Customer Service.
14 real jobs AI can take on for a customer service team - most of them inside the licence you already pay for. The colour on each card is the kind of AI; the line at its foot is what it takes to get it.
THE COLOUR IS THE KIND OF AI — AUTONOMY RISES LEFT TO RIGHT
The line at the foot of each card is what it takes to get it - a free chatbot, the licence you already pay for, or a build.
If these words are new, the homepage walks the whole spectrum →
Problem resolution.
High volumes of unresolved issues and reopened cases can result in decreased customer satisfaction and loyalty, affecting future revenue.
Cross-team diagnosis and a drafted, agreed reply reach the customer faster, turning a complaint into a resolved relationship.
Try it in your own AI
- Context: A customer complaint at [company] spans [teams, e.g. billing and delivery] - the history is below and the customer has already repeated themselves twice.
Objective: Reconstruct what happened across the threads, identify where it broke, and draft the reply that owns it, fixes it and gives them one named point of contact. [Paste the threads and notes.]
Style: Internal timeline first, then the customer reply under 200 words.
Tone: The reply human and unhedged - apologise once, properly, then be practical.
✓ In the licence you already pay for
Agents receive personalised cross-sell and upsell suggestions drawn from each customer's patterns, lifting revenue per interaction.
✓ Your licence can run this once it's set up - we do the setting up →
Agents get real-time, knowledge-based resolution steps tuned to the customer's mood and history, so issues close quicker.
Uses your suite's meeting AI - how this lands varies by licence.
✓ Your licence can run this once it's set up - we do the setting up →
Connected case data and team collaboration produce a fast, accurate complaint response inside the service workflow.
✓ Your licence can run this once it's set up - we do the setting up →
Case assignment.
Customer issues must be sorted and prioritized manually, which is time-consuming and can lead to slower issue resolution and a less personalized service experience.
Managers recap activity, spot performance trends and brief leadership, turning daily data into improved procedures the team adopts.
Try it in your own AI
- Context: I lead a frontline service team of [n] at [company]. Review time - and the activity data below holds real patterns I don't want to replace with anecdotes.
Objective: Find the trends: who's improving, who's overloaded, where handle times or reopens spike and what correlates. Then draft the leadership recap and the one procedure change the data supports. [Paste the activity data.]
Style: Trends with evidence, recap under a page, the procedure change as a plain recommendation.
✓ In the licence you already pay for
Support content is auto-classified, tagged and version-tracked, so agents find the right article first time.
✓ Your licence can run this once it's set up - we do the setting up →
Documents are monitored for breaches, with alerts and safer alternatives suggested before a compliance problem occurs.
✓ Your licence can run this once it's set up - we do the setting up →
At-risk interactions are flagged with remediation steps before satisfaction drops, protecting retention and reputation.
✓ Your licence can run this once it's set up - we do the setting up →
Field engineers get diagnosis, safety checks, parts lists and upsell prompts on the job, resolving faults right first time.
✓ Your licence can run this once it's set up - we do the setting up →
Work orders, technician matching and customer updates are automated end to end, cutting scheduling delays and admin.
This one needs building for your business - that's us →
Day in the life.
See how people can use your AI assistant to perform common tasks throughout their day to save time, generate value, and improve their wellbeing.
A walkthrough of how a frontline agent resolves customer issues faster across a typical shift.
Try it in your own AI
- Context: I'm a frontline service agent at [company]. Between tickets I lose time re-reading long histories and writing the same explanations from scratch.
Objective: For the ticket below: summarise the history in three lines, tell me what's been tried, draft my reply in our support voice, and give me the one-line case note for the log. [Paste the ticket thread.]
Style: Four labelled parts, the reply under 150 words.
Tone: The reply warm and concrete - next step, timeframe, no scripts.
✓ In the licence you already pay for
A walkthrough of how a support leader uses AI through the day to sharpen service operations.
Try it in your own AI
- Context: I run support at [company] - [n] agents across [channels]. I get numbers all day but the shape of the week only reaches me as anecdotes.
Objective: From the ticket and satisfaction data below, give me the week's story: what drove volume, where we were slow, which category is growing, and the one thing I should raise with [product/ops] because support can't fix it. [Paste the data.]
Style: One page - four headed paragraphs, numbers inline.
✓ In the licence you already pay for
Issue diagnosis.
Service agents may lack access to documentation and subject matter experts, which can lead to inconsistent problem-solving and delay resolution.
Calls are transcribed, summarised and analysed for sentiment and trends, building a case history that speeds future fixes.
✓ Your licence can run this once it's set up - we do the setting up →
Patterns across tickets, chats and emails surface recurring issues, so teams fix the cause once and share the answer.
✓ Your licence can run this once it's set up - we do the setting up →
Most of these run on the licence you already pay for.
If a few of these sound familiar, bring them to a quick call. Some are a prompt, some are an afternoon of setting up and a few are proper builds - and it's worth knowing which before you spend anything. No licence yet? Pick one first.
← All twelve teams