Ask a contact centre leader about after-call work and they'll usually frame it as a time problem. Wrap time is too long. Agents aren't “going available” quickly enough. AHT is inflating. The fix, in most conversations, is operational: better templates, tighter ACW targets, more monitoring.

That framing is not wrong, but it is incomplete. After-call work is not just a time problem. It’s a quality problem, one which has a direct customer-facing cost that most operations are not measuring.

What actually happens when the call ends

‍

The call ends. The agent is under pressure to “go ready” and be available for the next call in the queue. They have notes to write, a CRM record to update, a disposition to log. Often with multiple systems to update. They have approximately two minutes to do all of that before the queue moves. So, they write what they can. A sentence, maybe two. A shorthand that makes sense to them right now but will mean nothing to the agent who picks up next week's call. Sometimes nothing at all, and a disposition code carries the entire context of a complex interaction. Now multiply that across your team. Ten agents handling the same call type will leave ten different records. Some thorough, some minimal. Some missing the most important detail entirely - what was promised, what was escalated, what the customer was told to expect next. This is the quality problem, and it compounds quietly.

The customer pays for it twice

‍

The first cost is visible: longer calls, higher AHT, agents unavailable for longer than they should be. This is what gets measured. The second cost is less visible but more damaging. The customer calls back. A different agent picks up. They open the record - and it tells them almost nothing useful. So, they ask the customer to explain themselves again. That moment - the repetition, the sense that the company was not paying attention - is where trust erodes. It’s not dramatic. It does not show up immediately in CSAT. But it accumulates, and eventually it becomes the reason a customer switches.

Our Voice of the UK Consumer 2026 research found that 42% of UK consumers have already switched provider due to poor contact centre experience. The word ‘already’ matters. These are not consumers who are at risk of switching – they’ve already left. The post-call gap is not just an internal inefficiency. It's a retention risk dressed up as an admin problem.

‍

Why training cannot fix this

The instinct, when notes are inconsistent, is to retrain. Set clearer standards. Remind agents what a good record looks like. Monitor more closely. This rarely works. Not because agents do not want to do it well, but because the system is not set up to support consistency at pace. An agent writing notes under queue pressure, with no template and no structure, will produce exactly what the conditions allow. Varying quality, varying detail, varying usefulness. The problem is not discipline or intent. It is that the task is being done manually in the least forgiving conditions possible.

What changes when AI writes the notes

Agent Wrap Up Summary generates a structured call record automatically the moment the call ends; drawing on the conversation to produce a consistent summary of what was discussed, what was agreed, and what happens next. Every call. Every agent. Every time.

Consistency is the point. Not just the time saving, though that is real: wrap time typically accounts for 15–20% of an agent's working day, and a 50% reduction returns meaningful capacity to productive contact time. For a 50-agent team, that translates to an illustrative annual saving of £175,000: based on 50 agents, 50 calls per day, a 50% reduction in wrap time, and an average fully loaded agent cost of £25,000 per year.

The more significant change is downstream. When every call produces a reliable, structured record, that record becomes the foundation for what the next agent sees before their call begins. Customer History in Contact Hub surfaces that context automatically - so the agent who picks up next week is starting the call informed.

This is how personalisation at scale works. Not by asking agents to memorise histories or search through fragmented notes. By generating a complete record on every call, so context accumulates and becomes genuinely useful over time.

‍

The record is where the loop closes

Agent Wrap Up Summary is the start of a feedback loop, not the end of one. The structured data it generates - consistent, covering 100% of calls - feeds everything downstream.

Conversation Analytics can analyse that data at scale, identifying coaching opportunities, surfacing compliance drift, and enabling AI Call Scoring that cuts QA review time from 30 minutes to approximately 5 minutes per call. Real Time Agent QA (available in Beta Q4 2026), uses it to guide agents in the moment, surfacing compliance prompts, flagging sentiment shifts, and steering conversations towards the outcomes that best records show actually work.

Better calls produce better records. Better records enable better coaching. Better coaching produces better calls. The loop only works when it is closed. And it closes after the call ends.

‍

Start with the audit

You do not need a platform overhaul to find out where you stand. Pull a sample of CRM records from last week. Read them. Ask a simple question: if the next agent had only this record to go on, what would they know? The answer will tell you more about the state of your post-call process than any metric can.

‍

‍

Want to see how Agent Wrap Up Summary works in practice? Download The Assisted Agent - our practical guide to AI-enabled agent assistance across the full call lifecycle. Or if you'd rather see it live: book a demo with the MaxContact team.

If you run a contact centre, the chances are you're managing rising call times, inconsistent quality reviews, repeat contacts that erode margin, and a personalisation gap that's hard to close without the right data infrastructure underneath it.

‍

None of these are new problems. But the distance between where most operations are today and what's now achievable is narrowing fast - and the teams pulling ahead aren't waiting for a full platform overhaul to make it happen.

‍

At MaxContact's recent webinar, hosted by Marketing Director Kayleigh Tait and Principal Product Manager Conor Bowler, we worked through four specific challenges that are costing contact centres time and money right now - and showed, live, how AI is solving each one. Here's what we covered.

‍

Challenge 1: Call length is rising, and post-call admin is a big reason why

‍

Average service call duration in the UK is now 422 seconds - seven minutes per call - according to Contact Babel. That's the highest figure recorded in 20 years of data collection, and it's been climbing steadily since 2004. There's no sign it comes down on its own.

‍

A large part of the reason is fragmentation. 96% of agents are still navigating multiple systems on every single call. Only 4% of UK contact centres operate from a single unified desktop. 40% of agents are juggling more than four applications at once - doing real-time system-surfing while simultaneously trying to solve a customer's problem or make a sale.

‍

Then there's wrap time. 18% of every call is post-call admin: writing up notes, updating records, triggering next steps. That's queue time growing while your agents do data entry.

‍

The commercial impact is significant. For a 50-agent contact centre making 50 calls a day, a 50% reduction in wrap time is worth over £175,000 a year - based on MaxContact's own ROI modelling.

What good looks like:

An agent wrap-up summary that generates automatically within seconds of a call ending, built from a live stereo transcript that's already separated the agent's voice from the customer's. The agent reviews it, makes any edits, and submits. No blank page. No three to five minutes of typing between every call.

‍

MaxContact's Agent Wrap-Up Summary feature — currently in alpha testing and moving to beta in mid-June — does exactly this. Prompts are fully configurable via Prompt Studio, so the output format, structure, and language match your operation's context, whether that's a collections agency, a sales team, or a customer service function.

‍

Challenge 2: Repeat contacts are eroding margin and driving churn

‍

42% of UK consumers have already switched provider because of a poor contact centre experience - not because of a product issue, but because of the experience itself. A further 38% have seriously considered it. MaxContact's consumer research, shared at the After Work with MaxContact event, makes clear this isn't an edge-case risk.

‍

First contact resolution is what Contact Babel calls the "miracle metric." It's consistently cited as one of the top two KPIs most influential on customer satisfaction. Every repeat call is a direct hit on that number - and at roughly £5 per service call, a repeat contact doubles your cost before you've factored in agent time and churn risk.

‍

The AI angle here is often misunderstood. 69% of customers rate AI worse than humans for understanding their issue - but the problem usually isn't the AI itself. It's where it's introduced in the customer journey. AI deployed in an emotionally charged or complex situation will struggle. The bigger failure point is the handover: when a customer escalates from an AI interaction to a human agent and has to repeat everything from scratch. That's where trust breaks.

What good looks like:

Context continuity. When a human agent picks up - regardless of whether the previous interaction was with an AI agent, a chatbot, or a colleague - they start with the full picture. Customer history, intent, what happened last time, what was agreed. Not a blank screen.

‍

That requires clean data flowing across your channels and a single interface for agents to work from. It's a foundational requirement, not an aspirational one.

‍

Challenge 3: QA based on a sample of 1–2 calls per week isn't good enough

‍

The average contact centre reviews one to two calls per agent per week. Contact Babel's most recent guide describes this explicitly as "neither fair nor valid as a performance measurement tool." That's not a MaxContact opinion - it's the industry's own assessment of its standard practice.

‍

The consequence is that coaching decisions, script adjustments, and performance reviews are all made on a handful of conversations selected at random. Objection handling failures, compliance drift, and the moments where an agent is genuinely struggling can remain completely invisible until the problem is already embedded.

What good looks like:

100% call coverage. Scorecards built on every conversation, not a sample. AI that makes that achievable without overwhelming your QA team.

‍

MaxContact's AI call scoring — now generally available to all Conversation Analytics customers — reduces QA review time per call from 30 minutes to 5 minutes. That's approximately four days of analyst capacity returned to the team every month. Capacity that can go into actual coaching, script development, and performance improvement.

‍

Scorecards are fully configurable: yes/no questions, rating scales, observation notes, auto-fail criteria. Business context can be set per scorecard so the AI understands your products, processes, and compliance requirements before it starts scoring. Scheduled auto-QA at scale — allowing always-on scoring as calls come in, or one-off compliance campaigns across historical data — is moving to beta on 6 July, with general availability planned for early August.

‍

Challenge 4: Personalisation requires the right building blocks first

‍

76% of consumers say personalised communications influence their brand choice, according to Salesforce's State of the Connected Customer. Personalisation at conversation level isn't a luxury - it's a commercial lever.

‍

But it doesn't start with AI. It starts with having the right infrastructure in place:

• Customer history and intent available before the conversation starts
• In-call sentiment detection so agents know when someone is frustrated or at risk
• Consistent context across channels - what happened on the last call, the last chat, the last AI interaction
• Next-best-action guidance that surfaces what your best agents do in key moments, and replicates it across the team in real time

‍

Once those building blocks are in place, personalisation stops being an aspiration. It becomes the logical next step, because you already have everything you need.

‍

The bigger picture: it's not about solving one problem in isolation

‍

The demo Conor ran at the webinar wasn't designed to show five separate features. It was designed to show how they connect.

‍

A single agent interface. An automated wrap-up that feeds clean data into the next interaction. Real-time transcription with stereo accuracy that improves everything built on top of it. AI scoring across 100% of conversations. Context that follows the customer, not the channel.

‍

The teams that are getting this right aren't deploying AI as a standalone fix for one metric. They're building a connected system where each piece makes the next one work better.

‍

That's the direction of travel. And a lot of it is available right now.

‍

Watch the full webinar and explore the resources

▶ Watch the full replay on YouTube → https://youtu.be/C2ED-KwbKns

‍

📄 Download the Contact Babel UK Contact Centre Decision-Makers' Guide → https://www.contactbabel.com/

‍

📅 Book a demo or speak to your account manager → https://www.maxcontact.com/book-a-demo

‍

We surveyed 1,000 UK adults who had interacted with a contact centre in the last 18 months. The findings reveal something that goes beyond wait times, channel preferences, and AI adoption. This year, the biggest barrier to customer contact isn't the interaction itself - it's getting consumers to engage in the first place.

The Numbers Don't Lie: Trust Is Now an Operational Problem

Before we get to what consumers want, we have to address what's getting in the way. Our research reveals a structural trust deficit playing out before a single agent picks up the phone.

• 69% of UK consumers always or often screen calls from unknown numbers
• 46% have ignored a message from a legitimate company because they assumed it was a scam
• Only 22% strongly agree they can tell when unexpected company contact is genuine
• 77% of those who ignored a legitimate call experienced a real consequence such as a missed appointment, an unresolved problem, a missed payment deadline

This is the Trust Gap: the growing distance between a company's confidence in its own outbound contact and what consumers actually believe when they see an unknown number. It affects every sector with outbound ambitions and it can't be fixed by dialling more.

‍

Call Avoidance Varies Dramatically by Sector

Not all sectors face the same screening wall. Our data shows stark differences in call avoidance rates, and the gap between best and worst performing sectors is significant.

Loans, credit and debt management companies are the most avoided, with 37% of consumers saying they'd be least likely to answer a call from this sector. Insurance follows at 25%, with telecoms, technology, and retail/e-commerce close behind at 22–23%. Banks and building societies fare better at 16% avoidance, and notably, they also hold the highest sector trust score at 96%.

The lesson? Trust and answer rates move together. Sectors that have invested in consumer trust over time are reaping the operational benefits in their outbound performance. Those that haven't are paying the price at the identification gate.

"This data should make every contact centre leader pause," says Ben Booth, CEO of MaxContact. "Consumers broadly trust the sectors they deal with, but that trust doesn't translate into picking up the phone. If consumers can't tell the difference between a legitimate call and a scam, outbound strategies will struggle to deliver."

‍

What Actually Makes Consumers Pick Up

The good news is that the Trust Gap is closable. When we asked consumers what would make them more likely to answer, two things stood out clearly:

• 82% say they would be more likely to answer if caller ID clearly identified the company name
• 80.5% say a pre-call text or email would make them more likely to pick up

These aren't aspirational preferences - they're operational levers. The problem isn't the dialler. It's the identification gate. Legitimate contact centres aren't losing the persuasion game. They're often not getting on the pitch.

Contact centres should prioritise:

• Branded caller ID and carrier number reputation management - so consumers can recognise your call before they decide whether to answer
• Pre-call communication - give consumers a reason to expect your call, especially in high-avoidance sectors
• Treating contact frequency as a trust variable -too-frequent contact doesn't just frustrate consumers; in regulated sectors, it carries compliance risk

‍

AI Is Here — But Transparency Is Non-Negotiable

UK consumers have been interacting with AI in contact centres for some time. The problem is, many didn't know it.

87% of consumers believe they've interacted with AI or automation in a recent company contact. Of those, 22% were sure or fairly sure they'd been talking to AI — but weren't aware of it at the time. That's more than one in five consumers who discovered, after the fact, that part of their experience was automated.

Nearly 9 in 10 (88%) consumers say it's important for companies to clearly disclose when AI is being used. Half say it's very important.

"The reputational risk of undisclosed AI is real and avoidable," says Ben Booth. "Consumers aren't opposed to AI - they're opposed to being kept in the dark about it. Deploying AI without disclosure doesn't just frustrate customers; it reinforces the same uncertainty that's causing them to screen your calls."

AI Adoption: Where It Works and Where It Doesn't

Consumer opinion on AI is nuanced. Where AI genuinely adds value, consumers are broadly willing to accept it:

• Answering FAQs: 36%
• Routing to the right department: 35%
• Account updates and billing information: 26%

But the picture reverses sharply when it comes to high-stakes interactions. Over half (54%) say they don't want AI involved in emergency situations. Significant numbers also object to AI involvement in complex account problems (50%), financial discussions (49%) and when negotiating terms (46%).

Crucially, 71% of consumers say they'd be comfortable with AI helping resolve an issue faster — as long as a human agent was available throughout. The acceptance of AI is conditional on a clear, accessible escalation path.

‍

Humans Still Matter Where It Counts

Despite the growth of AI and automation, consumers are clear about when they need a person:

• Emergency situations - 41% want a human agent
• Complex account queries - 33%
• Financial discussions — 29%
• Explaining a sensitive or personal matter - 26%
• Making a complaint - 23%

These aren't edge cases. An AI that handles a billing query well creates modest goodwill. An AI that mishandles a bereavement disclosure or an emergency can permanently damage a customer relationship.

When things go wrong and complaints happen, consumers care most about: a clear explanation of the outcome (39%), being kept updated throughout (37%), appropriate compensation when the company is at fault (33%), and only having to explain the issue once (31%).

‍

What Builds and Breaks Consumer Trust

Our research shows consistent patterns in what drives contact experience, positively and negatively.

What puts consumers off before they even try:

• Long wait times: 36%
• Being transferred multiple times: 34%
• Difficulty reaching a human: 29%
• Having to repeat themselves: 28%

What good looks like:

• Quick resolution -36%
• Easy access to a human when needed - 35%
• Knowledgeable agents - 34%
• Clear communication throughout - 32%

On channel trust, email remains the most trusted channel for company contact (51%), followed by phone calls (30%) and letters (27%). For outbound communications that don't need an immediate response, email is still the most credible messenger.

‍

Five Focus Areas for Contact Centre Leaders in 2026

Based on our findings, these are the areas that will have the most impact:

1. Fix the identification gate: Deploy branded caller ID, carrier number reputation management and pre-call communication. The recoverable opportunity isn't every screened call; it's the willing contacts who are filtering themselves out because they fear scams.
2. Make AI disclosure the default: Clearly disclose AI use at the start of every AI-assisted interaction. In regulated sectors, it's a compliance requirement. Everywhere else, the reputational risk is reason enough.
3. Protect the human escalation path : Across every question about AI in this study, the most-cited condition for consumer acceptance was the same: a human must be available and clearly signposted. Design the escalation as carefully as you design the AI.
4. Treat 'only explain once' as an infrastructure target: CRM integration, context-passing between channels, and warm handoffs are the operational response to the number one complaint driver.
5. Audit complaint journeys against what consumers actually need : A clear outcome explanation, ongoing updates, appropriate compensation, not having to repeat themselves, and a human presence. These five things determine whether a resolved complaint becomes a trust-builder or a churn trigger.

Want the full picture? The Voice of the UK Consumer 2026 report includes sector-by-sector breakdowns across utilities, telecoms, finance/debt, and insurance — with data on vulnerability handling, AI comfort, complaint experience, and regulatory risk. Download the full report here.