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.

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.

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.

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.

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 today’s outsourcing landscape, success depends on much more than cost savings and process efficiency.

On 25^th February 2025, Neville Doughty and Phil Kitchen from the Customer Contact Panel hosted a webinar with Joe Hill-Wilson, CEO and Co-Founder of Learn Amp and Martin Hill-Wilson, Owner of Brainfood Consulting, to discuss Sustainable Operating Models in Outsourcing. One of the most important takeaways from the discussion on sustainable operating models is that Learning and Development (L&D) must be embedded into the core of every outsourcing strategy. Without continuous learning, sustainability simply isn’t possible.

Why Learning and Development is a Sustainability Driver

In outsourcing environments, teams often face rapid change, evolving client expectations, and shifting technologies. This is reflected in the data – 92% of organisations are facing high or very high risk of top talent leaving in the next year (Brandon Hall Group, HCM Outlook, 2024). Without a structured and ongoing approach to skills development, outsourced teams can struggle to keep pace, leading to inconsistent quality, reduced productivity, and higher turnover . During the webinar, 82% of attendees reported that current procurement practice restricts the value they can bring to their clients.

The key takeaway? Organisations that embed L&D into their operating models create more resilient, adaptable, and future-ready outsourcing workforces.

Challenges in Sustainable Learning for Outsourced Teams

The panel discussed the various challenges companies face when it comes to embedding learning into outsourced operations:

Geographical and Cultural Gaps: How can we create a unified learning experience for teams spread across different countries, cultures, and time zones?
Engagement and Adoption: With high attrition rates common in outsourced environments, how do we motivate teams to actively engage in learning?
Measuring Impact: How can we quantify the ROI of learning programs in outsourcing partnerships?

What Effective L&D Looks Like in Sustainable Outsourcing

When looking at solutions for the challenges discussed, the panel noted the importance of centralised learning platforms that deliver consistent, engaging content to all locations. Platforms like Learn Amp help organisations create:

Standardised onboarding programs to accelerate time-to-competence.
Bite-sized, mobile-friendly learning content to fit learning into busy shifts.
Social learning spaces that encourage peer-to-peer knowledge sharing.
Data dashboards to measure engagement, skills development, and business impact.

Embedding L&D into Operating Models: 3 Key Strategies

Treat L&D as a Business Process, not a Project
Learning shouldn’t be an afterthought or an annual event. It needs to be a continuous, embedded process that evolves with the business and its outsourcing needs.

Make Learning a Shared Responsibility
Learning success shouldn’t fall solely on HR or L&D teams. Operations managers, team leaders, and employees themselves all need to co-own learning outcomes.

Measure What Matters
Sustainable learning models measure not just completion rates, but real business impact: faster onboarding; fewer errors; higher customer satisfaction; and improved employee retention. The LinkedIn Workplace Report shared that 94% of employees would stay longer if companies invested in their development.

Key Takeaway

If there’s one key takeaway from the webinar, it’s this: sustainable outsourcing depends on sustainable learning. When organisations invest in embedding learning into every stage of the outsourcing lifecycle, they create an employee experience where team members thrive.

If you would like to access a copy of the recording it is available here: Webinar Link

The year ahead promises to be a turning point for customer contact. AI and automation are advancing at an unprecedented pace, yet businesses are facing economic uncertainty, rising costs, and rapidly shifting customer expectations. The pressure to adopt new technology and improve service levels means leaders must make bold, strategic choices.

At the end of 2024, we held our annual ‘Big Conversation’ to uncover key challenges for the year ahead and hear directly from cross-sector contact centre leaders about how they’re addressing them. These insights have shaped our latest whitepaper, 2025: A Year of Difficult Conversations?. In this paper, we explore those challenges in detail and outline priorities and solutions. One theme dominates: success in 2025 will depend on how well businesses navigate ‘difficult conversations’—both within their organisations and with their customer and suppliers.

How can you make the right tech decisions in the age of AI?

AI can be a powerful tool for improving operational efficiency. However, the reality is stark: according to Gartner, 80% of AI projects fail, which is twice the failure rate of non-AI projects. Despite this, the pressure in the boardroom to “do something with AI” is stronger than ever. The key question isn’t whether to implement AI, but how to do so strategically and safely.

When AI is implemented well it can deliver valuable results. But the risks of adopting this still fledgling technology can be significant—wasted investment, damage to reputation, and disruption to operations. The businesses that succeed with AI will be those that clearly define its use cases, align them with business goals, invest in high-quality, integrated data, and ensure that AI complements human expertise rather than replacing it. AI has the potential to be a game-changer—but only with careful consideration.

How do we meet economic, regulatory and resource challenges?

While grappling technology decisions, contact centres also face ongoing economic headwinds, regulatory challenges and a 15% decrease in headcount since 2019.

As businesses introduce new contact channels and explore innovative solutions, the fundamental customer need remains unchanged—a fast and effective response

But despite the rise in self-serve and co-pilot automation, customer satisfaction in the UK has declined. While automation is handling simple queries, agents are left to tackle only the most complex cases with fewer resources overall. Agents have little respite from more intense interactions and operations have fewer agents available. Even with future AI implementations, research predicts relatively modest headcount reductions of a maximum of 15%.

What’s more, in 2025, UK contact centres will need to absorb and manage an 8-10% increase in agent costs. Meanwhile the ongoing cost of living crisis means customers remain stressed and regulatory requirements add to operational demands —all against the backdrop of a muted growth forecast and ongoing economic challenges. No wonder things feel pressured.

Consequently, leaders are exploring various ways to optimise their service models, including offshoring, automation, or refining their approach.

Getting It Right: From Good to Great

One thing is clear. Transformation isn’t optional—it’s essential. The businesses that thrive in 2025 will be the ones that take a proactive approach. The most successful organisations will define clear, achievable AI use cases, align data, technology, and human expertise, prioritise governance, security, and compliance, and engage employees in AI adoption from the start.

The path ahead will present both opportunities and challenges, but with the right strategy, tackling today’s difficult conversations can pave the way for a stronger competitive edge tomorrow.

Read our paper for more detailed analysis of the challenges, but more importantly, how to tackle those challenges and put in place a positive programme of change.

The Whitepaper is free to download and immediately accessible below. We would love to hear your experiences too. Follow us on LinkedIn to share your thoughts.

View our Whitepaper now

Collectively Customer Contact Panel (CCP) have a lot of experience in contact centres and CX. However, as things are changing rapidly in the world of AI, we met with CCP to discuss some of the broader considerations, including what technology we could deliver as an alternative view of what could be our probable future reality with the support of AI – we agreed we would share the output.

The art of the possible?

We can talk about the pace and the tone of this piece, however the reality is that with appropriate questions raised and some boundaries that we included (some for our amusement) the content within this 10 minute AI generated “BotCast” not only demonstrates the capabilities of AI, but raises some interesting questions and considerations as to how CX evolves and what guard rails need to be in place.

Click here and have a listen, and of course, if you want to discuss we’d be happy to deploy some of our humans.

Say it quietly if you like, but businesses are grown and maintained through increases in customer numbers and/or customer value. Undoubtedly cost management is also a critical factor, but ultimately sales and retention activity that provides topline growth is critical to ongoing success and business value.

We all know that the chances of winning or retaining a customer are increased when you provide a great product or service. And that those who deliver, not just on price but perceived value, are in prime position to pick up customers from competitors when they do not.

Yet many businesses are focused on the potential cost savings that could be achieved through AI and automation. Have they have lost sight of the potential benefits of delivering a personalised service and those golden opportunities to encourage a customer to buy more or stay for longer?

Are you getting the best sales through service opportunities from AI and automation?

There are two key scenarios that could be playing out for many organisations, both B2B and B2C. Either of which could be limiting sales success:

1. The technology is doing great stuff

Customers are getting the service that they need in the moment they need it. Which means the brand is working on the assumption that because they’re well-served, they will come back to buy more. However, they are not engaged with these customers, they are simply dealing with their admin when they need to and as a result are being passive in their habits. This may work for on a number of levels, and it is reducing the cost to serve. However, is this a step away from brand bypass, as ultimately a gap in the connection with customers will result in them moving on when they see a better offer?

2. The technology isn’t hitting the mark

Customers are trying to resolve their issues, but are struggling. The automation or self-serve models don’t provide the right options and/or have no ‘way out’ for customers and as a result they become frustrated. So at the first opportunity, they are going to look to an alternative brand.

The examples are out there in key sectors.

Ofgem March 2024 data

Harder to contact and less satisfying to deal with?

Despite and improving picture, the latest Ofgem data shows that 16% of customers find it difficult to contact their supplier, up from the low of 10% in Q1 2019. Meanwhile, the same Ofgem data suggests that overall satisfaction with customer service across the energy industry currently sits at 66%, down from the peak of 74% seen in Q2 of 2020.

What’s more, the latest UKCSI data shows utilities performing the poorest with a score of 69.8. Telecommunications and Media brands are doing a little better at 73.3 (though down from January’s 74.7), but are still some distance short of the podium positions achieved by Retail (non-food) at 80.4, Tourism at 79.3 and Banks & Building Societies at 79.3. However, we can see drops in satisfaction across the board.

Could automation be contributing to those less satisfying experiences?

UKCSI data from earlier in the year tells us that for 53.7% of automated contacts, the customer still needed to speak with a human being.

Equally concerning, though, was that neither AI/chatbot or customer service employees are managing to resolve customer queries more than 54.2% of the time, as seen in the January results. Quite the damning indictment.

Consider also that 45.4% of customers would avoid using an organisation again due to poor use of technology.

Clearly there is work to be done.

Companies with higher customer satisfaction show stronger growth

But what is the impact of this on a brand’s fortunes? Is the 2-point drop in score for Telcos material?

Research in the UKCSI report from January 2024 shows that between 2017 and 2023 “companies with customer satisfaction at least one point higher than their sector average achieved stronger revenue growth”.

With c.80% higher compound revenue growth, 6.6% higher EBITDA, more than double the operating profit margin and a whopping £283.9k – more than half as much again – revenue per employee on the table for that increase of just one point, the importance of customer satisfaction to both the topline and the bottom line is stark. On the other side, the virtual lack of revenue growth and much reduced operating profit margin for 1-point lower puts into context the plight of Tourism, Leisure, Insurance, Public Services and the rather more beleaguered Telcos.

The same report highlights that 27.6% of customers who score an organisation 9 or 10 out of 10 for overall satisfaction will look to buy other products or services from them, whilst 20.8% of customers scoring 1 to 4 will spend less with the organisation and 41% scoring them at 1 to 4 will avoid dealing with the organisation again in the future if possible.

And so, it is easy to see why investment in customer service is critical to the success of an organisation. Why an organisation should be – and hopefully is – highly focused on it. And why a pure cost-reduction focus for automation or AI is short-sighted.

While these numbers tell quite the story, let’s assume things are the right side of the line service-wise, whether through AI or not. The next question then is, are you following up with the appropriate sales activity to effect further topline growth?

Are you ready to pick up the sales baton?

Effective sales operations depend on 7 key factors for growth, the same apply to both sales team and those required to deliver sales through service:

Access to the best people with the necessary sales and communication skills,
Clear reward and recognition structures with incentives, creating a culture and environment which encourages growth,
Appropriate product knowledge and ongoing team development, ability to handle objections effectively and to share learning to advance the performance of the team,
Effective technology which the team can leverage to access customer insights, understand which are the best customers to be contacted, when to contact them and what solutions to offer,
Practical approaches to sales compliance, which provide clear guidelines but can be managed without excessive burden to managers, allowing sales to be signed off effectively and if necessary, learning applied in a timely manner,
Ability to manage data and reporting to maximise sales opportunities which benefits the organisation, the sales agents and also the customers through ensuring access to right information at the right time,
Understand market conditions, customer behaviours and how your team needs to react to these.

If just one of these seven isn’t working too well, sales will suffer. But so may customer service or perceived value. For example, an intrusive offer in the middle of a customer complaint is likely to occur as unempathetic and may see the customer running for the hills. A well-handled complaint can increase value – or at least maintain it.

A colleague described a recent interaction about a problematic return with a well-known retailer, where mid-conversation they were invited to look at product that may interest them. Unsurprisingly, their reaction was not to immediately head to the link to browse, but instead to give a sharp retort – and then tell anyone who cared to listen how annoyed they were.

Not only did the retailer not make the sale, they likely turned the customer off. An excellent example of numbers 1, 2, 3, 4 and 7 (at least) not working. Not only was it bad scripting and a lesson in not what not to do, it may speak to overly aggressive reward structures and an environment that favours sales over growth. The nuance of which is important and why point four is critical – this was not the best customer to be contacted in this way at that time.

The same colleague similarly experienced rather odd service (from a Telco…) in store recently, where a service conversation without a satisfactory outcome turned to an attempt to upsell on a different product, followed by a recommendation to leave the brand for the product where the service outcome was unsatisfactory. Quite the rollercoaster! And no doubt an experience driven by a particular sales focus that the brand’s managers would be horrified to learn they have – let’s hope – inadvertently incentivised.

Picking your moment to turn service into sales is critically important and relies heavily on the skill of the individual, their training and incentivisation, supported by culture, technology and management.

With so much focus on customer service, do you have the need, will and capacity to optimise sales?

Great agents who can both serve and sell can be hard to find, and can be even harder to retain..

The use of technology and automation is increasingly expected for customer service – and rightly so, simple service issues don’t need complex solutions. But they do need human intervention when the service question isn’t simple, or the automated response fails. Or perhaps when a sales opportunity requires a more personal service.

The ability to deal with customers, their nuanced needs and when selling, their objections, still has a high level of dependency on human interaction.

Yet the data from Ofgem and UKCSI both illustrate that customers are frequently frustrated by both automated and agent interactions. Service delivery in many sectors is still some way short of previous highs, meaning there are still gaps to fix in customer service before you can even think of perhaps selling.

And to some extent, when improving customer experience can deliver increased revenue, getting the basics of service right first is a significant route to growth and building value – whether you agree or not about whether they ought to be, measures such as revenue growth, EBITDA and revenue per employee are important to investors and share price.

How you achieve optimised service, then layer on sales through service or even pure sales activity is a significant question. Each have their own challenges, but successful outcomes add up to an organisation that both sells to and retains customers optimally.