50\" data-height-tab-portrait=\"\" data-height-mobile-landscape=\"\" style=\"clear:both;display:block;\"><\/div>[\/vc_column][vc_column width=&#8221;1\/6&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1655972328700{padding-top: 55px !important;}&#8221;][vc_column]<div id=\"ultimate-heading-74676aa0e0edc0858\" class=\"uvc-heading ult-adjust-bottom-margin ultimate-heading-74676aa0e0edc0858 uvc-4612  uvc-heading-default-font-sizes\" data-hspacer=\"no_spacer\"  data-halign=\"center\" 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Artificial intelligence (AI) \u2013 especially generative AI and AI agents \u2013 is finding its way into operational processes, customers expect relevant communication across all channels, and at the same time, the requirements for&hellip;<\/p>\n","protected":false},"author":70,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_seopress_titles_title":"Martech trends for 2026: What marketers need to know now","_seopress_titles_desc":"In our blog, you can find out which five martech trends will be decisive in 2026 \u2013 from AI agents and context engineering to data quality, integration and loyalty KPIs. 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