The health & wellbeing sector has always been rooted in human connection. Whether it’s supporting someone on their fitness journey, guiding a patient through treatment, or reassuring a customer about a sensitive health concern, the role of empathy is central.

But as the industry expands fuelled by digital-first healthtech, growing demand for wellness subscriptions, and rising consumer expectations, customer contact teams are under strain. The question for leaders is clear: how can we scale, stay compliant, and still deliver a deeply human experience?

From Cost Centre to Care Hub

Contact centres in health & wellbeing have traditionally been seen as a cost to control. Yet every conversation from a dietary query to a mental health support call has the potential to strengthen or weaken customer trust.

Forward-looking organisations are reframing service operations as a growth driver. For example, brands in this sector are exploring:
– Streamlined renewals and cancellations to reduce friction in subscription journeys.
– Smart routing for repeat callers, ensuring recurring issues are addressed quickly.
– Consistent omnichannel service so customers feel supported whether they call, chat, or message via an app.

When the contact centre is positioned as a core part of the brand experience, it moves beyond cost reduction and becomes a foundation for loyalty.

Automation with Empathy

The volume of routine contacts in this sector is significant – booking appointments, tracking deliveries, resetting passwords, updating payment details. These are tasks that can be handled by AI and digital workers, delivering instant, 24/7 responses.

The real opportunity is in blending automation with human empathy:
– Real-time agent assistance: AI surfaces the right knowledge at the right moment, helping advisors answer health or wellbeing queries accurately and sensitively.
– Vulnerability detection: AI can flag signs of distress in a caller’s tone or language, prompting the advisor to adapt their approach or escalate where appropriate.
– Conversation wrap-up & QA: Every interaction can be automatically summarised, with 100% of calls checked for compliance and quality, giving leaders confidence that standards are met consistently.

This partnership between people and technology doesn’t replace the human connection. It amplifies it, giving advisors the space to focus on empathy while automation handles repetitive, time-consuming tasks.

Scaling Securely & Sustainably

The growth in health & wellbeing services from digital fitness programmes to home diagnostics demands agile operating models. Customer demand can spike rapidly, whether during seasonal health peaks or major product launches.

To stay ahead, organisations are:
– Leveraging flexible sourcing models (nearshore, offshore, hybrid) to expand capacity quickly and cost-effectively.
– Adopting workforce management (WFM) tools to optimise scheduling and keep wait times short.
– Embedding compliance and security (PCI DSS, GDPR, sector-specific regulations) to ensure every interaction is safe and brand-protective.

By combining these capabilities, health & wellbeing brands can scale without losing sight of what matters most: trust, care, and the customer’s wellbeing journey.

The Strategic Shift Ahead

The contact centre is no longer just a helpdesk. It is becoming the front line of wellbeing experiences, where automation drives efficiency, and skilled advisors deliver empathy. For leaders in the sector, the challenge (and the opportunity) is to design operations that are both sustainable and human-centred.

At Customer Contact Panel, we connect organisations with over 220 delivery providers and 115 technology partners. We help health & wellbeing brands navigate their options, align technology with their customer journeys, and build resilient, customer-first operations fit for the decade ahead.

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.

Of all the contact centre use cases for AI, Pure Voice AI is the most disruptive – and potentially the most transformative. Unlike Agent Assist or auto-wrap that augment human performance, Pure Voice AI replaces the agent entirely for certain interactions.

What is the AI doing in Pure Voice AI?

Pure Voice AI uses fully autonomous AI agents capable of holding spoken conversations with customers—with no human agent in the conversation. For an inbound call, the AI could triage the call, and if it can deal with the interaction itself, it doesn’t need to trouble a human agent. If the enquiry does need a human agent, it can monitor who’s available and route the call to the next best available agent.

Ultimately, the idea is that these AI agents can answer questions, resolve issues, and even handle sensitive interactions such as payment disputes or appointment scheduling.

It’s far more sophisticated than IVR (interactive voice response) trees or chatbots. Pure Voice AI uses advanced natural language understanding, real-time decisioning, and speech synthesis to hold dynamic, human-like conversations.

Key benefits: 24/7 service

The benefits case here is far less about cost reductions, agent productivity gains and optimisations, as use cases 1-6 have already delivered well here.

It is far more about providing round the clock service and enhancing brand experience. Because we all have lives, and work, that mean calling between set hours can sometimes be difficult. But the reason many contact centres are not 24/7 with human agents is because the business case of the cost and overheads – from staff costs to heating and lighting – doesn’t stack up.

Smoothing demand

Not only is calling at set times difficult, it creates spikes in demand, for example around lunch time or just after work. What’s more, pro-active outbound calls can also be scheduled for more customer friendly times of day.

Multi-lingual cover

Where a contact centre needs to serve multiple languages, there is typically a primary language that most human agents speak, with a handful of specialists available for secondary languages. Which means that those secondary languages are a scarce resource, both in terms of availability and recruitment. With Pure Voice AI in the mix, it can detect the language being spoken and switch seamlessly into it.

Implementation considerations

While everyone is trying to rush to this use case, without computer use, proper integrations, optimised and redesigned processes, there is no real opportunity to leap-frog to full voice AI. Because the foundations are simply not in place to support it.

What can we expect to see?

While not quite there yet, it is just around the corner, and there will undoubtedly be a proliferation of pure voice AI, especially for outbound. Though businesses should expect regulation to swiftly follow.

As we await the true potential of Pure Voice AI, it is a case of charting a path to how you achieve this in future, not the focus for today. Down that road lies complexity, risk and far greater likelihood of project failure. When you could be realising value right now and incrementally from use cases that build the maturity on which to develop pure voice AI. A far safer path to value on every front.

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.

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.

As self-serve improves, agents are increasingly left with the most complex queries.

Of course, it’s not always the case as many customers continue to prefer conversations over digital interactions, or are even digitally excluded, but for the most part, the expectation is that agents deal with call after call still, but with less and less respite where the query is nice and straightforward. And complex queries often involve multiple systems or multiple previous contacts, as they aim to follow the story. Which, if we go back to use case 1 – autowrap, we know our corporate memory can be patchy.

Yet customer expectation is that all of the necessary information is at the agent’s fingertips. And why shouldn’t they think that? Customers are often time poor and a bit frustrated already, so a longer than necessary call that may or may not answer their enquiry simply compounds their frustration.

What is the AI doing in Agent Assist?

As with all use cases to date, the AI is listening to calls to summarise key points. In Agent Assist – or ‘conversational guidance’, it is also retrieving and analysing previous data, so that, as with use cases such as auto coaching, it can make suggestions and guide processes during the customer interaction. It is, in effect, accessing and presenting the corporate memory that customers expect, but with bells on as it is also providing direction to the agent.

Key Benefits of Agent Assist: Assess customer needs

From the outset, the AI can help an agent understand what type of call they’re dealing with. Is this a customer with a quick query who is in a hurry, so it needs to be efficient, or do they have more complex needs that need to be addressed? Or even, is there an opportunity to cross- or up-sell to this customer?

Not only does this help to direct the nature of the call, it has an obvious impact on how the brand is perceived and even on topline revenue if it is possible to make a sale. Equally, it can prevent a clumsy attempt at a sale at the wrong moment, which could denude rather than enhance customer lifetime value.

This is also a key consideration in the push to self-serve, as customer experience becomes less personal and less represented by the people of the organisation. While a poor customer experience on a call can erode brand value, a great one can build far more than pure self-serve experiences.

Lower cognitive overload

In the context of agents needing to take more calls that are more complex, fatigue and cognitive overload is real. So while the last 15 years have been about focusing more on natural conversations and active listening, in a high-pressure, high-volume environment, doing that on every call is intense.

Of course, some agents may enjoy the need to think on their feet more than others, and therefore may be the ones who are trickier when it comes to adoption, but there are times when we all need a break from the mental strain of 100% concentration.

Optimise handling time

Agent assist can help to optimise handling time by keeping the agent on track, and reducing the amount of time spent unnecessarily building rapport. Of course some rapport is good, but if overdone, it can be confusing for the customer and result in repeat calls to resolve their actual query, rather than have a nice chat.

Accelerating the development of new starters

Dynamic conversational guidance allows new starters or less experienced agents to fly solo faster. They need to refer less to their supervisors for guidance (which in itself interrupts the flow of the call) and build confidence more quickly.

All of which translates to an increase in customer satisfaction from the use of corporate memory to deliver a better experience and faster call handling with responses that are right first time.

Implementation Considerations: Agent behaviour change

Aside from non-negotiables, such as good data (an absolute pre-requisite here) and systems integration, a key consideration for Agent Assist is to understand that it requires a much greater change in agent behaviour than the uses cases to date. Because you are in effect re-engineering conversations. So while it can accelerate the performance of a new starter, a longer tenured agent may be more ‘stuck in their ways’.

That means it typically takes 8 to 16 weeks to realise all the benefits of agent assist, possibly more if everyone is remote rather than office based. However, a two-to-four-month window to embed such change is both a really short time in the grand scheme of things and a small price to pay for the benefits available. Even considering that there will be a degree of things being a little slower in the first instance as you go through the J-curve of implementation.

However, almost all those aiming to realise AI benefits are jumping straight to this use case. Whereas they could prove the case for AI on much quicker and easier wins that are less likely to fail. What’s more, if those use cases have been implemented first, they lay the foundations for agent assist, and make both implementation and adoption easier and faster too. Without having proved the case for AI to the humans in the equation, adoption is slower, if accepted at all, and the benefits won’t come.

Knowledge base quality

Secondly, jumping too quickly to pure LLM Agent Assist and simply connecting to a knowledge base of historic conversations, then presenting information based on that alone as guidance, is a dangerous place to be. Because that assumes that all previous conversations were exactly as you wanted them, and not littered with the conversations of poorly trained or poor performing agents. Mojo’s advice here is to use the LLM to read in your script and build out the flow for that script and the associated dynamic pop ups. And it is essential that the knowledge base is part of continuous improvement too.

Measuring Success

There are some obvious key metrics that will be impacted by Agent Assist, from AHT and FCR to CSAT. More broadly, agent progression and job satisfaction can be measured through agent feedback, and customer lifetime value through customer analytics.

In summary, the key benefits are:

Increased agent satisfaction through reduced stress and increased performance
Increased customer satisfaction through faster and more accurate call handling
Reduced average handling time through more pointed conversations
Increased FCR through more accurate assessment and solutions
Improve opportunity spotting for x-sell and up-sell and guide to a sale
Improved customer lifetime value (CLV/LTV)

Done well, Agent Assist tools can enhance the capabilities of contact centre agents through intelligent support that enables agents to navigate complex queries or attempt to make sales with confidence. Which leads to more effective and satisfying customer interactions, greater customer lifetime value and even the potential to shift mindset from the contact centre as a cost centre to a value-driving profit centre.

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

In an article published in the run up to Christmas 2024, I discussed the growing tendency in many organisations of the traditional Christmas peak starting to diminish, due to a variety of factors. Musing on the question of whether we had “…passed peak peak” (see what I did, there?) I wondered whether a reduction in the scale of ‘peak seasons’ was being paralleled by a growth in other sorts of both structural and spontaneous increases in contacts.

I was reminded of this article – and the discussions with colleagues and partners that inspired it – when I came across what was a new term to me, last week. The phrase is ‘perma-peak’ – the concept of a long-running or perpetual series of periods of high contact demand.

If the old, usually Christmas-related, peak season was the product of a society-wide, predictable (but hard to handle!) patterns of behaviour, then a perma-peak reflects a very different world. One in which both consumers and brands exhibit or can leverage greater personalisation, but in which demand patterns can spread and grow very rapidly:

1. Brands now have the ability to test and flex proposition and pricing, with instantaneous digital communication to customers and prospects
2. Social network effects can massively amplify the features and virtues of an offer or product – be that from an ill-prepared start-up or a settled, established big player. And, of course, negative aspects or faults can also be shared and multiplied, resulting in a flood of questions, complaints or claims

Pattern non-recognition

Despite advances in analytics automation and machine learning / AI, understanding and responding to demand changes in real time remains difficult and rare. Contact centres have always been operationally flexible and able to display great agility, but this is typically still a manual, even instinctive, response.

When insights are predominantly based on pattern recognition, increasing unpredictability makes operational flexibility a consistent challenge.

Likewise, agent assist and self-service tools and guidance will largely be optimised on the basis of prior experience. Unexpected demand spikes might be driven by either new query types and failure demand triggers, or factors in a novel combination.

Planning for the unplannable

In traditionally ‘peaky’ business sectors like retail, peak planning is an intrinsic part of how businesses are run. If you have a Christmas peak then thinking about how to handle it the following year typically starts in January, before the backlog of post-Christmas complex queries and complaints has even gone! Planning for the unpredictable is a different challenge.

So, if we really are entering an era of perma-peaks, will contract centre operations find themselves back in the era of watching service level charts drop precipitously whilst wondering what on earth is going on?

Not necessarily.

Perma-peaks need perma-flex

Just because tried and tested techniques are strained or start to fail, doesn’t mean that they can’t be revisited, adapted and fine-tuned.

• Workforce Management may become more orientated around rapidly identifying the different characteristics of peaks and corralling real-time shift flexing – and less about aligning scheduled staffing to forecast demand
• Your attempts to match staffing to demand may be less focused on seasonal efforts with shift banking and the use of temporary or fixed-term contract workers, but instead looking at incentivising intra-day shift flexibility or the peak use of a ‘gig’ model, using either in-house or outsourced workers
• Customer guidance, contact steering and self-service options will need to be able to be changed and weighted immediately in the light of peaks or troughs in demand. Root causes of demand spikes need to be identified and addressed – including restricting the promotion of or access to certain contact channels for limited periods, if required
• ‘Unforced errors’ by which organisations inadvertently trigger customer contact need to be avoided. Experience shows that the best way of doing this is through ensuring the contact centre has ‘a seat at the table’ when initiatives and campaigns are being designed

Whether you think that your next peak could be with you at any moment – or you’re confident that it will kick in from the 2nd week of October, just like every year – how to do you anticipate, plan and prepare?

Let us know; we’d love to share experiences and ideas.

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.

Moving up the value chain of AI use cases, consistent and effective agent coaching is a vital to the performance of contact centres. From areas that are critical to risk management, such as in regulatory compliance, or value-driving in improving customer experience and brand perception.

Traditionally, coaching relies on the same small samples and manual evaluations as QA (per use case 2), which inevitably means observations are sporadic and opportunities for improvement missed.

What is the AI doing in Auto Coaching?

Auto Coaching harnesses artificial intelligence to analyse agent interactions. It ‘listens’ to the conversation to identify areas of strength and opportunities for development. These data-driven insights then inform an AI-generated, individual agent coaching plan. Providing their coach with the tools to cater to individual agent needs and foster continuous improvement.

Key benefits: Efficiency

When you consider that 60-80% of a team leaders’ time is spent gathering information (Mojo time and motion studies) from the likes of Excel or Power BI to knit together a story of performance – to bring together call data, performance stats and behavioural insights into one place – and deliver coaching sessions, it’s easy to see the benefit of AI taking on this task.

Not just in efficiency, where it is possible to shift from a 1:12 or 1:15 manager to agent ratio to closer to 1:18 without losing effectiveness, but increasing team leader job satisfaction. Where they feel that more of the work they do is making a difference. As with previous use cases, how you take this efficiency benefit is a choice. Either in headcount reduction, or in delivering more coaching – which in turn drives customer experience improvement, or redeploying resources to more strategic tasks elsewhere.

Coaching quality and consistency

What’s more, each coaching conversation will itself improve in quality, because the feedback is based on and prioritised by a much greater data sample both for individual agents and the agent population as a whole. It also ensures agents receive consistency in their feedback, not just in one-to-one manager/agent relationships, but again all managers across the whole contact centre are delivering the same messages on the same coaching points to improve the quality of interactions overall. Which means opportunities are no longer missed, and the opportunity cost diminishes, while also improving customer experience.

An example of this, particularly when integrated with other speech analytics and the QA scorecard of use case 2, could be for offshore contact centres, where the agents speak the language of their customers, but colloquialisms, dialect, accent, vocabulary, fluency, speech pacing or cultural differences result in misunderstandings or frustrations.

Personalised rapid development

With AI in the mix, you no longer need to wait to deliver coaching on specific issues. Or hope that you’ve picked up the key ones from the samples you have when reviewing manually, because the AI is dedicated to finding them on a daily basis. Meaning coaching points for individual agents can also be delivered in real-time, or near-real time depending upon implementation.

The consequence of this targeted, personalised rapid development is that team leaders are able to have the right coaching conversation in the right moment – or even that the agent can ‘self-coach’. Coaching becomes both more efficient and more effective. Agents develop more rapidly, picking up development points as they occur, not days later when it’s easier to have forgotten it (or perpetuated bad habits) and in bite sized pieces, making the feedback more digestible and memorable. Their job satisfaction is improved though faster progression and the business wins through better customer service, better selling or more impactful risk management.

Automated role play

A further step in the development of this use case is the potential for AI to synthesise customer calls for training at varying levels of complexity. Either to pick up systemic issues within the whole operation, or to pick up specific agent needs on the job, or as part of the grad bay, which can then itself also be automated in the analysis and scoring of agent responses, per use case 2.

Here we see real driving both efficiency and effectiveness throughout the contact centre. And again, by providing agents with confidence in a safe environment, other KPIs such as attrition can be positively impacted.

Implementation Considerations

As with all other AI use cases, integrations and data privacy are key considerations. But in this case, it’s important to consider your accuracy thresholds for the AI, and how you will test for accuracy so that team leaders are confident in the AI’s ability to deliver. Furthermore, you will need to educate team leaders and agents on how to use AI-generated feedback. Always think ‘Human-in-the-loop’ (HITL) to ensure coaching is still accompanied by all of the empathy necessary to make it successful.

Measuring Success

For this use case, consider monitoring agent metrics such as first-contact resolution, number of coaching points and CSAT as a measure of coaching effectiveness on a one-to-one basis, measures of coaching preparation time or manager/agent ratios as measures of effectiveness. Then more broadly consider agent retention rates as a measure of higher satisfaction and reduced turnover.

In summary, the key benefits are:

• Significant reduction of the 60-80% of time leader spent preparing coaching

• Shift of manager/agent ratio from c. 1:12 to 1:18

• Higher job satisfaction and reduced attrition among agents

• Higher job satisfaction among team leaders

• Improved CSAT and brand perceptions as service improves across the board

As we move up the value-chain, the AI does get more difficult to implement. However the payoff also tends to get bigger too. Auto Coaching can be considered a strategic investment in agent development, to foster a culture of continuous improvement, that leads to enhanced performance and customer satisfaction.

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

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.

Summarising calls takes time – anywhere from 10-30% of the call. And agents are almost always under pressure to get the task completed in as little time as is humanly possible to meet AHT and wait targets. This often translates to errors or even missing data. Which not only makes it hard for future agents to follow the story, it can be a regulatory challenge too.

AI-driven autowrap and summarisation tools are helping to alleviate this burden by automating the process, allowing businesses to cut handling times and improve CRM accuracy. According to Jimmy, it’s one of the easiest applications of AI a contact centre can implement.

What is the AI doing in autowrap?

Autowrap and summarisation technology uses natural language processing (NLP) and machine learning to transcribe customer calls in real time. As calls progress, key details such as issues raised, resolutions, and next steps are captured automatically. This eliminates the need for agents to manually document call details, both reducing errors and freeing up time for more customer-centric tasks.

Key Benefits: Time and Cost Savings

By reducing the time spent on manual transcription, businesses can lower wrap times by 50%, which translates to reducing handling times by 5-15%. For a contact centre with 200 agents, taking the mid-point of 10%, this could result in a reduction of up to 20 FTEs, and delivering a 2-3X ROI from day one.

How you take this benefit is then your choice:

a) A productivity gain, even through natural attrition

b) A service improvement by reducing wait times or improving service, with longer call times to allow for better first contact resolution

c) Reinvest in more value driving AI use cases to build maturity

Call Summary Accuracy

With manual transcription, there is always the risk of errors or omissions. AI-driven solutions eliminate these risks by automatically capturing the most relevant data from each conversation, improving both the consistency and quality of CRM records.

Increased accuracy has a number of benefits, whether you run a regulated business or not. First is in future contacts, whether you met a first contact resolution goal or not. Any future calls where a customer refers to a previous call – and reasonably expects there to be some level of ‘corporate memory’ – can be shortened by avoid any lengthy re-explanations of what has gone before. Not only does this provide a future productivity gain, it makes for a far better customer experience too. So even at use case 1, we are already facilitating value generation through slick customer processes that avoid typical customer frustrations, as well as productivity.

What’s more, the data is clean, reliable and available for future analysis and QA. Look out for an article on use case 2, Auto QA, for more on that subject.

When building a business case, these are important considerations; it’s important to remember that your baseline probably isn’t perfection. And so your quality uplift may be greater than you have otherwise anticipated.

Easy Integration: No Overhaul Required

While it is undeniably desirable to integrate Autowrap technology into CRM or policy admin systems, it’s not a pre-requisite to start making these gains. An agent – dubbed the ultimate API in our recent whitepaper– can easily check through the summary, make any necessary amendments if you require it (your benchmark of what is good enough will depend on your business) and copy and paste it in. They’re already used to connecting disparate systems and will be working where you want to capture it anyway.

This means that businesses can buck the trend of AI project failure and quickly adopt the technology with minimal disruption to existing workflows. Once the ‘short, sharp’ solution is working, of course you can consider and implement the deep integrations to automate the task, but you will be most of the way there without it.

Enhancing Agent Experience and Customer Outcomes

As alluded to earlier, the benefits aren’t just about reducing operational costs—they also enhance both the agent and customer experience. By automating mundane, and often poorly executed tasks like call transcription, agents are free to focus on more valuable work, such as problem-solving and building customer relationships.

This not only boosts job satisfaction – which in itself may then also translate to tenure, sickness and recruitment gains – it also contributes to higher-quality customer interactions. Look out for use case 5, ‘Agent Assist’ for more on this topic.

Measuring 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 or the gamut of other contact centre KPIs.

In summary, the benefits are:

1. Immediate productivity gains of c. 10% of agent all handling time

2. Improved accuracy of note taking

3. Customer satisfaction gains from better corporate memory and more attentive agents

4. More time available for valuable conversations

5. Employee satisfaction gains – happier agents, longer tenures, less sickness, reduced recruitment

6. Regulatory compliance improvements

7. Easy and scalable implementation to shorten implementation timescales and increase AI success

8. Ability to re-invest gains in building AI maturity

Ultimately, accurate (enough) autowrap is an obvious win in any contact centre.

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