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Most contact centres review a small fraction of their calls. A QA analyst picks a handful, scores them, flags what went wrong, and then moves on. It feels like it ticks the box for quality assurance. But for Ofcom-regulated telecoms operations and FCA-regulated financial services firms, it’s not enough, and the consequences of getting it wrong have never been higher.

This article explains why call sampling creates compliance exposure, what always-on monitoring looks like in practice, and what to look for when evaluating your current approach.

What is call quality monitoring?

Call quality monitoring is the process of reviewing agent-customer interactions to assess whether they meet your quality, compliance, and performance standards.

It typically covers:

• What was said and how the agent handled the conversation
• Whether compliance scripts and protocols were followed
• How vulnerable customers were identified and managed
• Whether the outcome was appropriate for the customer
• How performance compares against your scoring framework

When call quality monitoring is done consistently, it gives you a documented evidence-base across every call type, agent, and campaign. But when it’s done poorly or too infrequently, it leaves gaps that regulators are increasingly likely to find before you do.

How do most contact centres currently monitor calls?

Sampling is the typical approach many call centres take to monitoring calls. A QA reviewer listens to a set number of calls per agent per month, scores them against a framework, and feeds the results back into coaching. It is time-consuming work, so let’s break down the numbers.

Example:

• A single reviewer handles 50 calls a month at 30 minutes per call.
• This amounts to 25 hours of review time.
• And it is still only a fraction of the total call volume reviewed.

The problem here is not the effort; it's the coverage. On average, contact centres manually evaluate 5% of calls per week, meaning many QA operations are leaving the majority of interactions unreviewed. This means:

• You don’t know whether your agents are consistently identifying vulnerable customers.
• You don’t know whether compliance scripts are being followed on the calls you did not pick.
• You are not building an evidence bse, only a small sample.

FCA Consumer Duty: you need evidence across every interaction, not a snapshot

For debt collection, insurance, and other FCA-regulated contact centres, the stakes are different but the problem is the same. Consumer Duty requires firms to demonstrate they are delivering good outcomes for retail customers, not just on the calls they reviewed, but consistently and measurably across their entire operation.

The FCA has shifted decisively from implementation to enforcement. Regulators are no longer asking whether you have a quality monitoring process. They are asking whether you can prove, with documented evidence, that your agents are handling vulnerable customers correctly, following compliant scripts, and not causing foreseeable consumer harm. And that’s for every call, not just the ones you checked.

A sampling approach does not produce that evidence. It produces a snapshot.

For more on what the FCA now expects from contact centres in financial services and debt collection, see our Consumer Duty guide.

The problem with call sampling: A Summary

• Sampling typically covers around 5% of calls per week, leaving the 95% of interactions unreviewed and unverifiable
• Compliance drift happens slowly. By the time sampling catches a behaviour, it is already established and harder to coach out
• Poor agent behaviour on outbound calls can go undetected long enough to trigger carrier blocking or an FCA flag
• Vulnerable customers may not be identified correctly on calls you never reviewed
• Good performance goes unrecognised as you cannot replicate what you cannot see
• A sample tells you what happened on the calls you chose to review. It does not tell you what is happening in your operation

From sampling to monitoring: what's actually required

Moving from sampling to consistent call monitoring is not simply a matter of reviewing more calls. It requires the right infrastructure in place, and historically, that infrastructure was either too expensive, too time-consuming, or both.

At a minimum, always-on monitoring requires:

• Call recording across all interactions, not just selected campaigns or call types
• Transcription that converts voice to text accurately enough to be reviewed and searched at scale
• A platform that connects recording, transcription, scoring, and reporting in one place rather than across multiple disconnected tools

Without all three, monitoring at scale either falls back on human reviewers (which is where the 25-hours-per-50-calls problem comes back in) or produces data too inconsistent to be useful as a compliance evidence base.

MaxContact's Conversation Analytics brings all of this together in a single platform. Call recording, real-time transcription, and reporting sit alongside each other. This gives your QA team a single place to monitor, review, and evidence what is happening across every interaction, without stitching together multiple tools or managing separate systems.

The reason most contact centres have defaulted to sampling is not because they did not want better coverage. It is because the operational cost of achieving it manually was prohibitive. A team large enough to review every call would cost more than most mid-market operations can justify. But that has changed.

How Conversation Analytics makes always-on monitoring feasible

Conversation Analytics is the platform that makes consistent, always-on call monitoring operationally viable for mid-market contact centres.

Rather than relying on a QA team to manually select, listen to, and score calls, Conversation Analytics connects call recording, transcription, scoring, and reporting in a single platform – automating quality assurance. Every interaction is captured, transcribed, and made reviewable, giving your QA team complete visibility across all call types, all agents, and all campaigns without the resourcing overhead of manual review at scale.

The cost comparison is significant. Replicating meaningful call coverage with human reviewers alone would cost an estimated £14,000 per month in analyst time for a mid-sized contact centre. Conversation Analytics delivers that coverage at a fraction of the cost, freeing your QA team to focus on coaching, calibration, and the complex calls that genuinely need a human eye.

How AI call monitoring surfaces insights faster

AI is what makes the insights from always-on call monitoring actionable rather than overwhelming.

Without AI, full call coverage creates a different problem; more data than a QA team can meaningfully review and act on. AI-powered call monitoring solves that by doing the heavy lifting on routine scoring, so your team's attention goes where it matters most.

The result is not just faster QA. It is a more reliable, more defensible evidence base built on every call, not a sample of them.

What to look for in your call quality monitoring approach

Is your evidence transcript-linked? Generic summaries are not a defensible evidence base. Scoring decisions need to be traceable back to what was actually said.

Is your scoring consistent? If different reviewers score the same call differently, your evidence has a credibility problem. Consistent scoring logic applied across all interactions removes that subjectivity.

Does your QA sit within your analytics platform? If scoring, feedback, and reporting live in separate tools, you create friction and risk. Everything should be in the same place.

Is human oversight built in? Your QA team should be able to review, challenge, and calibrate outputs. Always-on monitoring supports human-led governance, it does not replace it.

Are you scoring the right calls? Configurable triggers and criteria by call type, queue, campaign, or outcome, mean your monitoring effort goes where the compliance risk is highest.

The question is not whether you can afford to monitor every call

It is whether you can afford not to.

Ofcom and the FCA have both made clear that evidence of compliance needs to be consistent, documented, and demonstrable. A sampling process may satisfy an internal audit. It is unlikely to satisfy a regulator asking for proof of good outcomes across your entire customer base.

Always-on call quality monitoring closes that gap. It gives your QA team better data, gives your compliance function defensible evidence, and gives your operation a consistent view of what is actually happening on the phones across all calls, rather than just the ones you happened to pick.

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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.

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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."

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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

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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.

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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%).

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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.

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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.

Here’s a number worth sitting with: 30 minutes.

That's how long it takes to manually score one call. Listen back, fill in the scorecard, write up the notes, log the result. Do that 50 times a month and you've lost on average 4 days per reviewer to scoring alone.  

Most QA teams know this. They're not slow or inefficient - the maths just doesn't work. One reviewer for every 25 agents, 30 minutes a call, finite hours in the week. Something must give, and that’s coverage.  The industry average sits at around 5% of calls reviewed, which means 95% of what happens on your contact centre floor stays invisible.

Not just to your QA team. To your compliance records. To your coaching programme. To the agents who deserve consistent, fair feedback on every call they handle.

That's the problem AI Call Scoring is built to fix.

‍Your standards applied to every call you wish to score.

The way it works is straightforward. You define your existing QA standards - built around your business rules, your compliance requirements, your definition of what a good call looks like. AI Call Scoring applies them to your selected calls, scoring each one against your criteria using evidence taken directly from the transcript.

No algorithm deciding what good looks like on your behalf. You set the standard.

‍What this means for your QA team day to day

One of the things that doesn't get talked about enough in QA is how demoralising inconsistency is. Two reviewers score the same call differently. An agent pushes back. The process loses credibility. And meanwhile, the team is so buried in manual scoring that the actual coaching - the conversations that change behaviour -never happen.

AI Call Scoring brings review time down from around 30 minutes to about 5 minutes per call. Your QA team stop being a scoring machine and start doing what they're good at - calibrating standards, making judgment calls, and coaching agents to improve.

For a reviewer handling 50 calls a month, that's roughly four days back every month. That's a lot of coaching time that wasn't there before.

It's not just for big teams

This is worth saying clearly because it matters: AI Call Scoring isn't just a tool for large operations with dedicated QA departments.

For teams of 50 or more agents, the time savings are significant - around 133 days per year across four QA staff, worth approximately £15k in team time. But beyond the hours, scoring more calls consistently means you start seeing the patterns that a small sample will never show you.  

For smaller teams of 10 to 30 agents, it's even more of a shift. Structured QA without dedicated headcount. Team leaders reviewing scored calls in minutes. Compliance coverage that doesn't require a compliance team. And a framework that grows with the business.

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‍When compliance is non-negotiable

For contact centres operating in regulated sectors, the stakes have risen. Consumer Duty, now actively enforced by the FCA, places a direct obligation on organisations to evidence that customers are receiving good outcomes.  

When a complaint lands, you can't point to a sample. You need evidence that the specific interaction was handled correctly. AI Call Scoring gives you an auditable record of every scored call, with auto-fail rules that catch compliance breaches - a missed disclosure, an incomplete ID check - regardless of how the rest of the call went. Every call you score is evidenced, auditable and defensible.  

Your team stays in control

We want to be clear about this: AI Call Scoring is there to support your QA team, not sideline them. Every scored result can be reviewed, edited, challenged or discarded. Your people stay in the loop. The AI does the volume work - your team does the thinking.

It all lives inside Conversation Analytics — scored calls, common objections, objection handling effectiveness, top performers and saved compliance views, together in one place.

Already on Conversation Analytics? It's yours.

AI Call Scoring is included within Conversation Analytics at no extra cost. If you're already using the suite, it's available to you now.

This is also just the start. Automated QA at Scale is coming later this year - fully automated scoring across campaigns at volume. More on that soon.

If you're not yet using Conversation Analytics and want to see what AI Call Scoring could do for your team, come and talk to us. Book a demo and we’ll show you what it looks like in your environment.

High-volume cold calling based on limited data is no longer a cost-effective outbound strategy and in the B2C world can be non-compliant.

With both sales and debt collection, the public has grown wary of unsolicited calls and generic conversations. But that doesn’t mean outbound dialling is over as a revenue engine.

It means it has to be smarter, multi-channel and data-led. Based on real-time information and a refined dialling strategy. Often personalised in tone, timing and approach.

So, if you’re reselling UCaaS today, there’s a strong chance your customers’ outbound results are being held back by the platform they’re on. When revenues stagnate, contact rates fall or conversions get harder to close, the problem usually isn’t effort - it’s strategy, data and tooling. This blog sets out what a high-performing outbound operation looks like, so you can have that conversation with confidence.

The outbound metrics your customers should be measuring

The foundation of any smart outbound strategy is good information. Help your customers understand that without the right data, they can’t tell whether calls are reaching the right people, whether agents are performing, or whether their scripts are working. Measurement isn’t a nice-to-have - it’s where improvement starts.

Outbound KPIs to share with your customers:

•      Connect rates: are calls being connected to a real person?

•      Contact rate: how often are agents reaching the right decision-maker?

•      Data penetration rate: is their data being used effectively - are they making the most of high-value leads?

•      Conversion rate: the percentage of contacts that result in a positive outcome

•      Calls to success rate: the number of calls needed per successful result

Take conversion rate as a case in point. It tells your customers two things at once: the quality of their contact data, and the effectiveness of their team.

Better data means a higher likelihood of reaching the right person. Skills-based routing - matching the right agent to the right call- increases that further. And stronger training, combined with more refined scripts, means more of those conversations end the way they should.

Qualitative insight matters as much as the numbers

Quantitative KPIs don’t tell the full story.Improving contact rates will generate more conversations - but without the right skills in place to handle them, conversion rates won’t follow.

Encourage your customers to combine the numbers with qualitative insight: what objections are coming up most, what their customers are saying about competitors, and where conversations are breaking down.Helping them bring both lenses together is one of the most valuable things you can do as a partner - and it’s a conversation most resellers never have.

Industry-specific KPIs worth knowing

The metrics above apply broadly, but it’s worth helping your customers zone in on numbers that are specific to their sector.

In debt collection, promise to pay (PTP - the percentage of calls resulting in a commitment to pay) and percentage of debt collected are key indicators. In sales, first-call close rates and average revenue per call say a lot about campaign effectiveness.

MaxContact’s KPI Benchmark Report gives a detailed breakdown of what good looks like across sectors. It’s a useful resource to share with customers who want to know how their numbers stack up.

Benchmarking: what good looks like

Once your customers know what to measure, the next step is helping them understand what the numbers mean.

MaxContact’s own research found that the largest proportion of respondents - 34%, across both sales and debt collection - reported conversion rates of between 10% and 19%. Cold outbound sales calls typically convert at 1–3%; warmer, more targeted calls can reach as high as25%.

Broad benchmark ranges for common outbound KPIs:

•      Average handling time: 4–12 minutes

•      Contact rate(cold calls): 5–15%

•      First call resolution: 10–40%

These are broad ranges and will vary significantly by sector and product complexity. The more important thing for your customers is to track their own numbers consistently over time - and to understand what’s driving movement in either direction.

What your customers can do to improve outbound performance

Once your customers are tracking the right metrics,the focus shifts to moving them. Here are the levers most likely to make a meaningful difference - and the conversations worth having:

•      Team training and coaching - conversation analytics can surface objection patterns, benchmark individual and campaign performance, and show exactly where coaching will have the biggest effect.

•      Smarter dialling strategy - when are their contacts most likely to answer? Are they prioritising by lead value? Are they using the right dialler mode for the campaign? These are practical questions you can help them think through.

•      Omnichannel engagement - how does combining SMS, email and calls affect contact and conversion rates? Could AI agents handle routine calls while human agents focus on more complex or sensitive interactions?

The performance advantage you can offer your customers

Helping your customers understand and act on their outbound performance data is a powerful way to open the door to a bigger conversation. Standard UCaaS platforms can’t offer the range of insight and capability that a specialist customer engagement solution like MaxContact provides - and once customers seethe gap, the case for change makes itself.

Think conversation analytics, AI chatbots, workforce management, intelligent outbound dialling and sophisticated contact strategies - capabilities that standard UCaaS systems simply can’t match, and that enterprise-grade platforms price out of reach for most teams.

MaxContact delivers measurable results - from 200–300% increases in contact rates to doubling sales teams’ conversion rates. Benchmark Insights Report.

That’s because its intelligent, intuitive platform lets teams build smarter outbound strategies and tailor them for every campaign.

Talk to the MaxContact partner team about adding a specialist customer engagement solution to your portfolio. Book a call

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AI doesn’t fix contact centres. It scales them. If your journeys are joined up, automation can reduce the pressure your team is facing. However, if they’re fragmented, AI amplifies the friction - faster transfers, repetition and customer effort. That’s why the most useful question contact centre leaders can ask themselves isn’t “What can AI do?” - it’s, "Are we actually ready for it?".

Whether you’re running sales and retention in telecoms, payment collections with vulnerability considerations in finance, customer support in utilities, or managing multiple client programmes in a BPO, the readiness question is the same - do we have the foundations to automate without increasing customer effort or operational risk?

“Always-on” support is an operating model, not a staffing one. It's built to remove avoidable demand, protecting your team's time for high-judgement conversations, and making escalation safe when risk or complexity arises. ‍

Always-On Service Starts with Resolution, Not Headcount.

Consumers are increasingly expecting help at any time of day, across voice and digital channels. But increasing headcount to meet 24/7 customer support expectations isn’t sustainable for most contact centres operating on tight margins.

An always-on contact centre doesn’t mean agents working around the clock. It means using AI and automation to absorb predictable demand across inbound and outbound – from service updates and appointment changes to sales follow-ups and renewals, to payment reminders and self-serve arrangements - without needing an agent for every interaction.

The trap many leaders fall into is assuming that automation alone creates always-on. It doesn’t. Always-on is the result of clear journeys, consistent rules, and controlled escalation.

The Real Readiness Problem: Avoidable Demand

Most contact centres don’t struggle because customers contact them. They struggle because customers are contacting them more than once.

A lot of volume is created by operational gaps:

• Unresolved issues driving repeat contact
• Too many transfers caused by poor routing
• Long handle times driven by missing context
• Channels operating as seperate service silos

This is the stuff that quietly drains performance. It also explains why some AI programmes stall: they automate interactions on top of broken flows, then wonder why customer effort doesn’t fall, and agent workload doesn’t change.

If you want a pragmatic AI strategy, start by identifying where the operation is generating demand it shouldn’t have to handle.

A Practical Readiness Lens: Demand, Continuity, Control

To make readiness tangible, use this simple lens. If any one of these is weak, automation outcomes will be capped - or worse, you’ll scale the wrong things.

1) Demand: Do You Know What Should Be Automated?

AI delivers value when it absorbs predictable, repeatable demand - the structured interactions that don’t require human judgement. If you can’t clearly separate predictable from complex demand, you’ll either automate the wrong things and frustrate your customers or keep too much with agents and miss the efficiency gains.

A pragmatic starting point is mapping the top drivers and asking: which ones are genuinely structured, and which are only “simple” because we’re not seeing the full context?

2) Continuity: Does Context Move with The Customer?

Customers think in outcomes, not channels. Readiness means your operation can maintain continuity when a conversation starts in chat and moves to voice, or when an outbound reminder triggers an inbound response, or when a customer returns with a follow-up and expects you to remember what happened last time.

If context doesn’t travel, automation becomes a reset button, and resets are where handle time, repeat contact, and frustration grow.

3) Control: Can You Escalate Safely and Measure Outcomes?

Automation should never be a dead end. When complexity rises, or when there’s vulnerability, a complaint, payment risk, or compliance exposure, you need controlled escalation to a human agent with the full context carried across.

If you can’t define escalation rules and success measures beyond containment” you’re not ready to scale. You’re ready to pilot.

Where AI Fits When You’re Ready: Layers, Not Channels

A common mistake is deploying AI as separate tools by channel - a chatbot here, an AI agent there - and expecting it to add up to an always on operation. It simply adds more mini contact centres to the one you already have.

A more practical approach is to treat AI as layers across the operating model:

• Decision layer (AI Agents): Interprets intent, resolves structured interactions, and prevents outbound activity from automatically creating inbound pressure through unmanaged follow-up

• Asynchronous layer (chatbots and messaging): Allows customers to complete routine tasks without joining a queue, while keeping journeys connected across voice and digital
 
• Visibility Layer (Conversation Analytics): Shows where demand originates, where conversations stall, and what drives repeat contact so you can improve routing, coaching, and automation design based on evidence rather than instinct

When these layers support end-to-end workflows, AI stops being a bolt-on and becomes a genuine performance lever.

A Quick Readiness Check: The Questions Most Teams Skip

If you’re planning AI-enabled automation this quarter, these questions are worth answering before you commit time and budget:

• What proportion of our demand is truly predictable and repeatable?
• Where do customers repeat themselves, get transferred, or drop out?
• What's creating repeat contact and how will we remove it?
• What are our escalation triggers for risk, vulnerability or complexity and do we trust them?
• How will we measure success beyond containment - effort, quality, outcomes, stability?
• Do inbound and outbound journeys reinforce each other, or create extra pressure?
 

If those answers aren’t clear yet, that’s not a blocker, it's your roadmap.

Pressure-Test Your Readiness With The Scorecard

If you want a structured way to benchmark readiness across the foundations that matter - demand, continuity, escalation, and operational fit - our scorecard is designed for exactly that. Use it to create alignment internally, prioritise improvements, and shape an automation roadmap that holds up under real-world volume, not just pilot conditions. Download the Always-On Contact Centre Readiness Scorecard here.

The pressure to introduce AI in contact centres has never been greater. But automating the wrong interactions doesn’t just waste investment - it actively frustrates customers and creates more work for your team. Here’s how to get it right from the start.

This article is based on a recent webinar - Watch the full replay on YouTube.

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The real challenge isn’t how to automate - it’s what

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Most business leaders today aren’t asking whether to use AI in their contact centre. They’re asking where to start. And that’s exactly the right question to be asking.

We recently hosted a webinar exploring this challenge with Kayleigh Tait, Marketing Director at MaxContact, and Conor Bowler, Principal Product Manager. Together, they walked through the research, the common pitfalls, and a practical framework that helps contact centres make confident, data-driven automation decisions.

Here’s what they covered.

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What UK consumers actually think about AI

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MaxContact commissioned an independent survey of over 1,000 UK consumers who had interacted with a contact centre in the last 18 months. The findings from the Voice of the UK Consumer Report are revealing.

• 45% of UK consumers say they’re comfortable interacting with an AI-powered chatbot or virtual assistant. But 36% say they’re uncomfortable.
• Only 36% say AI has improved their experience. Almost the same number - 32% - say it has made things worse.
• 65% of 25–34 year-olds are comfortable with AI, compared to just 27% of over-55s.
• 70% want a human when explaining their specific situation. 67% for emergencies. 61% when making a complaint.
• 55% of consumers have abandoned calls because of excessive wait times. 26% because they had to repeat information.

The takeaway? Automation isn’t automatically improving customer experience. It depends entirely on how and when it’s used - and critically, whether the strategy has been built around the customer or around internal efficiency targets.

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The modern inbound customer journey

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Most businesses treat every interaction the same, routing everything to queues. But inbound demand isn’t evenly distributed. It follows a pattern.

At the start of the journey, volumes are high and queries are simple: balance requests, payment dates, appointment changes, status updates. This is where AI and automation deliver the greatest impact - resolving queries quickly, reducing cost to serve, and freeing agent capacity without compromising experience.

As complexity increases, the role of automation shifts. Intelligent routing, context preservation from AI to human handover, and real-time agent support all help agents handle harder conversations faster and with more confidence.

At the resolution and advocacy stages, humans lead - supported by AI insights, not replaced by them. The goal is that automation removes repetitive workload at the top of the funnel, so people can focus on the interactions where judgment, empathy, and experience really matter.

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How Conversation Analytics uncovers automation opportunities

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Before you decide what to automate, you need to understand what’s actually happening in your contact centre. Conor Bowler demonstrated exactly how MaxContact’s Conversation Analytics makes this possible - at scale.

In the demo, Conor surfaced 28,000 calls from a single month, immediately identifying intent clusters: appointment booking accounted for 10% of interactions, technical challenges for 4%. Together, that’s 14% of call volume with clear automation potential - identified in minutes.

Using MaxContact’s AI assistant within the platform, teams can drill into individual calls, ask whether elements of those interactions could be automated, and use those insights to design workflows in MaxContact’s Workflow Studio. Those workflows can then be deployed directly to chatbots, voice agents, or email channels - with built-in escalation paths when automation reaches its limits.

For contact centres without Conversation Analytics today, this process is still possible — but relies on manual call sampling, disposition codes, and CRM data. It’s achievable, but slower and harder to repeat consistently over time.

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The MaxContact Automation Framework

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Based on research findings and direct experience working with contact centres of all sizes, MaxContact has developed a four-step framework for identifying automation opportunities.

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Step 1: Start with real interaction data

Automation decisions should be driven by evidence, not assumption. Too often, automation projects are led top-down - driven by boardroom pressure or a use case that sounds innovative rather than one grounded in data. Starting with call recordings, chat transcripts, CRM data, disposition codes, and repeat contact patterns gives you the factual foundation to make better decisions.

Look for patterns: what are the most common reasons for contact? What consistently takes under three to four minutes to handle? What drives re-contact within 24 to 72 hours? Technology makes this repeatable - so you’re not starting from scratch every quarter.

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Step 2: Cluster by intent

Rather than analysing by channel (voice vs email vs chat), cluster interactions by customer intent. Instead of ‘20,000 calls’, ask: how many were payment date queries? Balance requests? Appointment changes? Customers don’t think in channels — they think about the problem they want to solve.

Conversation Analytics surfaces these clusters automatically, saving hours of manual analysis and revealing patterns that might otherwise go unnoticed.

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Step 3: Rank by volume and effort

Not every repetitive query should be automated. Ranking by two lenses — volume (how often does this occur?) and effort (how much friction does this create?) - helps you prioritise strategically.

• High volume + low effort: immediate automation potential.
• High volume + high effort: may require journey redesign before automation.
• Low volume + high effort: remain human for now.
• Low volume + low effort: monitor and consider as a pilot.

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Step 4: Validate with your team

Before you automate anything, validate the decision with the people who handle those conversations every day. Ask: Is this emotionally sensitive? Is it a brand touchpoint that customers value? Does it spike seasonally? Does what looks like a simple query often become a complex one underneath?

A payment query might look straightforward - but if it frequently leads to a conversation about payment difficulty, that’s not a candidate for full end-to-end automation. This step prevents automation decisions that look good on paper but frustrate customers in practice.

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How do you know your automation is working?

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Automation is working when three things improve simultaneously: business outcomes (cost to serve, conversion, retention), customer experience (faster resolution, less repetition), and operational performance (agents spending less time on repetitive tasks and more on complex conversations). If automation only improves one area, it’s likely not deployed in the right place.

Monitor containment rates, drop-off points, and escalation paths on a weekly basis for early warning signs. Review and optimise on a quarterly basis - or more frequently in fast-moving markets with changing regulation or customer expectations.

Ready to start your automation journey?

Watch the full webinar replay on YouTube.

Download the Voice of the UK Consumer Report.

Book a complimentary automation consultancy session with our Customer Success team and we’ll run you through the MaxContact Automation Framework for your organisation: https://www.maxcontact.com/book-a-demo

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