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CX Manager

Build CX dashboards and reporting

Enhances✓ Available Now

What You Do Today

Create and maintain dashboards that make CX data accessible to stakeholders — NPS trends, journey metrics, verbatim highlights, and closed-loop performance.

AI That Applies

Automated CX dashboards — AI generates real-time dashboards with anomaly detection and automated narrative insights.

Technologies

How It Works

The system aggregates data from multiple operational systems into a unified analytical layer. 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 output — real-time dashboards with anomaly detection and automated narrative insights — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Dashboards update in real-time with AI-generated commentary: 'NPS dropped 3 points this week driven by claims experience in the Southeast region.'

What Stays

Designing dashboards that tell a story, not just display numbers, and ensuring stakeholders actually use the data.

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 build cx dashboards and reporting, understand your current state.

Map your current process: Document how build cx dashboards and reporting works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Designing dashboards that tell a story, not just display numbers, and ensuring stakeholders actually use the data. 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 Tableau 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 build cx dashboards and reporting 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 VP Operations or COO

Which of our current reports are manually assembled, and how much time does that take each cycle?

They're prioritizing which operational processes to automate

your process improvement or lean lead

What questions do stakeholders actually ask that our current reporting doesn't answer?

They understand the workflow dependencies that AI tools need to respect

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.