Strategy and growth. Chief Experience Officer
AI for the CX Office
Customers don’t want a faster answer. They want it fixed.
Most service AI answers questions and then hands the customer to a queue. The callback, the complaint and the cost all come after. We map the CX Office the way your service actually runs, from the first contact to the back office step, then put agents where they resolve, not just respond.
The short answer
It resolves customer requests end to end across voice, chat, email and messaging, including the back office step that usually causes a callback. It reads every interaction for complaints and root causes, and it serves customers in every language around the clock.
Your people take the cases that need empathy and judgment. The agents take the volume, so customers wait less and every contact costs less.
The mandate
Make it easier to be a customer. And cheaper to serve one.
What the board measures you on
NPS and CSAT
By journey, channel and segment
First contact resolution
Fixed the first time, or not
Cost per contact
The full cost of each resolution
Customer effort
How hard it was to get help
Complaints
Volume, age and root cause
Churn
Customers lost after a bad experience
The canvas of the CX Office
Most CX AI starts and stops at the chatbot. The real cost sits in the back office step, the repeat contact and the complaint that reaches a regulator.
Canvas 1
What you run
Every place a customer meets the company.
Contact centre
Voice, chat, email and messaging, in house or outsourced.
Digital self service
Apps, portals, help centres and assistants.
Journey design
How customers move from first contact to loyal customer.
Voice of customer
Surveys, reviews, complaints and interaction analytics.
Knowledge and back office links
The answers your teams rely on and the systems that act on them.
Canvas 2
What you answer for
The things regulators, the board and customers hold you to.
Service standards and complaints
Complaint handling, often reported to a regulator in financial services.
CX vendors
Outsourced contact centres, platforms and their quality.
AI disclosure
Telling customers when they talk to AI and when a person steps in.
Customer data and privacy
What agents can see and do in a customer’s account.
Where value leaks
Every one of them shows up twice. Once in your cost per contact, and again in your churn.
01
Repeat contacts
Customers call back because the first contact didn’t fix it.
Service agents. Resolve the request end to end, including the back office action, and confirm it with the customer.
First contact resolution
Repeat contact rate
02
Expensive simple queries
Skilled people spend their day on status checks and routine changes.
Autonomous service. Handles routine intents across voice, chat and email and hands over with full context when a person is needed.
Cost per contact
Containment
03
Complaints found late
The pattern hides in thousands of interactions until it hits the press or the regulator.
Voice of customer agents. Read every call, chat and email and surface themes and root causes every week.
Complaint volume
Churn
04
Language and hours gaps
Service quality drops outside your core languages and office hours.
Multilingual agents. Serve every channel in every language your customers use, around the clock.
Resolution by language
After hours resolution
05
Quality checked on samples
Outsourced quality gets scored on a few calls, often by the vendor itself.
Quality agents. Score every interaction against your standards and flag coaching needs and contract breaches.
Quality coverage
SLA credits recovered
06
Promises that don’t survive the sale
What sales sold isn’t what service knows.
Onboarding agents. Turn deal terms into an onboarding plan and service context before the first contact.
Time to first value
Early churn
Process lifecycles
We start from the platforms you already run. The orange steps are where an agent takes on the work, so your people spend their time on the customers who need them most.
Contact to resolution
Every contact, every channel
Contact
Identify
Customer and account
Agent
Understand
Intent, in any language
Agent
Resolve
Including the back office step
Agent
Escalate
Follow up
Confirmed and closed
Agent
Typical systems. Genesys, NICE, Five9, Salesforce Service Cloud, Zendesk
Complaint lifecycle
Every complaint
Log
Spotted in any channel
Agent
Investigate
Respond
Drafted within the rules
Agent
Root cause
Grouped across cases
Agent
Report
Typical systems. CRM, complaint tools, regulator reporting
Onboarding
Every new customer
Sale
Handover
Deal terms into service context
Agent
Setup
First use
Guided by usage signals
Agent
Settled
Typical systems. CRM, onboarding tools, product analytics
Voice of customer loop
Weekly
Listen
Every interaction read
Agent
Find themes
And root causes
Agent
Fix
Tell product
Into the backlog
Agent
Measure
Typical systems. Medallia, Qualtrics, interaction analytics
Agent intervention point
The CX control tower
Not a monthly survey score. A live view of what customers ask, what got fixed, what it cost and what’s about to turn into a complaint.
Illustrative
CX control tower
Sample data to show the view. Not client results.
NPS
41
▼ 4 pts
First contact fix
74%
▲ 6 pts
Cost per contact
€4.80
▼ 18%
Containment
46%
▲ 9 pts
Repeat contacts
17%
▼ 5 pts
Aged complaints
37
Over 8 weeks old
This week’s customer brief
Theme
Delivery date questions up 32% after the carrier change
Found in 4,100 chats and calls. Root cause sent to the COO Office
Quality
Outsourced site B below standard on identity checks
Every call scored, not a sample. SLA credit review suggested
Fixed
Address changes now resolved end to end in the app
Repeat contacts on this intent down from 31% to 6%
Handover
Twelve enterprise customers onboarding this week
Deal terms and promises shared with their service team
Resolved without a person, by language
English
58%
Dutch
51%
German
47%
Arabic
33%
Arabic lags on billing intents. Knowledge gaps listed for the content team.
Illustrative example. Figures are invented to show how the view works. In discovery we build it on your own data and KPIs.
Your industry changes the agenda
The canvas stays the same. What customers call about does not. Here’s the question we hear most from CX leaders in each industry, and where agents help first.
CPG
How do we serve consumers directly when retailers own the relationship?
Product questions and complaint themes
FMCG
How do we spot a quality issue before it becomes a recall story?
Complaint signals by batch and market
Retail
How do we handle peak season without hiring an army?
Order status, returns and refunds end to end
D2C
How do we keep service personal as order volume grows?
Order changes and subscription management
Automotive
How do we connect the brand, the dealer and the owner in one service?
Service booking, warranty and recall contact
Healthcare
How do we answer patients fast without adding clinical workload?
Appointments, referrals and admin questions
Energy
How do we cut call volume when every bill raises a question?
Billing explanations, meter reads and moves
Manufacturing
How do we support dealers and installers across many markets?
Technical support from manuals and service history
Logistics
How do we stop “where is my parcel” from flooding the centre?
Tracking, rerouting and claims
BFSI
How do we resolve more without breaching complaint and conduct rules?
Account servicing, disputes and claims
B2B technology
How do we scale support without the ticket queue growing with revenue?
Ticket resolution from docs and product data
Proof
Customer interactions a year across voice, chat and email
One of the largest banks in the US. CX Office
Of those interactions resolved the same day
One of the largest banks in the US. CX Office
Less time to process insurance claims by reading documents alongside ERP data
Automotive insurer, GCC region
How the CX Office connects
The CX Office catches every promise sales made and every problem operations caused. It also hears what customers want next. The agents that serve it share the same platform and data as the offices around it, so that insight travels back instead of dying in a ticket.
How we’d start
Pilot 1
Top intents, end to end
↳
Three to five request types that drive the most volume
↳
Resolved end to end on one channel, including the back office step
↳
Measured on resolution, cost and satisfaction against your baseline
Your decision
Which intents and channels to add next.
Pilot 2
Every interaction, read
↳
Three months of calls, chats and emails read by voice of customer agents
↳
Complaint themes, root causes and repeat contact drivers
↳
Quality scored on every contact, in house and outsourced
Your decision
Which root causes to fix first, and whether agents keep reading every week.
Both run under our Validation First Framework. They’re tested on your own data before you commit a larger budget, and if the numbers don’t hold, we tell you.
Your intervention map
Bring the reasons customers contact you most, and the one complaint you’d most like to stop. We bring the CX Office canvas. In one session we map it to your channels and systems, and mark where agents would pay back first.
01
Your CX Office canvas
What you run and answer for, checked against how your teams, partners and platforms really work.
02
Your value chain
The canvas placed on your industry and your customer journey, with the back office systems behind every fix.
03
Your intervention points
Each leak with an agent design, its controls and the KPI it moves. Delivered as a mind map, an architecture view or an interactive dashboard.
AI for the CX Office, answered
What can AI do for the CX Office?
Agentic AI resolves customer requests end to end across voice, chat, email and messaging, including the back office step that usually causes a callback. It reads every interaction for complaints and root causes, scores every contact for quality and serves customers in every language around the clock. Customers wait less, and each contact costs less.
How is an AI agent different from a chatbot?
A chatbot answers questions. An AI agent finishes the job. It understands the request, checks the customer’s account, takes the action in your systems, such as a refund, a booking change or a claim update, and confirms it. When the case needs a person, it hands over with the full context so the customer never repeats themselves.
How much can AI reduce cost per contact?
It depends on how many of your contacts are routine and how many need a back office action. We measure it on your own contact data in discovery. The savings come from resolving routine intents without a person and from cutting repeat contacts, which are often a large share of volume.
Will customers know they are talking to AI?
They should, and under the EU AI Act they must be told. We design every agent to say clearly that it is AI, to offer a person at any point and to hand over smoothly when the customer asks or the case needs it.
Do we need to replace our contact centre platform?
No. Agents work with Genesys, NICE, Five9, Salesforce, Zendesk and the other platforms you already run, and with the back office systems behind them. You keep your stack and your outsourcing partners.
Where should a CX leader start with agentic AI?
With your top routine intents and your repeat contacts. Pick three to five request types that drive volume, let agents resolve them end to end on one channel and measure resolution, cost and satisfaction against your current baseline before you scale.
