Chapter 03
Mapping
Reading time · 25 min
This is the main insights of this chapter whatever it might be
91%
Of something that you shuld know
7
of something tha tyou should know
78%
of something else to show and talk about
Some details to go with it
And even more ones
We surveyed Journey Management practitioners across industries, company sizes, and stages of practice. The charts below are drawn from those responses and stay editable in the CMS.
Figures show how JM is adopted by industry stage, how team size splits worldwide, how long people have been practicing, and which problems they solve beyond CX.
By industry stage:
Source: State of Journey Management survey
By company size:
Source: State of Journey Management survey
By years practicing:
Source: State of Journey Management survey
By problem solved:
Source: State of Journey Management survey. Hover a bar to inspect each use.
Interviews
80+
conversations with Journey Management practitioners
Between February and April 2025 we spoke with 60+ practitioners across continents and stages of practice — from emerging teams to mature programs, a range of team sizes, and different levels of AI adoption. Participation was voluntary via community. Quotes are anonymous, attributed by role level and company stage.
External sources
80+
external sources, reports, and published research
The report is grounded in 60+ external sources: industry research, published reports, and public writing on Journey Management, CX, and adjacent practice. These sit alongside the survey and interviews so the findings can be read in a wider context.
The 2026 stack
2. The designer’s AI toolstack has more than doubled
Designers we surveyed now use an average of 7 off-the-shelf AI tools, up from 3 last year. 76% have used an AI coding tool like Claude Code, Cursor, or Copilot.
Claude has overtaken ChatGPT as the primary general AI tool
Source: AI in Design survey, Q1 2026
The 2026 AI toolstack · AI in Design survey, Q1 2026
The 2026 AI toolstack · AI in Design survey, Q1 2026
Figma has shifted from being the primary design tool to a canvas for quick exploration and polishing details as input for Claude Code.
Phil Vander Broek · Head of Design, AI, Superhuman
What makes tools stick
3. Output quality is both the biggest driver of stickiness — and the biggest weakness
80% of designers say reliable, high-quality output is what makes an AI tool stick. At the same time, 62% cite inconsistent or unreliable output as their biggest challenge.
What makes an AI tool stick · AI in Design survey, Q1 2026
Top challenges of using AI in design work · AI in Design survey, Q1 2026
We need a tool that allows us to seamlessly go from research to discovery to concepting and sketching to prototyping to high fidelity to code, and not necessarily in a linear fashion.
Individual contributor · Growth-stage company
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