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Chief Digital Officer

Customer Experience Design

Enhances✓ Available Now

What You Do Today

You lead the design of end-to-end digital customer experiences — journey mapping, UX strategy, accessibility, and ensuring every digital interaction reflects the brand promise.

AI That Applies

AI that analyzes customer journey data to identify drop-off points, friction moments, and experience gaps across all digital touchpoints.

Technologies

How It Works

The system ingests customer journey data to identify drop-off points as its primary data source. The analytics engine aggregates data across sources, applies statistical analysis to identify significant patterns and outliers, and presents the results through visualizations that highlight what needs attention. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The design vision.

What Changes

Experience design becomes more data-driven. AI shows you exactly where customers struggle, abandon, or express frustration — replacing guesswork with behavioral evidence.

What Stays

The design vision. Creating experiences that feel intuitive, human, and brand-appropriate requires creative judgment and empathy that data informs but cannot replace.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for customer experience design, understand your current state.

Map your current process: Document how customer experience design works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: The design vision. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Journey Analytics tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long customer experience design takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your board chair or lead independent director

What are the top 5 reasons customers contact us, and which of those could be resolved without a human?

They shape expectations for how AI appears in governance

your CTO or CIO

How do we currently measure service quality, and would AI-assisted responses change that measurement?

They own the technology infrastructure that enables AI adoption

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.