We’re caught between high expectations and uneven delivery. AI has the potential to transform contact centres, but only if implemented in a transparent, human-centred, and context-aware way. This article explores how consumer sentiment, operational strategy, and evolving AI technologies converge to reveal what works and what needs further improvement.

In our February 2025 whitepaper, Customer Contact Panel highlighted that this would be a ‘year of difficult conversations’ in which speed, automation, and empathy must be reconciled. AI can increase efficiency, but risks creating a sense of detachment if it isn’t matched with emotional intelligence. Interestingly, complementary research suggests people are more honest with AI when judgment is removed, particularly in sensitive domains such as mental health, financial support, or legal services. However, as the MaxContact report confirms, the majority of consumers still turn to voice when the stakes are high.

What Consumers Are Saying (And Why It Matters)

Voice AI by the Numbers — summarising adoption, customer preferences, and industry usage stats from the Synthflow whitepaper.

What does all this mean for CX leaders?

MaxContact’s survey found that 55% of people abandon calls due to long wait times, while 35% cite the agent’s lack of understanding. Complex account issues, payment negotiations, or emotional complaints are scenarios where empathy matters (and where automation often fails). The data reinforces what many CX leaders already sense: customers will accept AI for triage or routine tasks, but demand a human for anything nuanced.

Only 36% of respondents believe AI has improved their contact centre experience, and nearly 32% say it has made it worse. There’s a clear generational divide: 65% of 25-34 year-olds are comfortable with AI, but only 27% of over-55s feel the same. This generational lens is essential when planning AI and omnichannel strategies.

The core problem is bad AI, not AI itself. As noted in our earlier whitepaper, many AI deployments fail not due to technical limitations, but due to design and governance flaws. When AI is introduced without clear escalation paths, brand tone calibration, or decision traceability, customer confidence suffers. Mature solutions in the market now take a more human-aligned approach, creating AI agents that behave like brand-trained teammates, capable of recognising tone, understanding escalation logic, and respecting compliance frameworks.

Every decision should be traceable. Every transfer should carry context. These principles distinguish AI that scales from AI that stalls.

Omnichannel vs. Human-Centric: Getting the Balance Right

Consumers prefer voice support for immediate, emotionally resonant assistance. MaxContact’s research shows 60% view phone calls as the fastest route to resolution, far surpassing digital channels. Automation should manage repetitive tasks and noise, freeing up humans for high-value, high-empathy interactions. Smart triage, seamless handoffs, and transparent automation logic are crucial for omnichannel success.

Trust, Tone, and Transparency: Designing AI That Works

To address the most cited customer frustrations: poor escalation, limited response options, robotic tone – solutions must be designed with:

• Cultural and tone calibration
• Customisable escalation protocols
• Transparent audit trails
• Privacy-by-design aligned to GDPR and beyond

These aren’t technical ‘extras’, they are fundamental requirements in sectors where mistakes can harm trust, reputations, or wellbeing. In regulated or high-stakes categories such as healthcare, dating, or finance, the operational risk of misjudged automation is simply too high.

AI has advanced quickly, but trust remains fragile. Customers want efficiency, but not at the cost of clarity or empathy. The future of contact is digitally respectful, not just digital. The best AI solutions will pause, listen, and escalate when needed, not just answer fastest.

For contact centres navigating this balance in 2025, the opportunity lies in creating experiences that feel both seamless and human where AI takes the pressure off, but never takes over.

The promise of utilising AI in contact centres is enticing: faster service, lower costs, and increased productivity that pleases the CFO. Initially, the numbers support this claim. Efficiency increases by 30 to 55%, costs decrease, and there is the opportunity to scale without increasing headcount.

However, this success story hides a hidden threat that can quietly undermine every gain made.

It is the paradox at the heart of traditional AI implementations: the more you optimise for productivity, the faster you accelerate burnout.

If you’ve engaged them properly in your AI project, then at first agents will welcome the support. AI can take on routine admin, provide helpful prompts, and cut down on cognitive load, but as performance improves, expectations rise. The business begins to see these AI-powered gains as the new normal.

Over time, this creates a silent squeeze. Agents have less recovery time, more pressure, and fewer moments of meaningful human connection. Stress builds, job satisfaction falls, and staff turnover rises. It is suggested that within 12–24 months, many contact centres could face a reversal of their early gains.

This is the AI productivity half-life—a concept backed by both data and human psychology. Traditional AI shows impressive results in the first year, but by year two burnout sets in, staff turnover increases, training costs rise, CSAT drops and the contact centre enters a cycle of decline.

The numbers paint a sobering picture:

• Training new agents costs £15,000–£25,000 per head
• AI systems degrade when experienced agents leave
• Customer loyalty falls when interactions lose their human touch

Ironically, the very systems designed to enhance performance can end up eroding it, because the human layer was never fully accounted for.

So, what is the alternative?

The better agentic AI solutions that we are now seeing take a fundamentally different approach. It’s not about replacing humans, it’s about augmenting them in sustainable, psychologically informed ways.

The AI agents don’t just answer questions; they complete tasks, make decisions, and manage workflows. They take pressure off humans without cutting them out.

And the results?

• Better job satisfaction
• Longer-lasting productivity
• Reduced turnover
• Stronger customer outcomes

More importantly, these systems are designed with long-term ROI in mind, not just how efficient your contact centre is today but how sustainable it will be two or three years from now.

How to Mitigate the AI Burnout Trap

• Develop an agentic AI that reduces workload without diminishing human agency.
• Shift KPIs beyond AHT to include FCR, CSAT, and engagement scores.
• Implement phased rollouts to monitor human impact before scaling.
• Prioritise agent training and involvement in AI design and deployment.
• Track agent wellbeing alongside operational performance.
• Combine automation with empowerment, ensuring humans retain control over complex and meaningful tasks.
• Regularly audit AI value, evaluating cost, sustainability, and satisfaction.

Agents often juggle multiple tasks during customer interactions, from information retrieval across systems, to research perhaps through search engines, and data entry to note-taking in CRMs and/or admin systems. Not to mention holding a conversation where they are listening and responding as naturally as possible. It’s a lot to ask while also ‘being present’ with the customer. The opportunity for errors and a sub-par conversation is obvious.

AI-driven hands-free conversations are designed to remove everything other than the conversation from the agent’s to do list.

What is the AI doing in Hands-free Conversations?

This again builds on the previous use cases as a good starting point. Think right back to use case 1 – autowrap, where the AI summarises call notes, and can either simply be copied and pasted into the CRM by the agent, or automated through deep integrations.

Imagine then a world, where not only does the AI do this, but it also navigates you though CRM screens as well as other platforms and apps, retrieving customer records and auto-populating information as you go. Meanwhile use case 5 – agent assist, is popping up with useful prompts to guide the call. Science fiction? Or science fact. The reality is that this is a genuine use case of today.

Key Benefits of Hands-Free Conversations: Absolute focus on the call

A truly liberating experience, the agent is focused solely on their conversation with the customer, while being fed the information they need to support the call and without worrying about what they are capturing as the AI is listening and interpreting to do that on their behalf.

The agent can listen intently, truly process the query and be mentally available to deliver responses where they’ve had the headspace to consider its appropriateness and the style of their delivery. Placing the human interaction at the very centre of the call to the exclusion of all other noise is extremely valuable when it comes to resolving that customer’s needs.

Reduced time spent on admin

If you were to say 10-20% of an agent’s time on a call is just typing and clicking to enter information and navigate screens, while a slightly arbitrary number, it’s inevitably slowing the call and reducing its value to the customer as the agent fills to give themselves the time to type.

Reduced keying errors

As the AI takes care of data entry, there are fewer agent keying errors. Not only does this reduce time on corrections, assuming there are field validations in place, or time taken to later interpret poorly captured data, it improves data quality overall. A key requirement for better analysis, better AI, better compliance, and better future performance.

Improved accessibility

What’s more, hands-free conversations can enhance accessibility – acting as a reasonable adjustment for people with visual impairments or limited hand function.

Implementation Considerations

First, the deep integrations necessary to support hands free integrations take time and shouldn’t be underestimated. Which is in part why this is use case 6 of 7. Because there will need to be some AI maturity building already to ensure both support for and success for this use case. But assuming you have that, it’s a natural progression to freeing agents simply to support customers.

However, there is another school of thought. Where the AI simply deals with all of the legacy for you. Which means you simply live with the poor processes, old mainframe systems, disparate add-ons and Excel spreadsheets you currently have, but without having to interact with them. The savings of not dealing with those, and not having to learn complex keying procedures to get to the screen you want, would be phenomenal and free cash for investment elsewhere. Listen from around 45 minutes into the webinar for Jimmy’s slightly mind-blowing hot take.

Second, accuracy and model training is paramount, which means training and testing the models will also take time and effort. As with other use cases, you will need to develop your own views of what is acceptable

Third, while it sounds all-encompassing, you could consider running the trained model locally and therefore reduce the computational costs.

Measuring Success

The primary KPIs here sit in customer satisfaction, agent productivity/average handling time and data entry or processing error rates. Beyond those, agent job satisfaction can be measured through feedback, attrition rates, etc.

But by far the most interesting benefit is the absolute focus on the customer and the delivery of superior service that should translate through to customer lifetime value.

To find out more about how CCP can help you make the right technology choices, read more here or get in touch.

This series of articles is drawn from our webinar with Jimmy Hosang, CEO and co-founder at Mojo CX. We explored seven key use cases for AI in contact centres, starting from the easiest productivity gains to value generating applications. You can find a summary of all seven use cases here, or watch the webinar in full here.

Identifying and supporting vulnerable customers – such as those experiencing financial difficulties, health issues, or emotional distress – is crucial for ethical, compliant and effective service delivery.

While the FCA has long taken a leading role in this space, other regulators such as Ofcom and Ofgem have also required vulnerability protections to be in place, with the UK’s Digital Markets, Competition and Consumers Act 2024 (DMCC), which came into effect on 6 April 2025, also widening the concept of vulnerable customers.

With thresholds higher than ever, the risks of not identifying vulnerable customers can be significant. Fines can now be imposed without a court order and at eye-watering levels, with reputational risk a compounding facto, not to mention the impact on vulnerable individuals themselves.

What’s more, with the divergence between UK and EU law, any cross-border businesses need to be even more on their toes in different jurisdictions.

What is the AI doing to detect vulnerability?

With the preceding three use cases essentially laying the groundwork for this kind of analysis and management, AI can identify signs of vulnerability by analysing speech patterns, language cues, and emotional indicators.

Key benefits: Categorisation and risk scoring

Vulnerability is a spectrum, and customers can move in and out of vulnerable states or between risk factors. Detecting this manually, however, is fraught with difficulty.

First, different people have different – and subjective – views on whether a customer may be indicating a vulnerability factor.

Second, the cues can be subtle and therefore challenging to pick up, especially when an agent – as a normal part of their job – is multi-tasking across multiple screens, taking notes and trying to hold a conversation at the same time.

But the AI is far less likely to miss those cues, because it isn’t distracted, isn’t having a bad or busy day, and doesn’t have empathy as an emotion. Any AI empathy is trained in data, and consequently consistently applied.

Record accuracy

As with use case 1, the use of AI enhances note taking and record-keeping by transcribing and summarising the call automatically. This avoids any temptation to rush the process, and risk non-compliance, while allowing the agent to focus solely on the customer’s needs.

Compliance alerts

While you could employ this kind of analysis in-flight, where prevention is almost always more desirable than cure, even a post-call analysis allows for flagging of potentially vulnerable customers and pro-active outbound or other management of that customer. retrospectively, and still gain some of the benefit, the nature of the regulatory and legal environment makes a real-time approach more desirable, with a prevention rather than cure approach.

Real-time vulnerability detection

The ultimate deployment of real-time detection during a call allows agents to adjust their approach on the fly. And for a true ‘belt and braces’ approach, if a risk score is exceeded, this can be flagged as a ‘red alert’ to the agent, very clearly instructing them not to sell, to do a welfare check, or provide relevant support information.

All of which not only manages the risk to individuals and the business, but empowers agents with the confidence to handle sensitive situations effectively and retains consumer trust through a commitment to their wellbeing that can also foster loyalty.

Implementation Considerations

Again, systems integration and data privacy are key factors in implementation, especially around matters of data usage and consent. As is training and embedding belief in the AI.

But where in other use cases it may be that the cost (or perceived cost) and complexity (or perceived complexity) of implementation of the AI project make the decision more difficult, in this instance, the potential of the AI is less about an ROI against cost than it is about ROI against the potential cost of those eye-watering fines if getting it wrong.

Measuring Success

Here, measurement may be a little trickier, depending upon how well you are able to understand the current baseline. Consider that manual QA is based on only 1-2% of calls, there could be whole swathes of risk going undetected.

The ideal situation is that there is nothing to measure. No issues, no incidents.

However, you can look at measures such as customer feedback from vulnerable customers, the numbers of interventions such as welfare or information provided, and adherence to regulations, particularly if using retrospectively rather than real time.

But the real benefits come from what doesn’t happen, rather than what does. In summary, they are:

• More frequent and consistent identification of customer vulnerability
• More accurate records
• More confidence in your compliance
• Less perceived risk within the business

Using AI to identify vulnerable customers enables contact centres not only to improve on consumer duty and meet the right ethical standards with empathetic and responsible service, it hugely decreases the risk of the worst possible outcome (from a business viability perspective) of an unexpected knock on the door from the regulator and/or widespread bad press.

To find out more about how CCP can help you make the right technology choices, read more here or get in touch.

This series of articles is drawn from our webinar with Jimmy Hosang, CEO and co-founder at Mojo CX. We explored seven key use cases for AI in contact centres, starting from the easiest productivity gains to value generating applications. You can find a summary of all seven use cases here, or watch the webinar in full here.

Quality assurance (QA) is a staple of every contact centre, more so where compliance and regulation demand it. Traditionally, manual QA reviews are concerned with the customer interaction itself, are labour-intensive and typically cover only 1-2% of calls.

While manual QA will pick up some training points, through a lack of comprehensive coverage, it often misses systemic issues that haven’t become immediately obvious elsewhere in the organisation but that could be found buried in call analysis.

What is the AI doing in Auto QA?

Auto QA uses artificial intelligence to automate the evaluation of both customer interactions through transcription (remember use case 1 – autowrap) and sentiment analysis, and what the agent did on systems.

Let’s examine the benefits.

Key benefits: Comprehensive coverage

With AI, it is possible to cover 100% of interactions; to fully assess agent performance consistently and at scale across all interactions and all areas of the QA scorecard, and send alerts straight to a team leader’s desktop.

Resource optimisation

With manual QA, you typically see around a 1:30 or 1:50 ratio of manual QA people to agents. But with Auto QA, you can expect around a 75% reduction in that overhead. Which is significant when working on fine margins, either in headcount reduction, or redirecting those resources to transformation or speech analysis tasks as opposed to data gathering.

Consistent evaluations

As with any human task, while we may believe all QA people are using their scorecard and delivering in the same way, even with calibration sessions and financial incentives, the chances of that being the case are slim; you may already know this from those calibration sessions. Indeed, the interpretation of the calibration itself may be flawed – for example, two different people may have very different takes on what constitutes empathy.

So while an AI scorecard evaluation of a voice interaction may, for example, only be 80% accurate to begin with, it is consistently 80% accurate, as opposed to the potential for human analysis to vary significantly and most likely sit at a lower accuracy figure of around 65%. Meaning more calls are scored at greater accuracy overall.

Real-time feedback

Finally, the benefits of real-time feedback while softer, are easy to understand. And completely measurable via the scorecard.

First, immediately picking up training points allows the agent to implement improvements on the very next interaction.

And second, for an agent taking hundreds of calls a day, picking up a training point even a few hours after the call occurred – especially if the interaction reason or resolution is atypical – makes it harder for the improvement points to stick, even with the benefit of the call to hand.

Implementation considerations

Aside from systems integrations, data privacy and compliance – and instead focusing more on the vagaries, of AI – accuracy (or lack of it) immediately translates through to an impact on human resources, where a less accurate AI could result in wasting resources on issues that aren’t issues.

Which is why it is always desirable to ensure there are humans in the loop (HITL), both in training, developing and refining the AI models, or in the process of checking its conclusions before delivering feedback.

With a combination of human review and machine learning improvements, the 80% accuracy figure can be improved to 85-90% accuracy in around four weeks, at which point you can consider pointing the human resources to different tasks. For systems interactions, including chat, you would expect greater accuracy from the AI from the outset, as it immediately has controlled data to assess.

If you can achieve 95-100% accuracy, per Mojo CX’s claims, then you can be confident human resources are targeted to where they are needed most. It may even be that you are willing to accept a lower rate of accuracy if the QA benefits outweigh the wastage. This is a decision unique to your business. And so as with use case 1, it’s important to understand the true baseline that the AI is improving upon.

Elsewhere, you may choose not to assess 100% of calls for processing and ESG reasons. These are all tolerances and optimisations that you can test and set to deliver against competing KPIs.

Measuring Auto QA success

For any AI implementation, it’s important to measure its success as this will build the case for future implementations. Whether that’s headcount, resource allocation QA KPIs or any of the many other contact centre KPIs.

In summary, the benefits are:

· 75% reduction in QA processing time

· 50-100 x increase in evaluated interactions

· 15-25% increase in evaluation accuracy and consistency

· Greater and faster improvement in agent performance and CSAT

While undoubtedly a little more complex to implement than use case 1, implementing Auto QA builds on those foundations by making use of call transcription and taking it to the next level.

To find out more about how CCP can help you make the right technology choices, read more here or get in touch.

This series of articles is drawn from our webinar with Jimmy Hosang, CEO and co-founder at Mojo CX. We explored seven key use cases for AI in contact centres, starting from the easiest productivity gains to value generating applications. You can find a summary of all seven use cases here, or watch the webinar in full here.

AI in the contact centre is no longer a question of if, but where to begin. In our recent webinar with Jimmy Hosang, CEO and Co-founder of Mojo CX, we explored seven practical, high-impact AI use cases that are already delivering returns in real-world operations. From automating wrap-up notes to exploring full voice AI, the conversation cut through the hype to focus on what’s truly working – and what’s coming next.

From productivity savings – and easy wins – to value generation, here we summarise each of the seven use cases, their benefits, pitfalls, and what it takes to make them work.

1. Autowrap / Call Summarisation

This is one of the most immediate and measurable wins for AI – and it’s relevant to every contact centre, whether procedural or regulatory. With AI transcribing and summarising calls, wrap time is reduced by 50% and average handling times by 5–15%. In a 200-seat contact centre, at 10%, that’s equivalent to freeing up 20 full-time agents.

It’s an easy sell for operations leaders: the 2-3X ROI is immediate, the data is clean (and doesn’t need complex integrations, a simple copy/paste will do to start), and the impact on agent workload is obvious. What you do with the benefit is up to you; save the 20 FTE through natural attrition, reduce wait times, improve service. Less typing, less admin, more time for real conversations.

2. Auto QA (Quality Assurance)

Manual QA processes typically only cover 1-2% of calls. With AI-powered auto QA, every conversation can be transcribed and assessed, increasing both coverage and scorecard accuracy, with the potential to reduce QA overhead by 75%. Once the model reaches high accuracy (which can be achieved in four weeks or less), it enables a rethinking of QA resourcing. Where teams can reinvest those hours into value-adding activities like deep-dive analysis or real-time speech insights.

What’s more evaluation consistency is likely to see an immediate uplift, as is agent performance through real time feedback.

3. Auto Coaching

Team leaders spend 60-80% of their buried in fragmented data or playing detective to understand performance issues. Auto coaching can bring together call data, performance stats, and behavioural insights into one view – streamlining prep time and allowing leaders to focus on actual coaching.

From an efficiency perspective, this facilitates a shift in manager-to-agent ratios from 1:12 or 1:15 to something closer to 1:18 without losing effectiveness. But beyond that, coaching quality and consistency improve and agent development is more pointed and expedited. It also unlocks the potential for automated role play both on the job and in grad bays. This provides the basis then for both greater job satisfaction among both managers and agents, as well as delivering higher quality interactions throughout the operation. All of which have an impact on broader measures such as agent attrition, CSAT and brand perception.

SIDE NOTE: While those first three use cases focus a lot on the potential for reduction in headcount, it’s often more about doing better work, not just less work. Think: HITL (Human in the Loop), not human out of the picture.

4. Identifying Vulnerable Customers

This is where AI starts playing a key role in risk management and regulatory compliance. Agents can’t always be relied upon to spot vulnerability signals in real time – especially when they’re under pressure to do many things at one in a short space of time. AI can listen in and flag when it detects signs of vulnerability, alerting the agent in the moment and ensuring the right customer journey is followed.

The benefit? Reduced regulatory risk, better outcomes for vulnerable customers, and more confidence in compliance reporting. This use case also pairs naturally with summarisation – capturing the right context and actions in the CRM.

5. Agent Assist

Beyond risk management and efficiency, AI also enables agents to add value in the moment. Agent Assist tools analyse the live conversation and suggest actions – whether it’s handling a low-value enquiry quickly, spotting a sales opportunity, or guiding a customer toward a better outcome.

This is where things get exciting. AI is no longer just reducing cost – it’s helping unlock customer lifetime value and improving journeys. It’s also a mindset shift: from cost centre to value driver.

SIDE NOTE: The constant push for self-serve may well be eroding brand loyalty, where a great conversation with an agent isn’t only about making a sale or solving a query, it’s an experience that plays into customer brand perception.

6. Hands-Free Conversations

Imagine an agent who doesn’t have to type, click around systems, or juggle tabs – just talk and listen. That’s the promise of hands-free conversations. With AI handling navigation, form filling, and admin tasks, agents can give customers their full attention.

It’s not just about productivity, it’s about truly human interactions that focus solely on the customer. How satisfying would that be? It could change the type of people you hire and shift expectations around what great service looks like.

7. Full Voice AI

Everyone’s chasing the holy grail: fully autonomous AI voice agents. Why? 24/7 customer contact, instant routing, and scalable service without scaling headcount.

But Jimmy’s message was clear – don’t rush it, though do keep your eyes on the prize. Build your maturity and path to value with easier use cases, underpinned by the right data and processes. This isn’t about flipping a switch – it’s about a journey to transformation.

Final Thoughts: Think “value first, tech second”

Across every use case, the AI you deploy is about outcomes. Whether that’s saving time and cost savings, improving job satisfaction or deepening customer relationships, AI only succeeds when it’s introduced with purpose.

Start small. Pick the use case with the clearest ROI. And don’t be afraid to move fast – but move smart.

In early February I attended the IP Integration “Spotlight” event at the Midland Hotel in Manchester where we were provided access to some great insights from the team and from Steve Morrell of ContactBabel, what follows are my thoughts and reflections arising:

Something around customer adoption of automated solutions has been playing on my mind, it often happens when I suggest someone talk to an automated bot solution so they can experience first-hand how far the technology has come, where it is going and what the real possibilities are.

Being in the CX world and having several partners on our network that have such solutions, I have a number saved to my phone, just for this type of conversation.  If I pull the phone from my pocket, find the number, dial it and hand it someone to have a conversation then I often feel that the “conversation” isn’t as free flowing as it should be.  Why? Well that is a great question.

I suppose it could be that for the past 15 years when contact centres have effectively forced customers to speak to automated voice response systems, we have typically limited customer so saying one word “listen to the following list of options and then say the option you would like” or “in a few words please say why you are calling today” so for years we’ve been saying ‘please speak to this automated system in a short staccato format’. Now, in a matter of a couple of years, some businesses are offering customers the opportunity to speak freely to their bots or automations, whilst others are still on the limited few words space. No wonder consumers get confused – and the acceptance and adoption of voice automation could well be held back as a result. 

Voice is here to stay?

The truth is that voice interactions are still our favoured route of contact as customers, when it comes to getting things done and obtaining reassurance that we’ve been heard.  Whilst the death of voice in contact centres has been forecast for the past 20 years, the reality remains that voice is here to stay, millennia of evolution cannot be undone so quickly. Data shared at our webinar on the State of the Customer Experience Market with David Rickard of Everest Group in November (article link) validated this, as their research highlighted that 72% of revenues amongst the outsource community were still coming from voice-based activity in 2023 when both agent supported voice and conversational AI driven interactions were considered.

The data shared in the room in Manchester by Steve Morrell of ContactBabel corroborated this view, with 64% of interactions being cited as voice in his forthcoming 2025 report. Also that we are so keen to ensure that we speak to someone that we will now wait in the longer queues that have been identified post pandemic and that we have accepted these as the norm.

So, as a human race we have a deep attachment to use of voice, however I’m still receiving articles daily which suggest otherwise – and ours is an industry which is based on employing people to talk to customers. We need to acknowledge that ‘the bots’ or automation is coming for our lunch, which according to an article in the New York Times on February 1^st it may however already be in a place to arrange someone to bring our lunch and where may that end?

An article by Kevin Roose details several tasks which he managed to complete using OpenAI’s Operator, a new AI agent in the week prior. Most of the tasks it did autonomously with minimal intervention. It met its brief of being an AI agent that uses the computer to accomplish valuable real-world tasks, without the need for supervision, to complete tasks in the background with a handoff back to the user to enter passwords or payment card details.  However, in Kevin’s article he talks of how it ordered lunch to be delivered to a colleague’s house and responded to LinkedIn messages well, up to the point where it started signing him up to attend webinars, amongst other tasks.  There were, however, several tasks where the automation struggled or needed an amount of reassurance or confirmations. Because of which he felt that it would have been faster to do the tasks himself, but acknowledged that the AI agent is at an early stage of development.

What we do know is that the evolution of technology is only gaining pace. Peter Diamandis, founder of the XPRIZE (https://www.xprize.org/) , is cited as having said in 2020 that “the next 10 years will bring more progress than the last 100 years” Given the pace of change in the past 5 years, it is reasonable to assume that Moore’s Law will hold true in this instance –  and that we need to be ready for this.

As humans we like voice, we choose voice. But if personal assistants in the form of OpenAI’s Operator or DeepSeek were to be adopted by the general public (your customers) to complete their home admin tasks, then these systems won’t have the same emotional connection to voice conversations and will be happy to interact directly with a company bot. However, how quickly will we reach that point?

Public adoption is key then?

We can implement the best solutions in the world, but if nobody uses them, what use are they?

Whatever is coming next, we have a dependency on customers to embrace and use those solutions, whether that is voice automation in the contact centre or the potential for the eventual use of “their own” automation by customers to engage with brands to resolve issues.

We’ve seen before conversations around ‘brand by-pass’. Now, using an Alexa or alternative voice-activated AI assistant to complete simple tasks is clearly the gateway to us getting to a point of asking technology to, say, engage with our utility provider to amend our direct debit or to find a cheaper insurance renewal.  At this point we as individuals will have less input to what brands we choose to purchase, so then the brands that will succeed are those that are easiest for our automations to interact with.

But before we get to this utopian vision of admin free lives with our AI assistants ensuring the effective running of our homes and lives, we need to pass a point of public adoption of AI.

A 2023 report from Ipsos shows that 66% of people they surveyed globally expect that products and services using artificial intelligence will profoundly change their daily life in the next 3-5 years. Whilst this is the average, the range of responses on a country and demographic level vary considerably, with the proportion expressing this belief in South Korea as high as 82%, whilst France sees the lowest number agreeing with this sentiment at 51% (we in the UK see 58% agreeing with this statement).

So, whilst there is broad agreement that services using artificial intelligence will change our lives, what people are willing to adopt and how is a key consideration, acknowledging that some will be unable to adopt due to a variety of reasons.

The conversation at the Spotlight event therefore quite naturally centred on work that could be done to implement changes or applications of AI to better support the contact centre agents in delivering service efficiently without too much impact to the customer, generating a series of marginal gains which support the agent in resolving customer queries, potentially reducing call durations and in turn queues and repeat contacts –  a series of win/win scenarios which:

• Improve service
• Reduce pressure on the contact centre team
• Reduce repeat contacts
• Reduce the time customers spend trying to get through
• Reduce costs
• Improve staff wellbeing

Changes which fulfil the appetite of businesses to implement changes and leverage AI, but consider how willing customers are to adopt these changes.

Is some re-programming required?

If we want the possible AI solutions to be successful, we will have to consider how we guide customers to use these solutions most effectively. Our industry has created a sub-optimal situation through a combination of poor customer experiences in the past, limited system capabilities and a “tell me in three words” approach. If we want customers to embrace the possibilities of technology, then we need to bring them on the journey.

Consider how self-serve check-outs have become the norm when we are out shopping in recent years . There is a journey that I’ve certainly been on to this point, which I discussed with IPI’s Sam Grant at lunch.

Coming prepared, we need our customers to come to the contact prepared to engage with AI.

Similarly, from prior experiences I soon learned that I need to stop putting my shopping bag in the bottom of my basket, then putting my items of purchase on top of it, which created friction in the process when I needed to get to my bag to enable me to pack items as I scanned them.  So, ideally, we need our customers to come to the contact prepared to engage with AI (unless they don’t want to?)

Offering a choice? Do I want to self-serve or would I prefer to queue?

When I’m approaching the tills, I can see a queue for a till with a cashier or I can see available self-serve checkouts. If I can also see someone there by the self-serve tills to support me, then I can make an informed decision.

Unexpected item in bagging area! Solutions need to be flexible enough to minimise friction.

That bag I just dug out from my basket, I’ve tapped that I’ve brought my own bag, but it is perhaps heavier than the scales expect, therefore I’ve got an unexpected item. I’m removing and resetting the bag but there is a red flashing light and now I’m waiting for someone to come help me.  We’ve all been there (please tell me this wasn’t just me!). The solution has now evolved, though, replacing scales either with additional trust by the retailer, or with cameras, but the result is a smoother customer experience.

Authorisation for purchase There will be times when someone must step in. If so, ensure it is done in a timely fashion.

OK I bought wine, it’s the weekend, please don’t judge me. The process to verify that I’m of age and can make that purchase has parallels also. We need to ensure that if a customer needs support then it is quickly available. Now I want those annoying flashing lights to flash brighter, because   I need help to complete my purchase.

How do you want to pay? Payments need to be frictionless, tap and go, no creased banknotes!

The same will apply to your callers they need to be able to make the payment without being moved to another channel and of course you need to ensure you are properly protecting that payment data.

Do you require a receipt? perhaps we need to acknowledge that customers will want validation of their conversation, of what was committed to and that they can trust that it will be done.

It has taken me a long time to reach the point of clicking no to a paper receipt. I want to be able to evidence that I’ve paid and not just walked round the shop popping things in my bag. Part of the reason so many of us are still reverting to speaking to a human when we have an issue, other than our lived experiences of trying to explain a complex situation in 3-word blocks, has to be that we can say “I talked to …. And he said he’d sorted it”.

What does it all mean?

People are complex. The implementation of self service and automation of the simpler query types means that average contact centre conversations are now much longer than they were and with rising staff costs there is a clear pressure on businesses to make changes to reduce customer servicing costs.

There is a broad spectrum of solutions available to support businesses address these challenges, whether outsource or technology. These need to be properly aligned to your objectives, and it is likely that you may need to speak with someone around how to select, prioritise and deploy these solutions.

If you need to chat then feel free to drop us a line.

As part of our recent webinar with Zoom, we discussed how a brand is far more than just a name or a product; it’s the sum of what the public thinks, feels, and believes about a business. It’s built on both tangible elements like product features and packaging, and intangible ones like emotional connections, marketing, and even independent conversations beyond a brand’s control. Delivering on the brand promise—a commitment to customers about what they can expect—is therefore paramount to success. But when businesses fail to deliver, the consequences are costly and far-reaching.

Businesses increasingly turn to outsourcing partners to support customer service and contact centre operations. However, ensuring these partners can uphold the brand promise is critical. By exploring the importance of a brand promise, the risks of failure, and the value of the right outsourcing partner, organisations can better position themselves for success.

What is a Brand Promise, and why does it matter?

A brand promise communicates the essence of a company’s mission, values, and purpose. It represents what customers should expect when interacting with the business. For example, Red Bull’s brand promise encapsulates the idea of “freedom” and giving “wiiings” to people and ideas. They successfully integrate this into their sponsorships of extreme sports and events, translating their values into tangible experiences that reinforce their mission.

Delivering on this promise consistently builds trust, fosters advocacy, and encourages loyalty. Customers who feel a brand aligns with their expectations and values are more likely to:

• Pay a price premium for products and services.
• Recommend the brand to others, driving organic growth.
• Maintain long-term relationships, increasing customer lifetime value.

The cost of failing to deliver on the Brand Promise

When businesses fail to meet expectations, trust is eroded. Research reveals that 31% of customers are willing to pay more for excellent service, but failure to deliver service quality results in significant revenue loss. Poor service costs UK businesses an estimated £7.3 billion per month in employee time spent resolving issues. Additional consequences of falling short on service delivery include:

• Damaged Reputation: Dissatisfied customers share their negative experiences online, influencing potential buyers before they even engage with the brand.
• Increased Marketing Costs: Companies must invest heavily to rebuild trust and mitigate reputational damage.
• Lower Customer Lifetime Value: Customers experiencing poor service are unlikely to return, reducing their overall spending potential.

Service delivery directly underpins the price premium brands can command. Without great service, even the best product offerings lose their appeal—and profitability.

Managing customer experience at scale

The challenge for brands lies in scaling customer experiences while maintaining human, natural, and supportive interactions. Customers expect more than just advanced technology; they demand seamless, elegant, and intuitive service that delivers the right information at the right time. Poor customer satisfaction—as seen in the UK Customer Satisfaction Index, which recently dropped to its lowest point since 2015—reflects the critical need for investment in experience.

To understand how service impacts decision-making, organisations should explore:

• Price Premium Expectations: How much more are customers willing to pay for exceptional service?
• Perceptions of Good Service: What defines great service from a customer’s perspective?
• Service’s Influence on Purchasing Decisions: How does a seamless experience drive loyalty and sales?

Leveraging outsourcing to deliver consistent experiences

Outsourcing has been a transformative tool for businesses over the past 40 years, enabling growth, transformation, and improved customer service outcomes. To realise these benefits, organisations must select their outsourcing partners carefully, considering solution fit, commercial alignment, and cultural compatibility.

1. Solution Alignment: The partner’s solution must match the company’s specific needs, including sector expertise, channel coverage, geography, and appetite for automation. Proven experience with similar challenges can offer peace of mind.
2. Commercial Mechanisms: The cost of service should account for the entire support structure—not just front-line agents—to ensure scalability and sustained quality. Contracts should incentivise mutual success and allow for evolving requirements over time.
3. Cultural Fit: Partners must embody the company’s values and approach, representing the brand authentically to customers. Building a genuine partnership requires mutual respect and clear processes for engagement.

Mitigating outsourcing risks

To minimise risk, businesses must define clear objectives, success measures, and realistic timelines before outsourcing. Processes should be fully documented, and knowledge transfer planned meticulously to ensure a smooth transition. Continuous communication with the outsourcing partner is essential for alignment.

Outsourcing also enables access to specialised skills, flexible scaling, and cost efficiencies, all of which support business growth without overextending internal resources. The key is selecting a partner who acts as an extension of the organisation’s team—not just a supplier.

Conclusion

Delivering on the brand promise is a strategic imperative that builds trust, drives loyalty, and sustains growth. Poor service is not just an operational issue but a risk to brand value and viability. Businesses that prioritise exceptional customer experiences can protect and enhance their reputations, achieving sustainable success.

Outsourcing, when approached thoughtfully, can be a powerful enabler of these outcomes. By choosing the right partner and fostering a collaborative relationship, organisations can mitigate risks, enhance service quality, and uphold their brand promises with confidence.

There are several universal truths, one of which is that we all have at least one subscription! Though I think that if we were asked to list all the things we pay a monthly or annual fee for we would probably come across some we’d forgotten about. We questioned how many subscriptions we have that you may not feel we’re getting value from?

Another consideration is that even if we’ve not been using our Netflix, Disney+, AppleTV, or whichever service one as much as we’d like, we may be  holding onto the knowledge that we will likely binge some boxsets over the festive period and how many of us then realise we are all subscribed to Amazon Prime and other subscriptions may have been unnecessary.

A third is that we are often encouraged to review our discretionary expenditure in January and cancel any that we don’t need or to look for a better deal.

It is always good to speak with experts in a field to understand how these elements all play out, Jonathan West is Client Development Director at Step Change Outsourcing and knows only too well the first-hand challenges of a subscription-based business model having led the Sky Business Division as National Sales Manager and Head of Indirect (Consumer) Channel at Three.  Simon Kissane is highly experienced in delivering CX and Contact Centre Performance Improvement having supported a number of interim positions and extensive experience as a Head of CX and Operations in the mobile and broadband space.

What does the data tell us?

Data from Barclaycard in 2020, Whistl in 2022 and from Statista suggest that on average a UK household has 8 subscriptions ranging from streaming services to food kits, healthcare and pet products.

Finder.com suggests 2023 research shows 79% of UK adults (42 million of us) have at least one subscription service. However 23% of us feel  these services are too expensive and 51% would be willing to cut that subscription to save money. Some research data which claims that we are spending an average of £500 each annually seems to focus on streaming services.

Further subscriptions which saw significant growth during the pandemic were the subscription box services. Whistl report an 18.9% year on year growth in in in the UK in their reports and referencing data from the Royal Mail in stating the market will be worth £1.8bn in 2025.

Whilst a little dated, the Whistl report shares some insights around key metrics for subscription boxes, their data suggests,

• 81% of households have at least one box subscription
• average spend of £52 per month in 2021 with annual spend to £620

Those subscriptions typically last 9 months

• 40% of us subscribe for convenience and 55% to save time
• 74% wish that companies made it easier to manage subscriptions

Clearly the different types of subscription are driven by differing motivations. Time and convenience are a key element, howeverthe value of the subscription is a vital consideration, too.

Data from the Department for Business & Trade, published in April 2023 (based on research with 2,000 UK adults conducted by Opinium Research in November 2021) showed the following level of subscription holdings:

Respondents were asked the critical question as to their expected behaviour if prices were to increase and unsurprisingly, they were more likely to consider cancellation as listed below by subscription type (key sectors):

• 79% Food & Drink
• 76% Digital fitness and wellbeing
• 73% Health and beauty
• 66% Flowers, craft, chocolates and treats
• 64% Entertainment and books
• 64% Product delivery services
• 38% Charitable donations

Subscriptions to telephone and broadband services don’t seem to appear in the data. Perhaps they  have ‘crossed the line’ into the utilities space? Digital connectivity may have become a physiological need, in the words of Maslow even. This seems  fair considering that we all need data connectivity to live our day to day lives today.

But if we consider for a moment the broadband and fixed landline space, the regulator Ofcom has been busy making changes in the past few months that could impact the sector.  On this basis, have sensitivities around price and service ever been so important to that sector – and are there considerations that can be applied across all subscription markets?

So, what does that mean to the customer contact community?

Well, there is a chance that a broken process earlier in the year – which made your contact centre hard to deal with – is going to result in customer retention issues when that customer reaches the end of their contract.

Or that automation process that you are thinking of implementing due to pressure from the business to reduce operational costs needs to be just right, or else it may result in driving customers not only to self-service, but away from your business altogether.

It could mean that you have an amount of retention work to do, or that you need to start thinking about additional marketing spend next year to attract new customers to maintain your numbers, never mind growing the customer base.

For many customers managing, their relationships digitally – like with NOW TV, for instance – should be easy. But my personal experience of trying to cancel a NOW subscription over the weekend was time-consuming and frustrating:

• Cancellation was not possible via the App which means logging onto my account,
• then being asked no fewer than 6 times whether I really wanted to cancel,
• and being presented with offers and discounts to retain me.

This feels like an example of when an understandable business desire to create a bit of friction has gone too far, turning off customers from coming back in the future.

However, the first that businesses with digital based relationships may know of my intent to cancel is when I’ve clicked on a box and my money stops the following month. Many will then commence an e-mail campaign, or outbound calling perhaps, to ‘win-back’.

One touch switching

The implementation of the Ofcom rules on one touch switching from September 12^th enabled customers to move to a new provider with just one contact. This means that alternative network providers (alt-nets) – despite huge investment in their infrastructure – are now in a place where customers can simply walk away without having to contact them, similar to my cancellation of my NOW TV subscription.

The new provider manages the switching process and the incumbent has little option but to go with it. Are the developing the expectations that customers will have about the ease of cancelling their telco and broadband subscriptions being mirrored across other services?

Additional considerations

In our discussion, Simon, Jonathan and I also considered whether there is a clear role for NPS in customer retention and operational performance. This was a topic which was discussed also on a Scorebuddy webinar that I supported, recently. With the level of insight available from the contact centre, do you really still need to ask the question around likelihood to recommend a product or/service? It could be that you can see this through all the other data and insights at your disposal. However, you need to ensure that you have the time and knowledge to implement the changes needed, which is where businesses can fall short.

When it comes to growing any form of subscription business, there is a clear need to balance acquisition with the realities of ongoing customer service. The work of retention and win-back teams should not be underestimated, but  if you get the customer service right then retention is less likely to be needed.

Scaling a business to cope with customer demands can be challenging. The transition from small in-house operations with wider departments helping where they can in supporting customer needs in those moments of truth (when something hasn’t been delivered as promised) can take people away from their roles in the wider business and risk future growth ambitions.  Where customers have bought/subscribed via a click then the first time your team speak with them could be at the point of disconnection and considering the costs of developing your business or network, there is a need to maximise customer lifetime value.

When growing a business and a contact centre team, you need to ensure that you are properly supporting and developing your staff. As businesses grow it is not just customers numbers where retention may become a challenge. With Simon and Jonathan I discussed these challenges around recruitment and training, this is where we have seen outsourcers taking the load, so that “you do you and let the outsourcer do the heavy lifting”.

Is your customer contact approach fit for purpose?

With the potential challenges of growth and increasing costs in 2025 from minimum wage and national insurance increases ahead, maybe it is time to review where you are on your journey and whether there are opportunities to optimise current operations – through either process review or implementation of new contact centre technology?

Perhaps you’ve reached a point where you need additional support from an outsource partner who has walked this path before and can help you grow customer numbers and/or evolve to the next stage of your growth, whether that is with

• acquisition activity
• out of hours support,
• peak capacity or
• end to end customer service which allows you to focus on your core activity and growing your business.

You may have an existing operation that requires review, but whatever your customer contact challenge feel free to contact us so we can talk it through.

At Customer Contact Panel we have extensive experience in supporting our clients in identifying the right fit solution for their business.

When you assemble a room of people with extensive levels of contact centre experience, as we did for our event hosted at Sutherland Labs, you know from the noise levels over coffee there are going to be some great conversations! Add some fantastic speakers from our outsource and technology networks to share their views of the market and a lively, open dialogue around challenges and opportunities (new and old) will follow.

We are looking forward to continuing these conversations and scheduling another event.  But in the meantime, how do we bring so much collective experience together in a short article that does justice to the quality of the conversations?

Navigating Business Decisions in a Rapidly Evolving Landscape

In the current environment, companies face a range of critical decisions, from implementing new technologies to fostering employee engagement. Despite knowing what needs to be done, many organisations struggle to translate that knowledge into actionable outcomes. This disconnect is often a result of inadequate systems, outdated training and coaching models, and an inability to adapt to change. 

In our recent L&D survey it was apparent that there is a clear gap between knowing and doing.  Results show that while employees understand their roles, there’s a significant disconnect between knowledge and execution. This is particularly evident in how businesses approach training, often relying on outdated, “once-and-done” programmes that fail to evolve alongside the changing work environment. As companies shift to remote work, many are noticing a reduction in employee loyalty and engagement, partially because of the lack of in-person interaction and relationship-building.

Addressing the Changing Needs: Evolving Training and Technology

To bridge this gap, organisations must rethink how they train their employees, particularly if they are to continue with a work from home or hybrid working model. Has enough been done to redesign training and refresher modules that better fit a virtual environment? Equally, more needs to be done to focus on continuous education rather than static, one-time courses which tick a box for compliance. Furthermore, conversational AI can be a powerful tool in reshaping learning; allowing employees to ask dynamic, evolving questions rather than relying on predefined solutions.

AI offers the potential to unlock the true capabilities of people and data, but as we have said before is not a silver bullet. It can revolutionise business processes by supporting employees in their roles, reducing friction, and enhancing decision-making. AI can also help agents manage customer queries more efficiently, giving them access to foundational knowledge in real-time. However, the challenge lies in positioning AI correctly: not as a threat to jobs, but as a tool for augmenting human capabilities.

For example, AI’s ability to analyse customer intent and apply insights to guide agents through complex interactions can dramatically improve customer experience (CX). By properly integrating AI into business workflows, companies could potentially resolve the eternal challenge of moving from being seen as a cost centre to profit centre, unlocking new value opportunities across the customer journey.

Location strategy is still a consideration as the global market evolves. The outsourcing industry, particularly in sectors like fintech, IT support, and healthcare, appears poised for significant growth.  We know countries such as South Africa have already emerged as strategic hubs for business services, offering talent and capabilities that align with the growing demand for multilingual and technologically adept service providers. Whilst there are valid concerns as to the capacity that remains available, with 33% unemployment in South Africa (60% for young people) as well as the wider continent opening for business, then combined with the capabilities of technology great opportunities remain available.

Overcoming Challenges in AI Adoption

While AI presents numerous opportunities, it also comes with significant challenges. Many process owners may be hesitant to adopt AI due to concerns about how it will impact their workforce and customer relationships. Meanwhile, senior leadership may be focused more heavily on the potential cost saving benefits.  There’s a widespread misconception that AI will replace jobs, particularly in customer service. However, AI’s true value lies in assisting and enhancing human roles, not replacing them.

For businesses to adopt AI successfully, they need to:

• Align AI with company goals and culture: AI should be seen not as a technology investment, but as a strategic asset that drives both customer and employee experience.
• Shift from a cost-saving mindset to a value-driven approach: Technology shouldn’t be about cutting costs; it should unlock value, address problems at their root cause and improve service quality.
• Build the right business case: Secure buy-in from different budget owners by emphasising how AI can enhance outcomes across the organisation.

Aligning Metrics and Culture for the AI-Driven World

To fully leverage AI’s potential, cultural and operational changes are required. Business leaders need to:

• Align metrics with an automated world: Ensure that technology handles routine tasks, allowing people to focus on complex, human-centric work.
• Redefine the agent role: The agents of the future will need to deliver more value and possess different skills compared to traditional customer service roles.
• Foster a culture of continuous improvement: Embrace ongoing evolution, where AI serves to complement human skills and free up time for higher-value tasks.
• Focus on proactive engagement: Let technology handle the repetitive, allowing people to engage with customers in a more meaningful way.
• Encourage bravery in decision-making: Leaders must support bold decisions around AI investment to drive long-term success.

Irrespective of delivery location, the future of CX delivery will increasingly rely on AI and automation to enhance customer journeys, optimise operations, and drive sustainable growth.

Transformation is never-ending. Businesses must approach AI and automation not as one-time projects but as ongoing evolutions. This requires understanding the unique challenges they face, aligning technology with business goals, and ensuring that AI enhances rather than replaces the human element.

With the right strategy, AI can unlock unprecedented opportunities for growth, helping companies stay competitive in a rapidly changing world. However, without the appropriate attention to employee experience, success will be illusory or limited.

Delivering the right experiences

At Customer Contact Panel we support organisations in delivering contact centres that match their ambitions. In a Deloitte Digital research articles from May 2024 it was cited that 55% of contact centre leaders reported that they didn’t meet their strategic goals in 2023 and 76% reported that their agents were overwhelmed by systems and information*.

If you are facing challenges meeting your strategic goals or fulfilling the ambitions you have for your people, customers or technology, we have the experience to support you. Just ask. 

*Balancing priorities in a new era of contact center transformation – Deloitte Digital