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.

To explore why, I spoke with three top-class BPO and Jamaica experts, each of whom brings their unique perspective, from testing multiple outsourcing destinations around the world to Jamaican nationals deeply engaged in the country’s thriving BPO ecosystem.

According to Brad Meiller of Spectrum Brands, who has over a decade of client-side global outsourcing experience from in both retail and telecommunications, and whose philosophy is closely aligned with CCP’s own, while cost and service quality matter, it’s cultural alignment that often makes the biggest difference. And in his view, Jamaica ticks all the right boxes.

1. English-Speaking Advantage

As a native English-speaking country with strong cultural ties to the UK and US, communication is seamless, nuanced, and naturally aligned with Western service expectations. This fluency translates to higher first-call resolution rates and empathetic customer service experiences. And it’s not just language. Jamaican agents bring tone, warmth and cultural familiarity to the table too.

Jamaica is the third-largest English-speaking nation in the Western Hemisphere, and its accent is well-received by British customers.

2. Infrastructure & BPO Ecosystem

Jamaica’s government has invested heavily in digital infrastructure and the BPO sector, recognising it as a key pillar of economic growth. The island now boasts multiple outsourcing hubs in cities like Kingston, Montego Bay, and Portmore, all supported by reliable high-speed internet, business parks, and international flight access.

Connectivity is robust and reliable, with redundant data centres in locations such as Miami ensuring business continuity. The country also hosts two incubators, 220 seats in Montego Bay and 140 in Kingston. This provides scalable options for both startups and growing teams.

Modern network infrastructure, including low-latency fibre and support from the Universal Service Fund, gives Jamaica the capacity to meet UK standards. Ongoing developments like Starlink’s entry to the market continue to strengthen Jamaica’s digital resilience.

Big names like Concentrix, Teleperformance, Sutherland and Alorica already operate successfully on the island, proof that Jamaica can handle high-scale, high-performance outsourcing operations. And with global success stories like Amazon, Netflix, and Target leveraging Jamaican talent, the island’s credentials are hard to ignore.

3. Strategic Time Zone Alignment

Many BPOs in Jamaica provide 24/7 coverage, with service hours tailored to key markets including the UK. For UK businesses, Jamaica’s location also supports efficient logistics. Direct flights from Kingston and Montego Bay to London, Manchester, and Birmingham make it one of the most accessible Caribbean destinations. And with such a solid telecoms infrastructure , remote work is also a viable staffing option, particularly useful for late-night or flexible coverage.

4. Talent Pool & Education

With a literacy rate above 88% and a large youth population, Jamaica is producing thousands of skilled graduates annually, many of whom are turning to the BPO industry for stable careers. Institutions like the University of the West Indies and local vocational programmes  are directly feeding the outsourcing workforce, with a strong focus on service, IT, and administrative support.

There is also strong industry-academia alignment. The Global Services Association of Jamaica works hand-in-hand with universities and training programmes to ensure the labour force is future-ready. And not just for entry-level roles, but for higher-value positions in areas like IT development, integrations, and knowledge-based work.

Jamaican education initiatives such as the HEART/NSTA Trust program provide training across a range of skills such as language communication, sales, data entry, CRM, and IT, ensuring a steady flow of qualified professionals.

5. Competitive Costs with Cultural Fit

While Jamaica may not always be the cheapest, it offers incredible value-for-money when you factor in native English fluency, low agent attrition, cultural compatibility, and a growing pool of trained talent.

At time of writing, the exchange rate between the British Pound (GBP) and Jamaican Dollar (JDM) remains competitive, as highlighted in recent research by Peter Ryan Strategic Advisory, a leading market research and consulting firm focused on CX and BPO.

Critically, the service offering goes beyond standard customer service. Jamaican providers cover front- and back-office functions, including sales, debt collection, IT support, and more.

Importantly, Jamaica is no longer viewed solely as a destination for transactional CX work. It’s now recognised for complex support roles, higher agent touchpoints, and knowledge process outsourcing (KPO) – including finance and accounting services aligned with UK qualification standards.

6. Government Support & Incentives

With special economic zones (SEZs), tax incentives, and strong partnerships with international investors, the Jamaican government has rolled out the red carpet for global businesses. Whether you’re setting up from scratch or partnering with an existing provider, the regulatory environment is built for speed and scalability.

The country’s legal system is modelled closely on the UK’s, providing familiarity and confidence for British and Commonwealth investors. The same is true of its education system, which mirrors the UK structure and standards.

Jamaica’s 2023 Data Protection Act aligns the country’s data policies with international standards, making it suitable for regulated industries like banking, healthcare, insurance, and utilities.

During the April 2025 Outsource2Jamaica event we attended, the government’s commitment was front and centre – Jamaican Prime Minister Andrew Holness personally welcomed international guests and industry speakers, underscoring the strategic importance of the sector.

As Gloria Henry of the Port Authority of Jamaica and Conrad Robinson of the Jamaica Promotions Corporation (JAMPRO)  – both are helping position Jamaica not just as a viable outsourcing option, but as a strategic hub for global service delivery – explained, Jamaica isn’t just promoting itself, it’s backing up its vision with significant public investment. Over $15 million has already been invested into talent development through global training programmes.

JAMPRO also offers “concierge-style” support to businesses entering the Jamaican market, further streamlining the setup and integration process for UK firms.

Final Thoughts

Jamaica is a smart, scalable, and soulful choice for businesses looking to outsource. With its blend of cultural alignment, language fluency, government backing, and operational maturity, Jamaica stands out as a trusted and future-ready BPO partner for UK businesses, particularly for those seeking alternatives to traditional offshore delivery points.

And as Brad Meiller shared with me, BPO selection processes across global organisations often involve extensive RFPs and a lot of box-ticking. Thanks to the strengths outlined above, Jamaican BPOs make that box-ticking exercise remarkably straightforward.

Want to find out more or meet vetted providers in Jamaica? Drop us a line, we’re happy to help you explore your options.

With thanks for their insights to Brad, Peter, Gloria, Conrad and CCP’s Phil Kitchen, who all attended the Outsource2Jamaica event in April 2025.

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

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

What is the AI doing in Auto QA?

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

Let’s examine the benefits.

Key benefits: Comprehensive coverage

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

Resource optimisation

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

Consistent evaluations

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

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

Real-time feedback

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

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

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

Implementation considerations

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

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

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

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

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

Measuring Auto QA success

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

In summary, the benefits are:

· 75% reduction in QA processing time

· 50-100 x increase in evaluated interactions

· 15-25% increase in evaluation accuracy and consistency

· Greater and faster improvement in agent performance and CSAT

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

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

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

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.

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

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

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

1. Autowrap / Call Summarisation

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

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

2. Auto QA (Quality Assurance)

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

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

3. Auto Coaching

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

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

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

4. Identifying Vulnerable Customers

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

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

5. Agent Assist

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

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

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

6. Hands-Free Conversations

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

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

7. Full Voice AI

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

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

Final Thoughts: Think “value first, tech second”

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

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

1. Call Pickup Rate – Low pickup rates may indicate poor dialling strategies or incorrect customer data. Are managers addressing these issues to improve connection rates?
2. Average Call Duration – Longer calls may suggest engagement or inefficiencies. Do managers have the available insight to help distinguish between productive interactions and wasted time?
3. Average Handling Time (AHT) – Balancing efficiency with quality is crucial. Are managers optimising processes to reduce delays without compromising service?
4. Answering Machine Detection Rate – High voicemail rates waste agent time. Are contact centres adjusting their approach to minimise this?
5. Rejection Rate – A high volume of unanswered calls may point to poor targeting or dialling strategies. Are managers refining their approach based on this data?
6. Agent Wait Time Between Calls – Excessive downtime signals inefficiency. Are workflows optimised to keep agents engaged and productive?
7. Conversion Rate – Success is measured by call outcomes. Are managers analysing why some calls convert while others fail, and adjusting strategies accordingly?
8. Occupancy Rate – Are agents being overworked, leading to burnout, or underutilised, resulting in wasted resources? Do managers have the right data and leadership skills to balance their team members’ workloads effectively?
9. Data Accuracy – Poor data leads to inefficiencies and failed connections. Are managers ensuring databases remain up to date?
10. Right Party Contact Rate – If agents aren’t reaching the intended recipients, performance suffers. Are managers taking steps to improve contact accuracy?
11. Customer Satisfaction Score (CSAT) – A positive customer experience is essential. Are managers prioritising training to enhance service quality?

• Lack of real-time performance insights.
• Insufficient training for managers to interpret and act on KPI trends.
• Inefficient processes that fail to align with data-driven improvements.
• Gaps in technology preventing optimal call routing and workflow automation.

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.

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

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

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

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

Voice is here to stay?

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

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

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

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

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

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

Public adoption is key then?

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

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

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

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

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

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

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

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

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

Is some re-programming required?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What does it all mean?

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

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

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

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

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

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

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

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

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

The cost of failing to deliver on the Brand Promise

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

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

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

Managing customer experience at scale

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

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

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

Leveraging outsourcing to deliver consistent experiences

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

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

Mitigating outsourcing risks

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

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

Conclusion

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

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