URIBurner Knowledge Graph

State of Martech 2026

The report frames marketing's AI shift as metamorphosis, highlights MCP as an integration layer, and analyzes martech market churn and AI adoption patterns across six categories.

May 5, 2026
Authors: Scott Brinker, Frans Riemersma
Sponsors: GrowthLoop, Hightouch, Knak, MoEngage, Pega, Progress, SAS
15,505Martech Products
208Survey Respondents
10Organizations
20Key Terms Defined
Knowledge Graph

Organizations Mentioned

Sponsors and companies interviewed in the report

Dattura Studio GrowthLoop Hightouch Knak Marketing Technology Media, LLC MartechTribe B.V. MoEngage Pega Progress SAS
Key Concepts

Defined Terms

Essential terminology for understanding the AI-driven martech transformation

AEO Agentic workflow Context-as-a-Service Composable CDP Context (colloquial) Context engineering Context graph Context stack Context window Decisioning GEO Golden Context Governance gap MCP Model Context Protocol Orchestration RAG Trust ladder Value engineering Vibe coding
Expert Voices

Company Interviews

GrowthLoop

Focus: causal AI, context graph, decisioning

Hightouch

Focus: agentic workflow, composable CDP, MCP

Knak

Focus: creation gap, marketing ops, MCP

MoEngage

Focus: decisioning

Pega

Focus: AI governance, decisioning

Progress

Focus: AI integration

SAS

Focus: AI analytics, enterprise use cases

Hightouch

Focus: agentic workflow, composable CDP, MCP

Knak

Focus: creation gap, marketing ops, MCP

MoEngage

Focus: decisioning

Pega

Focus: AI governance, decisioning

Progress

Focus: AI integration

SAS

Focus: AI analytics, enterprise use cases

Practical Guides

How-To Instructions

HowTo #1: Measure Market Motion
1Record total products, products added, and products removed for the period; compare net change to churn volume (pages 18-19).
2Calculate (added + removed) / total to quantify market motion even when net growth is near zero (pages 18-19).
3Break removals down by founding cohort, revenue band, and headcount to see which vendor segments are being culled (pages 20-21).
4Use churn patterns to infer category renewal, stability, or decay rather than relying on the headline net number (pages 23-24).
HowTo #2: Manage AI Governance
1List where AI is already producing customer-facing or brand-facing outputs (e.g., copy production, chatbots) to identify exposure (pages 39, 45-50).
2For each use case, note whether authenticity checks, logging, human review, and escalation paths exist; highlight gaps like low adoption of detection and lineage (pages 39-40, 53).
3Address data lineage, privacy/consent, and compliance because failures propagate into every downstream agent and workflow (pages 40, 53).
4Establish cross-functional decision rights so tactical agent deployments align to strategy and do not leak sensitive information (pages 39, 104).
5Ensure decisions can be inspected and outcomes measured, especially for decisioning and recommendation agents (pages 100-101).
HowTo #3: Implement Context Engineering
1Use value engineering to identify which customers, journeys, and moments matter most before connecting everything (pages 64-65).
2Choose a curated set of signals and constraints; context engineering is curation, not cramming (pages 66-67).
3Structure data, codify policies, and package tools/permissions so agents can interpret context at runtime (pages 67-73).
4Ensure context arrives at the moment of decision; connect sources and destinations before signals decay (pages 70-71).
5Record actions and outcomes to improve decisioning and support explainability; treat context as a living asset (pages 70, 120-121).
FAQ

Frequently Asked Questions

Common questions derived from the report's key claims, charts, and interviews

What does the chrysalis cover image represent?

The chrysalis symbolizes a structural transformation where old forms dissolve and new ones assemble, rather than a simple incremental upgrade (pages 1 and 6).

How many martech products are in the 2026 landscape?

The landscape contains 15,505 products in 2026, up 121 from 15,384 in 2025, which is 0.79% growth (pages 17-18).

What is MCP and why does it matter for martech?

MCP (Model Context Protocol) is an open standard connecting AI agents to tools and data sources; thousands of MCP servers enable fluid connectivity and shift constraints from integration to orchestration (pages 10-13).

What is 'context engineering' and why is it central?

Context engineering prepares curated, usable context (data, policies, tools, permissions, instructions) so agents can make good decisions at runtime; it becomes the bottleneck as building apps/agents gets cheaper (pages 63, 67-73).

What is the governance gap highlighted by the survey?

High AI production adoption outpaces governance: for example, AI copy production is 91% adopted while content authenticity/detection is 37%, and data governance/lineage/privacy lag operational AI use (pages 39 and 53).

What is 'Golden Context'?

Golden Context is the dynamic overlap of company context, customer context, and systems context at the moment of decision, where value and revenue are generated (pages 66-67).

About

About This Page

This knowledge graph overview was generated by querying the URIBurner SPARQL endpoint for the named graph https://linkeddata.uriburner.com/DAV/docs-for-knowledge-graph-and-embeddings-generation/UB-PDFs/state-of-martech-2026.pdf. The original PDF document was transformed into RDF using data-twingler and kg-generator, then uploaded to the Virtuoso-based URIBurner server. The SPARQL query retrieved entity types, organizations, defined terms, interviews, how-to instructions, and FAQs from the knowledge graph. The HTML infographic was then rendered using AI Agent Skills (data-twingler, kg-generator, rdf-infographic-skill) powered by minimax_m2.5free and running on Virtuoso.

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