Marketing technology (martech) has entered a new era defined by AI agents, composable architectures, and the rise of buyer-side AI assistants that are fundamentally changing how customers discover brands and B2B vendors.

The martech landscape now includes 15,384 solutions, representing 100X growth since 2011, according to Scott Brinker's 2025 Marketing Technology Landscape. Yet the real transformation isn't in tool proliferation. It's in how AI is embedding itself into every layer of marketing operations, from content creation to customer engagement to the discovery process itself.

What is martech?

Martech refers to the software and platforms marketers use to plan, execute, manage, and measure customer interactions across channels. The category spans customer relationship management (CRM), marketing automation, customer data platforms (CDPs), analytics, content management, and increasingly, AI-powered tools for personalization and workflow automation.

What distinguishes martech from advertising technology (ad tech) is its focus on owned customer relationships rather than paid media buying. Martech supports the full customer lifecycle, from lead capture through retention. The two categories increasingly overlap as platforms consolidate, though 12% of ad budgets worldwide have been lost due to poor integrations between martech and ad tech systems, per Forrester Consulting and Investis Digital data cited by EMARKETER.

What does a modern martech stack include?

A martech stack is the collection of integrated tools an organization uses to execute marketing operations. While stacks vary by company size and business model, most include several foundational categories:

• CRM platforms. Salesforce, HubSpot, and Microsoft Dynamics manage customer data, track interactions, and support sales alignment.
• Marketing automation. Platforms like Marketo, HubSpot, and Pardot enable email campaigns, lead nurturing, and workflow automation.
• Customer data platforms (CDPs). Tools that unify customer data from disparate sources into actionable profiles. CDPs are increasingly moving toward composable architectures that work directly with cloud data warehouses.
• Content management systems (CMS). WordPress, Adobe Experience Manager, and Contentful power web content creation and delivery.
• Analytics and attribution. Google Analytics, Adobe Analytics, and specialized tools measure campaign performance and customer behavior.
• Digital asset management (DAM). Platforms such as Bynder, Brandfolder, Adobe Experience Manager Assets, and others that provide a centralized place to store, organize and access creative assets, including images, videos and documents.
• AI and automation tools. Generative AI assistants, agentic workflows, and AI-powered personalization engines are now core stack components.

How is AI changing marketing technology?

AI is reshaping martech across three dimensions: operational efficiency, creative production, and customer discovery. Currently, 90.3% of marketing organizations use AI agents somewhere in their martech stack, according to Scott Brinker's Martech for 2026 research.

Content production agents and audience discovery agents are the top internal-facing AI types, with 68.9% of organizations using the former, and 40.8% the latter, according to Brinker’s research. AI handles tasks from email personalization to predictive analytics to content optimization, reducing manual effort while increasing targeting precision.

The more disruptive shift involves how buyers find brands. AI-powered search through ChatGPT, Perplexity, and Google's Gemini is displacing traditional search, with McKinsey estimating that 20% to 50% of traffic from traditional search channels is at risk. This indicates that marketers must now optimize for AI discovery, not just search engine rankings.

What is agentic AI and how is it used in martech?

Agentic AI refers to autonomous systems that don't just assist but act independently, making decisions and executing tasks with minimal human intervention. 2025 marked an inflection point for agentic AI in marketing, according to EMARKETER, as platforms moved from text generators to decision-making collaborators.

Major platforms now offer agentic capabilities:

• OpenAI's ChatGPT agent handles research, forms, and document workflows in a sandbox environment.
• Salesforce Agentforce 360 integrates agentic AI into marketing operations, automating customer experience workflows across Slack and campaign systems.
• Amazon Ads Creative Studio generates complete ad campaigns in hours using AI agents.
• Adobe Experience Cloud deploys enterprise-grade AI agents for audience building, content scaling, and experimentation.
• Jasper's marketing suite functions as a self-learning ecosystem managing SEO, personalization, and research.

What are buyer-side AI agents and why do they matter for marketers?

Buyer-side agents are AI assistants that consumers control to research, evaluate, and engage with brands. ChatGPT, Claude, Perplexity, and Google Gemini are the primary examples. Scott Brinker argues these represent "the real disruption" to marketing, far more than vendor-side AI implementations.

The disruption is structural. Buyer-side agents bypass traditional search, social, and website discovery channels. They synthesize information from across the web and deliver answers directly, often without users clicking through to brand properties. This threatens the foundational assumptions of SEO, content marketing, and demand generation strategies built over the past two decades.

The challenge for marketers is that 63% recognize this shift in buyer search behavior, but only 14% have actually adapted their content strategies accordingly. Brands that want to remain visible must learn to market to AI agents as intelligent intermediaries, structuring content for extraction and citation rather than just clicks.

What is a composable martech stack?

A composable martech stack uses modular, loosely-coupled architecture where individual components can be swapped, upgraded, or extended without disrupting the entire system. This approach favors open APIs, interoperability, and flexibility over monolithic one-stop shops.

Key characteristics include:

• Data warehouse centralization. Cloud data warehouses (Snowflake, Databricks, BigQuery) increasingly serve as the foundation, with composable CDPs working directly on warehouse data rather than duplicating it.
• API-first integration. Components connect through standardized APIs rather than custom integrations.
• Best-of-breed selection. Organizations choose specialized tools for specific functions rather than relying on a single vendor's suite.

The shift is measurable: CDPs dropped from 26.9% to 17.4% as the center of B2C martech stacks, with capabilities migrating to either the warehouse layer or engagement platforms.

Who are the major martech platform providers?

Several companies hold outsized influence in the martech landscape due to adoption scale and AI investment:

• Salesforce. Marketing Cloud and Agentforce 360 combine CRM, automation, and agentic AI capabilities. The platform integrates across sales, service, and marketing functions.
• Adobe. Adobe Experience Cloud, Experience Manager, and Adobe Analytics provide enterprise content creation, data analysis, and customer experience tools. Adobe Firefly offers generative AI with IP indemnity for enterprise clients.
• HubSpot. Originally focused on SMBs, HubSpot has expanded into enterprise markets with an integrated CRM, marketing automation, and content management platform.
• Oracle. Oracle Marketing and Eloqua serve marketers with email, lead scoring, and automation tools integrated with Oracle's broader data and cloud offerings.

The competitive dynamic is shifting as AI capabilities become table stakes. Many providers now rely on the same OpenAI or Anthropic models, making their agentic offerings "almost indistinguishable," per EMARKETER analysis. Differentiation increasingly depends on integration depth, data access, and workflow-specific customization.

What challenges do marketers face with martech complexity?

Martech complexity has reached a breaking point. Marketers across categories are calling for simpler systems after years of fragmentation and technical overload, according to EMARKETER interviews with leaders from Criteo, LiveRamp, Reddit, Vistar Media, StackAdapt, and DoorDash.

The core challenges:

• Fragmentation and integration failures. Managing campaigns across dozens of platforms creates operational overhead and data silos.
• AI readiness gaps. 62% of B2B tech marketing leaders lack the skills, budget, or strategy to compete with AI-native firms, per 3Thinkrs research cited by EMARKETER.
• Discovery visibility. AI systems deprioritize content older than 30 to 60 days, requiring constant content refresh to maintain visibility in AI-powered search.
• Measurement inconsistency. KPIs vary across platforms, making cross-channel attribution difficult.

The industry is responding with calls for standardization, unified workflows, and platforms that reduce toggling between interfaces. Buyers want fewer steps, clearer decision-making tools, and transparent insight into where their marketing runs.

How should marketers evaluate and manage their martech stack in 2026?

Effective martech management in 2026 requires balancing AI adoption with operational simplicity. Five principles guide the approach:

1. Audit for AI readiness. Assess which stack components support AI integration and which create bottlenecks. Prioritize platforms with native AI capabilities or open APIs for AI tool integration.
2. Consider composability. Evaluate modular tools that integrate through standard APIs instead of monolithic suites. This allows flexibility as AI capabilities evolve.
3. Invest in data infrastructure. Clean, unified data feeds AI performance. Consider cloud data warehouses as your foundational layer with composable CDPs managing context.
4. Simplify where possible. Eliminate redundant tools. Favor platforms that unify workflows over point solutions that add complexity.
5. Build cross-functional governance. AI agents require oversight spanning marketing ops, data engineering, and compliance. Establish human checkpoints for consequential decisions.

We prepared this article with the assistance of generative AI tools and stand behind its accuracy, quality, and originality.

EMARKETER forecast data was current at publication and may have changed. EMARKETER clients have access to up-to-date forecast data. To explore EMARKETER solutions, click here.