The CX AI Maturity Index: How enterprises are scaling AI in customer experience.
Learn about the Adobe Customer Experience AI Maturity Index and take the assessment to find out where your organization stands.
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Summary
Enterprises are investing heavily in AI, but most haven't built the infrastructure to run it at scale. The Adobe Customer Experience AI Maturity Index benchmarks where your organization stands across two dimensions, customer experience orchestration (CXO) execution and enterprise-grade control, based on 501 enterprise assessments and 6,885 responses.
AI investment is outpacing CX maturity.
Enterprises are moving from AI experimentation to execution, but customer experience outcomes aren’t keeping pace. Only 12% of organizations have fully orchestrated AI-driven customer journeys, and just 9% report real-time AI personalization in production, even as AI investment grows. The gap isn't ambition or capability. It's operationalization: AI is deployed in isolated use cases instead of scaling as one connected customer experience system across content, data, journeys, and governance.
For many enterprises, execution and control aren’t working together.
Two dimensions determine your AI CX readiness.
AI maturity is the ability to scale customer experiences with control. The CX AI Maturity Index measures that ability across two dimensions:
CXO execution. The horizontal axis measures how effectively you run customer experiences across content, journeys, and brand visibility — at speed, at scale, and repeatedly.
Enterprise-grade control. The vertical axis measures how confidently you execute with the controls required to scale safely, including privacy, governance, brand integrity, and decision accountability.
More than two-thirds of organizations (68.3%) landed in “disconnected,” making it the most common state in the diagnostic.
Assess your readiness in 12 questions.
Answer 12 questions about how you deliver customer experiences today and get a clear read on where your business stands in 3–4 minutes. The diagnostic evaluates both CXO execution and enterprise-grade control, identifies the specific constraints limiting your ability to scale, and translates the results into personalized next steps you can act on immediately.
What 500-plus enterprise leaders told us.
You’re not the only one working through AI implementation challenges. The Adobe CX AI Maturity Index draws on 501 completed enterprise assessments and 6,885 responses across 12 questions. Here’s where organizations land:
Clearly, “disconnected” is the default, not the outlier. Disconnected respondents were also more likely to request a detailed follow-up (73.3%), suggesting the lowest-maturity organizations are the ones most actively seeking a path forward.
Key findings in the data.
Operational AI, not yet systemic.
While 58% of organizations report mid-stage AI maturity, only 11–15% operate AI-native execution models. Most enterprises have folded AI into existing workflows, but few have redesigned the operating system around it.
CX orchestration lags AI investment.
Just 12% report fully orchestrated, AI-driven customer journeys, and only 9% report real-time AI personalization in production. Enterprises are scaling AI capability faster than they’re scaling customer experience maturity.
Governance frameworks, not governance systems.
The majority of respondents (59–67%) say governance policies and workflows exist but are only partially operationalized. Awareness of what good governance looks like has outpaced the ability to execute it.
Human dependency remains structurally embedded.
Only 11% report fully automated governance and approval systems. Most of the work still gets done by hand — someone reviewing, someone approving, case by case.
Demand for clear maturity models.
Advanced maturity states were selected infrequently in the diagnostic, yet users consistently navigated toward them to understand what they looked like. What's missing is a clear model for how mature AI execution actually operates at scale.
CXO execution
1. Testing and optimizing CX: Occasionally (35%)
2. Content production capacity: Growing, with some efficiency gains (41.7%)
3. Speed to market: 1–3 weeks (47.4%)
4. Journey orchestration: Coordinated across some channels (40.7%)
5. Real-time personalization: Limited, periodic updates only (39.8%)
6. Brand presence in AI discovery: Limited, fragmented insight (40.3%)
Enterprise-grade control
7. Governance and approvals: Partially streamlined (37.2%)
8. Data policies for AI: Defined but inconsistently applied (34.1%)
9. Roles and responsibilities: Ad hoc and informal (31%)
10. Traceability of decisions: Partially documented (47.7%)
11. Brand integrity at scale: Moderate, inconsistently applied (41.7%)
12. Ownership across workflows: Partially defined, varies by team (41.3%)
Know your state. Know your next step.
- Disconnected
- Unbalanced
- Constrained
- Orchestrated
Disconnected (low execution, low control)
Diagnosis: You're producing content and launching experiences, but they aren't connected to a repeatable system. AI exists, but only in isolated pockets.
What's breaking: No shared governance model across teams. Risk and compliance are reactive, not integrated into decision-making. Ownership across content, data, and journeys is unclear.
Why it matters: Without a repeatable system and shared ownership across teams, AI insights never translate into scalable, standardized experiences. Every use case starts from zero.
Next step: Get marketing, tech, and risk teams into a room to agree on who owns what before adding more AI use cases on top of a system nobody’s steering.
Unbalanced (high execution, low control)
Diagnosis: You've built real momentum across content, journeys, and personalization, but the system isn't governed well enough to scale consistently.
What's breaking: Governance isn't embedded into workflows, so there's no consistent way to validate what goes live.
Why it matters: Growing fast without shared standards increases brand and compliance risk as you scale.
Next step: Build governance directly into content and journey workflows. Treat risk as a design partner, not an approval layer at the end.
Constrained (low execution, high control)
Diagnosis: You have strong governance, policy, and control and may already have AI processes. But your operating model won't let those processes scale.
What's breaking: AI capabilities exist but can't translate into scalable, operational workflows.
Why it matters: You can control your experiences. You just can't produce and orchestrate enough of them to compete.
Next step: Replace manual approvals with policy-driven automation, so teams can move faster inside the guardrails you've already built.
Orchestrated (high execution, high control)
Diagnosis: You're orchestrating customer experiences as one connected, adaptive system, where every experience builds on the last.
What's breaking: Fewer than one in six organizations reach this state. The challenge isn't building the capability but sustaining it as complexity grows.
Why it matters: Orchestration compounds as an advantage only as long as ownership and accountability keep pace with scale.
Next step: Continuously optimize across content, journeys, and channels, and expand into conversational and hybrid human-AI workflows without losing alignment across marketing, technology, and risk.
Frequently asked questions about AI maturity.
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