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.

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

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

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

What is the AI doing in Auto QA?

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

Let’s examine the benefits.

Key benefits: Comprehensive coverage

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

Resource optimisation

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

Consistent evaluations

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

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

Real-time feedback

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

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

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

Implementation considerations

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

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

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

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

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

Measuring Auto QA success

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

In summary, the benefits are:

· 75% reduction in QA processing time

· 50-100 x increase in evaluated interactions

· 15-25% increase in evaluation accuracy and consistency

· Greater and faster improvement in agent performance and CSAT

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

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

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

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

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

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

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

Voice is here to stay?

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

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

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

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

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

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

Public adoption is key then?

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

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

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

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

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

Products and services using artificial intelligence will profoundly change my daily life in the next 3-5 years – 66%

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

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

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

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

Is some re-programming required?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What does it all mean?

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

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

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

At Customer Contact Panel (CCP), we’ve witnessed first hand how these factors are influencing decision-makers, especially CX leaders and CFOs. If you’re in the midst of making an outsourcing choice, you’ve probably got one of the following on your mind.

Growing Customer Demands: Meeting High Expectations

It’s not just about answering calls anymore. Customers want fast, personalised, and empathetic interactions that feel seamless and aligned with your company values. This means businesses must be more careful than ever when choosing an outsourcing partner. A BPO’s cultural fit with your company is crucial—they need to speak your tone, align with your brand, and uphold the level of service your customers expect – all of which take time which you don’t have. So, companies are scrutinising potential partners more closely, ensuring they’re a perfect match.

Technology: The New Wild Card

Right now, you’re being asked to do more with less or deliver a better service with the same budget. With inflation, high interest rates, and currency fluctuations, offshoring doesn’t feel like a financial guarantee anymore. Add in automation—think AI tools and chatbots – and CFOs are starting to wonder if tech could be the silver bullet to that beast of a budget. Whilst AI and Automation can scale fast, they can come with hefty initial costs. Businesses are now weighing their options:

Do they stick with outsourcing (onshore, nearshore, or offshore)? or
Do they double down on tech?

It’s a tough decision. Get it right, and they could boost customer loyalty; get it wrong, and it might lead to a backlash.

ESG: Outsourcing in a Politically Charged World

Outsourcing is no longer just about cutting costs; it’s also about navigating complex ethical and political waters. With Keir Starmer pushing for stricter ESG (Environmental, Social and Governance) standards, businesses are questioning their outsourcing partners, especially if those countries are known for poor labour practices or environmental issues. Throw in political instability and outsourcing now feels like a risky gamble. Operations could grind to a halt at any time, and businesses can’t afford that.

On top of that, data security is tighter than ever. With the UK government’s more stringent regulations, especially for industries like finance and healthcare, outsourcing is becoming bogged down in compliance red tape. A single data breach could ruin a brand’s reputation and customers’ trust—so finding a partner who understands data security is more important than ever.

Lastly, with the UK’s £22 billion budget shortfall and a focus on reshoring jobs, companies are balancing cost savings against their political and ethical responsibilities.

How CCP Makes Your Life Easier

At CCP, we get it – outsourcing feels complex. But we’re here to simplify it for you. We help businesses make smart, informed and equitable choices through services such as:

Partner Matching: We connect businesses with a handpicked network of pre-vetted outsource partners (220+ partners infact), cutting down on the time and risk of finding the right partner.
Cultural Fit Analysis: We ensure your outsourced team aligns with your brand’s values and service style, so there’s no misstep in tone or approach.
Technology Sourcing: We know how difficult it is to cut through the sales patter and find the right tech for your customer contact needs. Well look no further, we have a network of 120+ pre-vetted and audit technology partners – who will get right to the point.

The Bottom Line

Outsourcing decisions are taking longer now because the stakes are higher. Customers expect nothing less than excellent service, and businesses are being much more careful about who they partner with. But with the right approach, outsourcing remains a powerful tool.

At CCP, we guide businesses through the process, ensuring they find the right fit, reduce risks, and build lasting partnerships. In fact, 93% of CCP’s clients maintain long-term relationships with their outsourcing providers – proof that our approach works.

With CCP by your side, navigating the increasingly complex outsourcing landscape is much smoother, helping you make the right decisions for today’s customer demands and tomorrow’s success.

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.

Since the launch of ChatGPT and subsequent proliferation of AI-driven technologies across the customer contact technology landscape, the pace of change has accelerated exponentially. From communication analytics, quality management and agent assist, to replacing IVRs with their omni-channel equivalents and real time translation services, the impact of AI on our industry has almost been as big as the invention of the telephone itself. Not only is this a source of organisational stress, are there implications for outsourcing and contract mechanisms?

While the telephone fundamentally changed human-to-human communication, AI technologies are transforming human-to-machine interactions. And given how we now communicate with each other, that can only mean that change is both permanent and ongoing.

This permanent change is putting increasing stress on organisations to restructure how they equip themselves to communicate with both their customers and their target market, irrespective of the sector they operate in.

We are seeing organisational stress in three areas:

Understanding the impact of these changes across the customer and target market demographic
Properly evaluating the ability of existing technology platforms to accommodate change and realign to new communication paradigms in the mid to long term
Fully understanding the mid to long term commercial impact of bolt on technologies to deliver short term performance and cost reduction gains.

In helping organisations cope with these stress areas we see striking parallels to the changes seen when organisations first began to interact with customers remotely. The evolution which began in the 90s to get more value from data, media and communication spend resulted in specialist operational activity often being outsourced as few organisations had the specialist people, process or technology to support those functions as cost effectively as outsourced providers could.

As customer became core to operations and remote customer management became an operational necessity, organisations began to establish their own capabilities, leveraging existing data management infrastructure to control and drive insight. Customer contact levels boomed, the technology to enable customer contact saw similar growth and our industry was born as more organisations grasped the benefits of controlled and managed remote customer contact.

Considering those parallels, we ask “Should organisations be stressing themselves or does that same customer contact outsourcing industry ‘muscle up’ for this second cycle of permanent change to reduce organisational stress in the shape of Outsourcing 2.0, underpinned by end-to-end AI driven technologies and the accelerated levels of operational insight that provides?”

Observing change in the global customer contact outsourcing market

Outsourcing is a very broad church. Our global network of two hundred plus providers gives us a privileged view of the diversity of the services provided, service delivery and how services are contracted. Whilst industry pricing has generally reduced in real terms over time, the way that service pricing is presented has not really evolved that much, it has not been that complicated, clients have had a choice of:

straight hourly rate,
the hourly rate with outcome components,
outcome with activity components and
outcome alone.

Making money has not been that complicated either. As long as the effective revenue per hour outweighed the total known costs per hour, then outsourcers made money.

The outsourcers’ challenge has always been about packaging service and price to be more attractive than the nearest competitor. Of course, that global mind set has led to an incredibly diverse and attractive industry offering, but it is doing little to relieve the level of stress we see in organisations today. Especially as permanent change begins to impact on mid to long term business performance forecasts.

What we see in the global outsource community is the emergence of some clear lines of distinction between service offerings. Principally between those that have the capability (or plan to have the capability) to relieve mid to long term organisational stress and those that don’t. Whilst those customer contact outsourcers that don’t have (or don’t plan to have) this capability to de-stress, continue to be well placed to solve organisations’ immediate and short-term challenges, we have seen some significant levels of technology and skills investment towards an Outsourcing 2.0 capability, amongst those that can.

What we are not seeing enough of is an evolved, Outsourcing 2.0 contracting proposition. One that is equitable for both parties. A simple contract proposition that provides an upside for the technology and skills investment of the outsourcer and in return, long term risk and cost reduction for the client.

Striving to deliver an equitable position to accommodate permanent change

Our view of the customer contact world is that its typically the client that makes the decisions on how they want to automate customer contact to reduce friction and cost in their operations. We also understand that the client may also want to:

know more about how changes in customer contact are going to flow through product and service delivery,
be able to make active decisions on what those changes mean in terms of risk and profitability,
identify service solutions that will de-risk their journey to deliver customer contact at lower cost,
deliver value and benefits that justify the time, cost and effort in enabling process change
potentially deal with specific customer use cases where they do not have the physical capacity or technical capability,
do all this without compromising service delivery in terms of quality and data governance i.e. the regulatory and contracted controls supporting data privacy and data security.

We also recognise that the world of customer contact tech is changing faster than it probably ever has; and it’s hard to tell what the next few years will bring in terms of time saving and service enhancing technologies.

SaaS based technology is easier to deploy, easier to recognise ROI, is already impacting on the flow of low complexity tactical work into the global outsourcing industry. This trend is also evidenced by more work. being delivered via the client’s own technology, enabling them to leverage their existing back-end systems integrations and continue BAU without interrupting the established end to end data flows supporting their existing operational reporting and decision-making processes.

Whilst the outsourcer community is continuing to strive to deliver uncompromising value, to positively impact service delivery and comply with regulatory and contractual data governance, there is clearly an acknowledgment of the impact that customer contact automation brings to their core market.

Contracting to deliver mid to long term value by de-risking the transition to a permanent change in customer contact

Global outsourcing capability now has a firm dividing line across it. Those that have (or plan to have) the infrastructure and technology to deliver permanent change (2.0) and those that have not.

For those that have (or plan to have), we can see the opportunity for a new type of contracting relationship with clients. A contracting relationship that:

de-risks the client from the turbulence of technology change whilst delivering the permanent changes we see in human communication and the impact that has on customer contact service delivery,
will enable the outsourcer to deliver appropriate levels of automation without compromising the clients service objectives and targeted cost reduction when expressed as simply as a ‘cost per customer per annum’.

In the context of customer contact, clients find the idea of managing permanent change, de-risking the potential downside of getting that change wrong and doing that at a fixed and reducing cost very attractive. Especially when that contract properly considers how service quality is measured and underpinned as well as being directly linked to cost.

There are then two big variables in the client conversation that are both fundamental to a new contracting proposition.

One is the composition and contracting position of the legacy technology overhead that delivers existing levels of customer contact, especially how that customer engagement technology stack is connected into the ‘back office’ technology of the organisation.

The second is about existing costs. This is the client’s understanding of their current cost of managing their customers. Specifically, how that’s calculated especially in the context of a desire to express that as a ‘cost per customer per annum’.

Recognising the barriers to change

It is easy for us to oversimplify what we see as a new contracting paradigm. But we also recognise that the old contracting models don’t proactively and deliberately reduce the organisational stresses that put mid to long term business performance at risk.

Of course, some business verticals carry significant levels of technology debt. However, unless we explore, and explore deeply, what the possibilities are, then the weight of technology debt will drown organisations and fail as a lifejacket to guarantee ongoing survival.

Unless your technology is able to destress your organisation now, then the same technology stands little chance of keeping you afloat in the mid to long term.

Customer communication and customer contact management has changed permanently and is different from customer contact pre-the explosion in generative AI. This means new risk and new levels of risk for both client and the customer contact outsource communities. Our view is that contracting between the parties needs to change fundamentally.

Outsourcing 2.0 offers a mid to long term view of how both client and service provider can benefit whilst reducing risk on both sides. By looking at contracting differently, both parties can focus on their core strengths and experiences to set and deliver against service goals and commercial objectives. To do that mutually establishing, expressing and agreeing the existing technology stack directly supporting customer engagement and the cost per customer per annum to deliver growth and service objectives.

What next?

What are your thoughts on the future of customer contact outsourcing contracts? Do you think we’re thinking along the right lines, or have we underestimated the complexity of clients’ current position, especially in the context of technology debt? Whether you’re a client or service provider we’d love to hear what you think.