Sureserve operates as a Smart Meter Operator, responsible for installing smart meters on behalf of energy suppliers. A core part of their operation involves making high‑volume outbound calls to customers to book smart meter installation appointments - making efficiency, accuracy, and scale critical to success.

To support this, Sureserve required a reliable dialler solution capable of handling outbound calling at scale while reducing manual effort. As the business grew, there was also a need to improve customer engagement channels and streamline pre‑call processes.

One challenge that emerged over time was knowledge continuity. While the system was already in place when the current team joined, training knowledge had been retained by previous staff and was lost during role transitions. This limited full visibility of the platform’s capabilities, even as usage expanded across the business.

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Our partnership with InDebted is an example of AI working hand-in-hand with humans, a combination we will see more of in the future. Since their AI Agent joined the team, InDebted’s contact centre productivity grew by 30% and resolution rate by 12%.

Creating space for humans to focus their time and efforts where it’s most needed is one of the greatest values AI can bring to contact centres.

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InDebted is a fintech startup revolutionising debt collection by helping customers boost their financial fitness. Their product uses empathetic digital messaging and offers self-serve options which makes it easy for customers to resolve their accounts. Since its launch in 2016, InDebted has helped over 250,000 customers with a 98% customer satisfaction.

When COVID-19 hit, InDebted saw a spike in the need for debt repayment support. When reaching out to customers, the team realised they were spending most of their time guiding customers on tasks that were fully enabled through self-serve. InDebted was looking for a way to continue supporting customers to self-serve their enquiries and payments while creating the space for their call centre team to help with the trickier enquiries. They wanted a solution that was smarter than an Interactive Voice Response (IVR), could provide great customer experience and deliver on their call deflection goals.

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In Australia, 21,000 people die every year from smoking-related diseases. Quitline Victoria is looking to improve this statistic.

Quitline offers trained counsellors to help and support those who want to quit smoking. Studies show Quitline dramatically increases a person’s chances of stopping smoking. Quitline is a phone-based service and so engagement is an incredibly important metric. The more people that interact with Quitline, the more impact counsellors can have. That’s why Quitline was intrigued by our AI Agents ability to call people at scale with conversational AI.

Completion rates and re-engagement with the Quitline programme have increased since using AI Agent. With AI Agents, Quitline sees answer rates of 62%, completion rates of 18% and re-engagement rates of 10%. Here is the story of how Quitline is helping people quit smoking with us.

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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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According to our latest Benchmark Report, 66% ​​of contact centres are currently using or piloting AI with the aim of reducing operational costs and driving productivity.

But measuring ROI from AI automation isn’t straightforward.

A contact centre specialising in debt collection may measure ROI through reduced cost per contact, improved payment completion rates, or increased compliance consistency. Meanwhile, an outsourced contact centre that handles high-volume inbound enquiries may focus on deflection rates, average handling time or agent utilisation.

Channel mix can also influence impact. Voice-reliant operations see ROI through reduced call queue pressure and lower cost per call, while digital-first environments may prioritise customer containment and response speed.

Measuring AI ROI properly means understanding what success looks like in your specific environment, rather than relying on generic savings estimates.

Start with a baseline: what does a human-handled interaction really cost?

Before you can begin to measure the return from AI automation, you need a clear picture of what interactions cost when they’re handled by people.

For most UK contact centres, the average cost of a human-handled voice call sits between £5.50 and £6.50 per interaction. This is often used as a headline figure, but it doesn’t tell the whole story.

The total cost is driven by other factors, including:

• Agent salaries and on-costs
• Training and onboarding, which are made more expensive by high attrition rates
• Quality assurance and compliance overhead, including call monitoring and reporting
• Out-of-hours staffing, which significantly increases the cost for 24/7 coverage
• Inefficiencies caused by repeat calls, transfers and long handle times

Even when handled efficiently, live calls demand dedicated agent time, whereas digital interactions can be managed asynchronously and at a greater scale.

A voice-heavy operation will feel cost pressure very differently from a digital-first one, and ROI calculations need to reflect that reality.

By contrast, AI-handled interactions typically cost a fraction of a human-handled call, often coming in at under £0.50 per interaction, depending on channel, complexity and volume. That gap is where ROI potential starts to emerge, but only if you understand what you’re replacing or augmenting in the first place.

Put simply, you can’t measure ROI without first understanding what each interaction costs you now. Without a baseline, your savings might look impressive on paper but will prove impossible to validate in practice.

The core ROI generators of AI automation

Not every AI capability is designed to solve the same problem, and not every contact centre will prioritise the same outcomes.

The key to measuring ROI accurately is understanding where value is being created in your operation.

1. AI Agents: reducing cost per interaction and extending capacity

AI Agents deliver ROI by reducing the cost of handling routine interactions and extending service availability without increasing headcount.

Instead of relying solely on human agents to manage every enquiry, AI Agents can handle high-volume, repetitive interactions end-to-end. This includes tasks such as customer authentication, balance enquiries, payment queries and status updates. Each interaction handled by an AI Agent reduces the cost of a human-handled call.

From an ROI perspective, contact centres typically measure:

• Cost per AI-handled interaction (often under £0.50)
• The percentage of total interactions fully handled by AI
• Reductions in out-of-hours staffing costs
• Reduced call queue pressure during peak periods

When AI Agents are used to automate between 40-60% of repetitive interactions, the cost impact is significant. Organisations frequently see monthly savings running into tens of thousands of pounds, driven purely by lower cost per interaction and improved utilisation of human agents.

Extending availability without adding cost is one of the strongest ROI drivers for AI Agents. We explore this in more detail in How to Offer 24/7 Customer Support Without Increasing Headcount.

ROI in Real Terms: Indebted

For Indebted (a contact centre in the debt collection industry), automating repetitive interactions with an AI Agent led to a 30% increase in contact centre productivity and a 12% uplift in resolution rates.

Read the full case study.

2. AI Chatbots: deflection, containment and digital ROI

While AI Agents reduce the cost of handling interactions, AI Chatbots drive ROI by preventing interactions from becoming calls in the first place.

AI Chatbots aren’t a separate intelligence layer. They’re a digital channel through which AI Agents operate, using the same logic, workflows and compliance rules. The difference is where the interaction happens.

From an ROI standpoint, AI Chatbots are measured through:

• Deflection rates (queries resolved without reaching an agent)
• Reduction in inbound call volume
• Digital containment rates
• Cost difference between chatbot interactions and human-led webchat or calls
• Impact on Average Handle Time (AHT) by removing routine demand

When routine queries are resolved digitally, contact centres reduce inbound pressure, shorten queues and protect agent capacity. Customers benefit from instant responses, while the organisation avoids the higher cost of voice-based interactions altogether.

For a deeper look at how reducing routine demand directly impacts handle time, see How to Reduce Average Handle Time in Your Call Centre.

3. AI-powered conversation analytics: ROI beyond cost reduction

Not all AI-driven ROI comes from removing interactions. Some of the most valuable gains come from making existing interactions more effective.

AI-powered conversation analytics deliver ROI by improving visibility, performance and compliance across every conversation. Teams gain insights across 100% of interactions instead of manual samples.

From an ROI perspective, contact centres typically measure:

• Reduced manual QA effort and review time
• Faster onboarding and agent coaching
• Improved compliance monitoring and risk identification
• Earlier identification of call drivers and friction points
• Improvements in agent effectiveness over time

Conversation analytics don’t directly reduce demand. Instead, they help contact centres understand why interactions are happening, where time is being lost, and how performance can be improved at scale.

ROI looks different depending on your contact centre model

The value AI automation delivers depends on how your contact centre operates, what pressures you’re under, and what success looks like to you.

Below are three common models, and how ROI typically shows up in each.

Debt collection & financial services

In debt collection and financial services, ROI is closely tied to cost control, compliance and availability.

Key ROI drivers typically include:

• Lower cost per contact
• Consistent, auditable compliance
• Always-on availability without expensive out-of-hours staffing

AI Agents are particularly effective here because they can handle structured, repeatable interactions reliably, including:

• Customer authentication
• Payment flows
• Balance and status updates

By automating these journeys, organisations reduce inbound demand on human agents while ensuring interactions are handled consistently and compliantly.

As seen with Indebted, automating high-volume, predictable enquiries helped reduce the cost per interaction while maintaining service availability across extended hours.

Outsourced contact centres and BPOs

For outsourced contact centres, ROI is less about absolute cost reduction and more about margin protection and scalability.

Typical ROI focus areas include:

• Improving agent utilisation
• Protecting margins under fixed-price or SLA-based contracts
• Maintaining service levels during demand spikes

AI plays a key role by absorbing predictable demand during peak periods, reducing the need to rapidly scale your headcount. This helps BPOs meet SLAs without over-recruiting or burning out agents during busy periods.

There’s also a longer-term ROI impact through reduced pressure on frontline teams, which can help lower churn and stabilise delivery costs.

Public sector, health and support services

In public sector and support-led environments, ROI is often measured in capacity, continuity and service quality, not just financial savings.

Key ROI considerations include:

• Extending service availability with limited budgets
• Reducing pressure on frontline staff
• Protecting agent wellbeing in emotionally demanding roles

ROI in Real Terms: Quitline Victoria

Using AI Agents to support outbound engagement, Quitline Victoria achieved a 62% answer rate, 18% completion rate and 10% re-engagement rate, extending service reach without increasing pressure on frontline counsellors.

Read the full case study.

In this context, ROI is realised through better allocation of human effort, improved service continuity and a more sustainable operating model, rather than simple cost removal.

A practical framework for calculating AI automation ROI

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Common ROI mistakes to avoid

When measuring the ROI of AI automation, it’s easy to focus on the headline numbers and miss what actually drives long-term value. These are some of the most common pitfalls contact centres run into.

Measuring AI in isolation
AI rarely delivers ROI on its own. Its impact comes from how well it’s embedded into existing journeys, channels and workflows. Measuring AI separately from call routing, workforce management, or analytics often underplays its true value.

Expecting 100% automation
AI isn’t designed to handle every interaction. The biggest gains come from automating the right interactions. The interactions that are predictable, repeatable and time-sensitive. Complex or sensitive conversations should always be assigned to human agents.

Focusing only on call deflection
Reducing inbound volume matters, but it’s not the whole picture. ROI also comes from shorter handle times, better first-contact resolution, smoother handovers and improved agent productivity.

Ignoring quality, compliance and experience
Lower cost interactions mean very little if service quality drops or compliance risk increases. ROI should always be measured alongside consistency and customer outcomes, especially if you’re operating in a regulated environment.

Treating ROI as a short-term metric
AI ROI compounds over time. As models learn, workflows improve, and teams adapt, the value of it grows. Measuring success only in the first few weeks can hide the longer-term gains in capacity, scalability and higher resilience.

ROI is about balance, not replacement

The strongest ROI from AI automation comes from supporting people rather than replacing them.

Used as part of a human-AI hybrid model, AI Agents, AI Chatbots and analytics help contact centres reduce cost per interaction and extend capacity to deliver a more consistent service, without increasing headcount or burning out teams.

ROI isn’t something you measure once and move on from. The most successful contact centres refine automation over time as demand, channels and expectations change.

If you want to understand what ROI could look like in your contact centre, start by exploring how AI can support your existing operation.

Explore AI contact centre solutions

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