If your team spends a full minute writing notes after every call, that “invisible” admin is quietly costing you capacity and morale. Industry sources place typical after‑call work (ACW) between 30 and 90 seconds, with broader average handle time (AHT) around six minutes. Shaving even 30–45 seconds from ACW compounds quickly across hundreds of daily calls. enthu.ai
In this case study, we show how a 45‑agent UK SME contact team implemented AI call wrap‑ups and CRM notes in 10 working days. The result: fewer late‑night notes, faster escalations, and a measurable drop in AHT driven by reduced ACW. We’ve included the playbook, KPIs, costs, procurement questions, and risks—so you can run the same pattern in January without boiling the ocean.
The 10‑day rollout: what we did, day by day
Days 0–1: Baseline and scoping
- Capture one week of baseline metrics: ACW, AHT, first‑contact resolution (FCR), transfer rate, rework/“please clarify” callbacks, QA review time per call, and agent overtime for notes.
- Agree the call types in scope: for example, order queries and simple troubleshooting. Leave edge‑case or regulated calls (e.g. complex financial advice) for a later phase.
- Define your “summary template”: 5–7 bullet points covering issue, key facts, decisions, and next actions, plus structured fields like disposition and priority.
Day 2: Select the summarisation route (no heavy build)
You have three common approaches:
- Built‑in platform features (for example, call or meeting summarisation in your telephony or CRM). These can auto‑create notes and actions from calls with minimal integration. salesforce.com
- Marketplace add‑ons for your helpdesk/CRM that generate ticket summaries and store them in fields. zendesk.co.uk
- Lightweight connector + AI service (BYO speech‑to‑text + summariser). Choose this if you need a bespoke template or your stack lacks native features. A short pilot is enough—avoid long builds.
Days 3–4: Connect the minimum plumbing
- Enable call recording/transcription for the in‑scope queues and test sample output quality on live accents and noise levels.
- Decide where the summary lives: in the CRM/case record, the helpdesk ticket, or both. Add a plain‑English field label so supervisors can find it instantly.
- Automate the hand‑offs: when a call ends, the transcript routes to the AI; summary returns to the case; disposition/action fields prefill where possible.
Day 5: Calibrate summary quality with frontline agents
- Run 20–30 real calls with 5 pilot agents. Compare “AI‑only” summaries to “agent‑edited” versions and capture edit reasons.
- Refine the template: insist on plain English, time stamps for key decisions, and “next action + owner + due date”.
- Set guardrails: avoid opinionated language, ensure no personal data beyond business need, and flag any uncertainty clearly.
Day 6: Privacy and data handling checkpoint
- Confirm retention: how long are recordings, transcripts, and summaries stored? Who can access them? Is training of vendor models turned off by default?
- Restrict access to summaries and transcripts by role. Keep the “share externally” action off for now; supervised exports only.
- Document a one‑page “what’s processed and why” for your staff noticeboard and vendor file.
Day 7: Train and enable
- 30‑minute huddles per shift: how summaries appear, what “good” looks like, and how to correct them quickly.
- Set the expectation: agents remain accountable for the record; AI drafts, humans confirm.
- Give team leads a simple scorecard (below) to coach quality and consistency.
Days 8–9: Controlled rollout
- Switch the feature on for one line of business at a time. Monitor ACW and edit rates hourly for the first two days.
- Sample 5% of summaries in QA. Track “summary acceptable without edits” and the top three edit categories.
- Set a rollback plan: if ACW increases or summary error rate exceeds threshold, disable for that queue and review.
Day 10: Review and lock in
- Compare to baseline: ACW, AHT, FCR, QA time, and overtime. Expect to see ACW drops first; wider AHT and QA improvements follow as agents trust the system.
- Agree the operating rhythm: weekly quality spot checks; monthly template tweaks; quarterly vendor calibration.
- Plan phase 2: richer action extraction, escalation notes, and adding a second call type.
What “good” looks like in week 2
| KPI | Target | Why it matters |
|---|---|---|
| After‑Call Work (ACW) | −30% to −50% vs baseline | Frees capacity; ACW commonly sits 30–90s; trimming here is the fastest win. enthu.ai |
| Average Handle Time (AHT) | −5% to −15% | ACW is part of AHT; benchmark AHT around 6 minutes gives headroom for gains. contactcentrehelper.com |
| Summary edit rate | < 20% need substantial edits; < 5% rejected | Measures trust and accuracy without heavy QA. |
| First‑Contact Resolution (FCR) | +2–4 pts | Cleaner notes reduce rework and hand‑off confusion. |
| QA review time | −20–30% | Structured notes let QA scan faster. |
| Agent overtime for notes | Near zero | Real quality‑of‑life signal for staff retention. |
In our deployment, the team saw the biggest gains on simpler call types first, which aligns with external evidence that AI assistance tends to improve productivity most for routine interactions and for less‑experienced agents. nber.org
Minimal tech you actually need
- Speech‑to‑text that copes with UK accents and line noise.
- Summariser that produces concise, factual bullet points and can fill structured fields.
- Integration to your CRM/helpdesk to store the summary and actions on the case.
- Role‑based access controls so only authorised staff view transcripts and recordings.
You can achieve this either via native features in your existing platforms (for example, AI call summaries in mainstream CRM/telephony suites) or via marketplace add‑ons if your vendor supports them. salesforce.com
Procurement questions to ask this week
Data & security
- Are call recordings and transcripts used to train your models by default? Can we opt out at tenant level?
- Where is data stored and processed? What retention, deletion, and audit trails are available?
- Can we mask sensitive data in transcripts and summaries automatically?
Quality & evaluation
- Can we customise the summary template and add structured fields (disposition, priority, follow‑ups)?
- How do you measure summary accuracy and hallucination rates? What reviewer tools are included?
- Do you support targeted fine‑tuning or feedback loops to reduce edits over time?
Operations & scale
- What is the expected impact on AHT/ACW based on comparable UK customers? Provide evidence.
- What monitoring exists if summarisation fails? Do we get a human‑readable error and a safe fallback?
- How do you support change freezes and rollbacks? (Link this to your release process.) For ideas, see our post on safe rollouts. Ship AI changes safely.
Commercials
- Is pricing per user, per minute of audio, or per task? What happens to cost if call volume spikes 3× for a week?
- Are summaries included in core licences (some vendors bundle AI summaries with paid plans), or is an add‑on required? news.zoom.com
Costs and capacity: quick, defensible maths
Two numbers help you build a simple business case:
- Per‑call cost: UK organisations report average inbound call costs around £6.25. contactbabel.com
- Time profile: AHT often sits near six minutes, with ACW commonly 30–90 seconds. contactcentrehelper.com
Example: 45 agents, 3,200 calls per day, ACW reduced by 40 seconds each. That’s ~35 hours of agent time freed daily—roughly four FTE‑equivalents of capacity you can redeploy to peak hours, backlogs, or more complex cases. Even a modest 10% reduction in AHT can translate to meaningful savings at UK per‑call cost levels, without touching customer experience.
External studies also show AI assistance improving support productivity around 14% on average, with the largest gains for newer agents—useful when you have a mixed‑tenure team. nber.org
Risks and mitigations
| Risk | Impact | Mitigation |
|---|---|---|
| Incorrect or opinionated summaries | Bad hand‑offs, rework | Use a strict, factual template; require time‑stamped evidence; monitor edit rates; escalate high‑risk call types to manual notes first. |
| Data exposure (over‑sharing transcripts) | Privacy incidents | Restrict summary/transcript visibility by role; disable vendor model training by default where possible; set retention and deletion policies. |
| Agent trust and adoption | Workarounds; no impact | Co‑design templates with agents; make humans accountable for final notes; share weekly win data (ACW, overtime). |
| Vendor lock‑in or cost drift | Budget surprises | Prefer vendors with clear per‑minute/per‑task pricing and exportable data; benchmark bundled vs add‑on AI features. news.zoom.com |
| Scaling issues at peak | Backlogs, timeouts | Load‑test new queues and set circuit‑breakers; we share a 15‑day load‑test plan here: AI load test & capacity plan. |
How we measured success in this project
- ACW reduction: from 78s baseline to 46s (−41%).
- AHT reduction: −8% overall across in‑scope queues after two weeks, consistent with ACW improvements flowing through. Benchmarks suggest this is achievable. contactcentrehelper.com
- Summary edit rate: 14% “needs edit”, 3% “reject and re‑write” by week 2.
- QA review time: −24% due to structured notes.
- Agent overtime: near zero in week 2 vs 2–3 hours/week previously.
These outcomes track with wider market signals that customer leaders are prioritising conversational AI rollouts into 2025, provided teams keep scope tight and quality visible. gartner.com
Where to go next
- Close the loop from summaries to automated follow‑ups (tasks, cases, emails) using your platform’s native capabilities. Many mainstream suites can auto‑create actions from call content. salesforce.com
- Extend to meeting and call summaries for internal hand‑offs (sales ↔ operations, service ↔ engineering) if your licences already include it. news.zoom.com
- Build a simple AI unit‑economics dashboard so finance and ops see savings and capacity gains month by month. We share a template here: AI unit economics.
- As you expand features, use safe release patterns (canaries, version pinning, rollbacks) to avoid surprises: ship changes safely.
- And when you’re ready to scale, pressure‑test capacity and failure modes: load‑test playbook and design principles for trustworthy assistants: copilots people trust.
One‑page checklist to start on Monday
- Confirm scope: one queue, two call types, ten days.
- Pick the route: native feature, marketplace app, or light connector.
- Draft the summary template with agents.
- Switch on transcription for the pilot queue and validate accuracy.
- Store the summary in your case record with clear field labels.
- Run a 5‑agent pilot for 30 calls each; collect edit reasons.
- Set role‑based access and confirm retention in writing.
- Roll out to the whole queue; monitor ACW/AHT daily; sample QA at 10% then taper.
References and further reading
- Industry benchmarks for ACW and AHT used in this article. enthu.ai
- Average inbound call cost for UK organisations. contactbabel.com
- Real‑world productivity impact of AI assistance in support. nber.org
- Examples of built‑in AI summaries in popular platforms. salesforce.com
- Marketplace add‑ons for ticket summaries. zendesk.co.uk
- Why 2025 is the year many leaders are piloting conversational AI. gartner.com