For much of the last decade, customer experience investment was driven by a relatively simple argument. Better experiences create better outcomes. Today, that argument is no longer enough.

Customer Contact Panel sponsored research by Ryan Strategic Advisory through the 2026 CX Technology and Global Services Survey to explore a challenge we encounter regularly: what gives organisations the confidence to approve investment in customer operations transformation? We asked 815 enterprise executives what forms of external support would help them make faster and more confident decisions regarding contact centre technology, outsourcing and operational change.

The answer was remarkably clear. Proof. Business case and ROI modelling ranked as the single most important factor. Independent views on technology options, trade-offs and proven outcomes ranked immediately behind. Delivery model design also emerged as a major consideration.

Taken together, the findings reveal a significant shift in how organisations evaluate CX investments. Decision makers are no longer looking for promises. They are looking for evidence.

This reflects the environment many organisations now operate within. Budgets remain under pressure. Technology options continue to multiply. AI vendors are making increasingly ambitious claims. Outsourcing models are evolving. The number of potential solutions available to contact centre leaders has never been greater.

Ironically, more choice has made decision making harder. The challenge facing many organisations is not a shortage of opportunities. It is a shortage of confidence.

Can the supplier deliver? Will the technology work? How long will implementation take? What risks need to be managed? Most importantly, what return will the organisation receive in exchange for its investment?

The survey suggests that organisations increasingly want independent validation before committing to major decisions. This explains why advisory support, benchmarking, decision validation and technology assessment all scored strongly among respondents.

Executives are seeking reassurance that the choices they make are grounded in proven outcomes rather than marketing claims.

This has important implications for suppliers and internal transformation teams alike. Features are no longer enough. Capabilities are no longer enough. Even successful case studies may not be enough.

Organisations want to understand how a proposed solution will perform in their environment, against their objectives and within their commercial constraints.

That requires a different conversation. One focused less on products and more on outcomes. Less on innovation and more on implementation. Less on possibility and more on proof.

Perhaps the most significant finding from the survey is that decision making itself is becoming a competitive advantage. The organisations moving fastest are not necessarily those with the largest budgets or the newest technology.

They are the organisations that can build confidence, align stakeholders and create a credible business case for change.

In a market full of options, confidence may be the most valuable asset of all. And confidence starts with proving the ROI.

Catch up on the series by reading Why CFOs Hold the Key to CX Transformation here, followed by Who Really Drives CX Change? here.

Most customer experience transformation programmes begin with an assumption. 

If the business wants to improve customer outcomes, the people responsible for customer experience will naturally drive change. 

The reality appears more complicated. 

As part of the 2026 CX Technology and Global Services Survey by Ryan Strategic Advisory, Customer Contact Panel sponsored research into a question we regularly encounter when supporting customer operations transformation programmes: who actually drives change inside large organisations? The results highlight a clear distinction between the executives who champion transformation and those who are more likely to support it once a direction has already been agreed. 

When asked which board-level stakeholders are most likely to drive customer operations transformation, respondents consistently pointed towards CEOs, Managing Directors, Chief Operating Officers and Chief Customer Officers. 

In fact, nearly seven in ten respondents identified CEOs as either enablers or strong enablers of change. 

The Chief Customer Officer and Chief Marketing Officer scored even higher when it came to strong enabling influence. 

These findings reinforce an important truth. 

Successful CX transformation is rarely a technology initiative. It is usually a business initiative. 

The strongest advocates for change tend to be leaders who are accountable for growth, competitiveness, customer retention or operational performance. 

However, some of the survey’s most interesting findings relate to the roles that were notably absent from the list of major change drivers. 

Chief People Officers and HR leaders were among the least likely to be identified as strong enablers. 

Transformation Directors and Solutions Leaders performed better, but not as strongly as many might expect given that transformation is often their primary responsibility. 

Even Chief Technology Officers were more likely to be viewed as enablers than strong enablers. 

Why does this matter? 

Because many organisations continue to position transformation projects around technology implementation rather than business outcomes. 

Technology teams help make change possible. 

Transformation teams help coordinate delivery. 

But the survey suggests that the initial momentum often comes from commercial and operational leadership. 

The leaders driving change are those closest to customer outcomes, growth targets and operational performance metrics. 

For CX leaders seeking support for a new initiative, there is an important lesson. 

The first challenge is not selecting the right solution. 

The first challenge is building the right coalition. 

Understanding who naturally supports change, who remains neutral and who may resist investment can dramatically improve the chances of success. 

Many transformation programmes fail before they begin because they focus on technology selection before stakeholder alignment. 

The data suggests the reverse approach may be more effective. 

Get the right people around the table first. 

Then decide what to do next. 

If you missed the first article, Why CFOs Hold the Key to CX Transformation , you can read it here. Then continue to the third article, Show Me the ROI: What CX Leaders Need to Prove, here.

For years, the customer experience industry has focused on technology. 

CRM, AI, automation, analytics, cloud migration and workforce optimisation have dominated conference agendas and boardroom discussions. Yet despite the growing number of available solutions, many organisations still struggle to move forward with meaningful change. 

New research from the 2026 CX Technology and Global Services Survey suggests the problem may not be technology at all. 

Customer Contact Panel sponsored a series of questions within the survey by Ryan Strategic Advisory, gathering the views of 815 enterprise executives responsible for strategic contact centre decisions across North America, Europe and Asia-Pacific. The findings reveal a striking pattern. 

When respondents were asked which board-level stakeholders are most likely to drive or block change in contact centre operations, one role stood out above all others. 

The CFO. 

Forty percent of respondents identified the CFO or Finance Director as either a blocker or extreme blocker to customer experience investment decisions. No other executive role attracted a higher level of resistance. Meanwhile, only 36% viewed finance leaders as enablers or strong enablers. 

That creates a significant challenge for anyone attempting to introduce new technology, redesign an operating model, or secure investment in customer service transformation. 

The findings are not necessarily a criticism of finance leaders. In many organisations, the CFO is fulfilling exactly the role they are expected to play. 

Their responsibility is not to champion innovation. Their responsibility is to protect capital allocation and ensure investments generate measurable returns. 

The issue is that many CX initiatives are still presented in terms that finance teams struggle to validate. 

Better experiences. 

Improved customer journeys. 

Reduced effort. 

Greater engagement. 

All worthwhile objectives, but often difficult to translate into financial outcomes. 

The survey’s second question helps explain why this matters. 

Respondents were asked what external support would help them make faster, more confident decisions around contact centre investment. The clear winner was business case and ROI modelling. 

More than four out of five respondents rated ROI modelling as having either high or critical impact on decision making. 

The message is straightforward. 

Most CX leaders spend time trying to convince stakeholders that change is necessary. The data suggests they should spend more time proving that change is financially justified. 

The organisations making progress are not necessarily those with the best technology. 

They are the organisations that can clearly demonstrate the commercial impact of change. 

If finance is holding the purse strings, then finance needs evidence. 

The future of CX transformation may depend less on the quality of the solution and more on the quality of the business case behind it. 

Continue the series by reading Who Really Drives CX Change? here, followed by Show Me the ROI: What CX Leaders Need to Prove here.

AI has amplified all of this. What was already a complex technology landscape is now louder, faster, more confident in its promises and far less easy to rationally assess. 

The result is a familiar pattern. Reams of content. Lots of conversations. Plenty of demos. Very few decisions. 

“The problem isn’t a lack of technology. It’s a lack of confidence about where to start.”

Too much choice, not enough direction

Most contact centre leaders are exposed to hundreds of tools, platforms and propositions. CCaaS, Automation, AI, Analytics, Workforce optimisation, Knowledge, Quality, Speech, Sentiment, and/or Real-time coaching. 

Each promises transformation, yet few explain sequencing or iterative value. 

Technology discussions often jump straight to an end state. Fully automated journeys. AI-first contact centres. Single platforms doing everything. The reality is that most organisations are not starting from a clean slate. They are operating with legacy systems, ingrained processes and teams who are already stretched. 

When leaders are presented with change at scale, hesitation is a rational response. 

“Indecision is rarely caused by resistance to change. It’s caused by unclear risk.”

One ecosystem, many perspectives 

One of the most common mistakes we see is treating contact centre technology as a single audience decision. In reality, it is experienced very differently depending on where you sit. 

Customers experience outcomes – Resolution, speed, effort. 

Agents experience tools – Screens, prompts, workflows, knowledge. 

Team Leaders experience data – Performance metrics, quality scores, coaching demands. 

Executives experience cost, compliance, risk and return. 

Technology fails when these perspectives are treated in isolation. A tool that improves reporting but makes life harder for agents will not deliver sustainable value. Automation that reduces contacts, but damages trust will quickly be rolled back. 

“Technology only works when data flows through the organisation, not when it stops at functional boundaries.” 

Why replacing everything may not always work

There is a temptation to believe that the answer is replacement. New platform. New vendor. Clean start. 

Sometimes that is necessary. Often it is not. Make sure you have clarity as to what you need to achieve and the capability of the solution you are looking at as large-scale CCaaS or platform replacement is expensive, disruptive and can be slow. It introduces delivery risk at exactly the moment many organisations are under pressure to stabilise performance. It also assumes that the underlying processes are already fit for automation, which is rarely the case. 

Some organisations have truly exhausted their tech ecosystem’s capabilities and potential.  

But many organisations do not need everything at once, they need progress. 

That is why we increasingly see value created through targeted, point-solution adoption. Technology that does one job well and (crucially) integrates into the existing environment.

“Momentum is more valuable than perfection.” 

Starting where impact is visible 

One of the most effective starting points we see for technology change is quality management. 

Historically, quality assurance has been constrained by sampling. A handful of interactions reviewed each month, representing a fraction of actual customer conversations. Coaching is based on partial insight. Risk is often identified after the event. 

Automation changes that dynamic. Moving from fractional sampling to full visibility unlocks far more than compliance. It enables better coaching, faster identification of issues, clearer insight into customer sentiment and more consistent experiences. 

Importantly, this type of AI does not remove people from the process. It supports them. 

• Agents receive clearer feedback. 

• Team leaders focus on coaching rather than administration. 

• Leaders gain confidence in what is happening across the operation

“AI delivers value fastest when it helps people do their jobs better, not when it tries to replace them.” 

What good looks like now 

The most effective contact centre leaders we work with are not chasing the biggest transformation story.

They are making deliberate choices.
They prioritise problems before platforms.
They sequence change rather than attempting to do everything at once.
They invest in technology that supports people and process, not just cost reduction.
They accept that doing nothing is still a decision, and often the riskiest one. 

Technology will continue to evolve. AI will become more capable. Customer expectations will continue to rise. The organisations that succeed will be those that move with intent rather than waiting for certainty. 

“The most effective contact centres are not the most automated. They are the most deliberate.”

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

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

What Consumers Are Saying (And Why It Matters)

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

What does all this mean for CX leaders?

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

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

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

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

Omnichannel vs. Human-Centric: Getting the Balance Right

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

Trust, Tone, and Transparency: Designing AI That Works

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

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

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

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

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

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

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

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

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

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

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

The numbers paint a sobering picture:

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

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

So, what is the alternative?

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

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

And the results?

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

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

How to Mitigate the AI Burnout Trap

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

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

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

What is the AI doing in Hands-free Conversations?

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

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

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

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

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

Reduced time spent on admin

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

Reduced keying errors

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

Improved accessibility

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

Implementation Considerations

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

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

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

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

Measuring Success

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

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

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

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

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

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

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

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

What is the AI doing to detect vulnerability?

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

Key benefits: Categorisation and risk scoring

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

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

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

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

Record accuracy

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

Compliance alerts

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

Real-time vulnerability detection

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

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

Implementation Considerations

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

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

Measuring Success

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

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

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

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

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

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

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

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

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

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

What is the AI doing in Auto QA?

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

Let’s examine the benefits.

Key benefits: Comprehensive coverage

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

Resource optimisation

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

Consistent evaluations

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

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

Real-time feedback

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

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

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

Implementation considerations

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

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

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

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

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

Measuring Auto QA success

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

In summary, the benefits are:

· 75% reduction in QA processing time

· 50-100 x increase in evaluated interactions

· 15-25% increase in evaluation accuracy and consistency

· Greater and faster improvement in agent performance and CSAT

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

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

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

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

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

1. Autowrap / Call Summarisation

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

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

2. Auto QA (Quality Assurance)

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

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

3. Auto Coaching

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

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

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

4. Identifying Vulnerable Customers

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

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

5. Agent Assist

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

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

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

6. Hands-Free Conversations

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

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

7. Full Voice AI

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

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

Final Thoughts: Think “value first, tech second”

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

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