Compliance & Governance Archives - Customer Contact Panel

The subscription economy has become the preferred commercial model in the digital era. From its early pre-digital beginnings supported by Standing Orders and Direct Debits, the acceleration in adoption happened because three powerful trends converged:

The digitisation of products and services
The ability to store payments credentials securely
The recognition within the investment community that recurring revenue is far more valuable than transaction revenue

Today, regulators, card schemes, banks and consumer protection agencies are introducing controls designed to place consumers firmly in control of the recurring payments underpinning the global subscription economy. Although regulatory approaches differ the UK, European Union and North America are all moving towards a common objective:

Consumers should retain effective control over recurring payments throughout the entire subscription lifecycle.

This principle is driving reforms relating to transparency, consent, authentication, renewal notices, cancellation journeys and dispute rights.

So how should organisations react? What does a reasonable response look like to respond to change and maintain revenue flows whilst reducing cost and risk?

To answer that, let’s look at what is driving change globally. Perhaps the most significant changes have come not from regulators but from the card brands themselves. The distinction between Customer Initiated Transactions (CITs) and Merchant Initiated Transactions (MITs) sits at the centre of modern subscription governance. A CIT occurs when a customer actively authorises a transaction. The initial subscription purchase normally falls into this category. An MIT occurs when the merchant subsequently charges the customer using previously obtained authority. Subscription renewals, annual renewals and usage-based billing are common examples.

Card schemes increasingly require merchants to demonstrate a clear link between the original CIT and subsequent MITs. In addition, Visa and Mastercard Stored Credential Frameworks require merchants to identify and categorise recurring transactions, maintain evidence of consent and provide stronger consumer disclosures.

As well as demanding more from their merchants, the card schemes are making it easier for consumers to request a ‘chargeback’. Historically subscription disputes were resolved between merchants and consumers. Increasingly consumers bypass merchants entirely and seek refunds through their card issuer. Issuers now possess more transaction data, improved recurring payment visibility and stronger dispute rights. Less control for merchants brings more and higher costs.

When we look specifically at geographical markets the UK has emerged as one of the most active subscription regulation markets. The Digital Markets, Competition and Consumers Act introduces enhanced consumer protections including reminder notices, cooling-off rights and simplified cancellation mechanisms. In addition, The Competition and Markets Authority has also signaled increasing scrutiny of subscription retention practices. The UK is additionally positioned to become a leading market for Open Banking Variable Recurring Payments which may begin to challenge card-based subscription models, even without any chargeback mechanism being in place to protect the consumer.

Irrespective of the markets organisations (merchants) operate in, those that are going to thrive in this new pressurised environment will have three things in common:

Firstly, a customer engagement platform capable of analyzing customer interactions across all engagement activity, irrespective of customer engagement channel, across all business outcomes, right through to full payment history. Which means having the ability to view and analyze data across historical data silo’s to identify and deliver targeted conversations with customers and prospects capable of delivering desired outcomes
Second, a payments capability that is absolutely focused on delivering more customer payments, more easily and at less cost. Which means leveraging a new generation of payment gateway functionality capable of orchestrating payments across multiple merchant acquirers in real time to uplift payments acceptance rates and reduce transaction costs. It also means (for those trading in the UK) adjusting to card scheme mandates for no PAN on cards beyond 2030 and migrating stored payment credentials to card scheme network based tokenisation.
And finally, a broad and robust data governance capability embracing consumer rights and protections, regulatory compliance, third party oversight and merchant acquirer contractual obligations (PCI DSS compliance).

Recurring revenue remains attractive. Consumer convenience remains attractive. However, what we are likely to see in the next 12 to 36 months is greater cost and risk pressure on established workflows and business process supporting the subscription model.

Recurring revenue and convenience that cannot withstand regulatory scrutiny, issuer challenge or consumer dispute is increasingly becoming recurring risk.

Guardrails amidst the chaos

The world of AI – perhaps especially in the contact centre and customer experience space – is clouded with exaggerated claims, disputed evidence and unqualified ‘experts’. Everyone talks about guardrails, but there’s no settled agreement as to what is and isn’t reasonable or acceptable. So legal regulations and requirements would presumably be helpful in defining some foundational guardrails? But at a federal level the US government is active opposed to any AI-specific regulation and in the UK, while the government points to its ‘sector led’ approach, there are no plans for an overarching AI law.

However, it’s very different in the EU where the AI Act has been law since 2024 and will be fully implemented from this August. The Act is often described as “the world’s first comprehensive AI legislation”.

In which case, if you’re based in the UK, North America, Africa or Asia you may well be thinking “why should I care? That doesn’t affect me”.

If so, you’d be wrong; very wrong.

Why the EU AI act matters to you

No EU presence? It doesn’t matter. If you have EU customers you will need to comply with the Act’s requirements. And even if you don’t your EU suppliers will. And even if you don’t have EU suppliers or partners, it’s quite likely that in the global regulatory vacuum the EU AI Act will become a default standard, similarly to the way GDPR did for data protection.

While the odds of your organisation being prosecuted under the Act are low, bear in mind that fines are at GDPR levels. So, for the gravest transgressions you’d be looking at €35 million or 7% of global annual turnover.

What does the Act say?

Unsurprisingly, the Act’s quite lengthy, but there are some key highlights to get your head around:

The definition of AI:

”a machine-based system designed to operate with varying levels of autonomy, capable of adapting after deployment, and generating outputs such as predictions, recommendations, content, or decisions that can influence physical or virtual environments”

What’s ok and what’s not:

Unacceptable Risk AI: Banned totally

Social scoring, manipulative AI, and biometric categorisation based on sensitive traits are prohibited

⚠ Watch Out
Use of black-box AI for things like fraud prevention or dynamic pricing could put you at risk.

High-Risk AI: Strict new controls in place

Applies to recruitment, education, healthcare, credit scoring, policing, and safety-critical infrastructure

Requirements: Detailed risk assessments, transparency, human oversight, and conformity checks before launch

⚠ Watch Out
Don’t assume you’re exempt. Even seemingly innocuous recruitment screening tools could fall within the scope of these rules.

General-Purpose & Generative AI: New obligations

Foundation models (like ChatGPT or image generators) must ensure transparency, appropriate labelling AI-generated content, management of systemic risks, and clarification of the use of copyrighted data

Limited-Risk AI: Transparency required

Chatbots and similar tools must clearly inform users they’re interacting with AI

⚠ Watch out! Many voice bot providers currently advise clients to hide the fact that customers are interacting with machines. This will need to change – even as it becomes increasingly hard for customers to tell.

Minimal-Risk AI: Largely unaffected by the Act

Spam filters, video game AI, and similar tools are mostly out of scope of the Act

Who carries the liability?

As you might expect, the Act differentiates between AI developers (providers) and AI users (deployers).

Developers (providers) are liable for ensuring that AI systems comply with the Act’s requirements, including safety, transparency, traceability, and respect for fundamental rights. They are specifically liable if harm results from software defects, cybersecurity vulnerabilities, or algorithmic discrimination.

Users (deployers) are responsible for the legal operation of AI under their control. They need to ensure proper monitoring, human oversight, and adherence to transparency obligations are in place. Users will be held liable if harm occurs due to misuse, failure to supervise, or neglecting operational safeguards.

This means there is less scope for commercial partners to attempt to contractually ‘offload’ legal obligations onto their customers or suppliers than we often see in the realm of data protection. Added to which, when so many organisation and service providers are taking the opportunities to adapt and build upon AI foundation models, they may find themselves legally regarded more as developers than users.

What’s to be done?

What’s clear is that the use of AI in customer experience and contact centres in alignment with the EU AI Act isn’t a one-team or one-time task. Organisations need to truly understand where AI is being used, to achieve what and how. This isn’t just a job for the compliance team. Tech, data, proposition, digital, risk, pricing, finance, legal and customer experience colleagues all need to be involved. And to stay involved as AI solutions innately change and develop over time.

It’s a classic cross-functional business change project, but one that is likely to spur or reflect significant changes in business rules and structures – as well as needing to become embedded into ‘business as usual’ processes.

Need Help Navigating the EU AI Act?

At Customer Contact Panel, we help organisations find and successfully implement compliant, effective AI solutions, so you can innovate with confidence and accountability.

Drop us a line and we’d be happy to have a chat.

If you’re looking for a practical breakdown of what the EU AI Act requires and how to prepare, read our detailed guide:

EU AI Act Compliance: What Every Business Needs to Know and Do

BPO operations are built for constant change. Contracts ramp quickly, headcount fluctuates, and compliance expectations never ease, all while margins remain under pressure. Yet one area that underpins all of this is still rarely treated as strategic infrastructure: how devices are provisioned, managed, and recovered at scale.

For many BPOs, device management sits in the background. It is often fragmented across internal IT teams, multiple suppliers, and manual processes that have evolved over time. While this may work at smaller volumes, it quickly becomes a constraint as operations scale.

When Devices Slow Performance

In high-velocity BPO environments, time is directly linked to revenue. Delays in device onboarding mean agents cannot go live, training investment is underused, and programmes lose momentum. Offboarding creates equal risk. High churn and contract changes mean devices leave the estate constantly, and without tight controls this exposes data, compliance, and asset recovery issues.

Alongside risk, cost quietly increases. Idle devices, unnecessary new purchases, repeated configuration work, and reactive support all erode margins, often without being visible as a single problem.

Why Traditional Models Fall Short

Many BPOs still rely on capital-heavy purchasing or piecemeal provisioning models that were never designed for workforce volatility. Buying new devices for each ramp-up ties up capital and leaves surplus stock when demand drops. Limited asset visibility makes it difficult to track usage, ownership, and compliance, especially as remote and hybrid delivery models expand.

A Managed, Circular Alternative

An increasing number of BPO providers are adopting a fully managed, circular IT and asset management model. This approach treats devices as operational infrastructure rather than one-off purchases. Devices are pre-configured, securely deployed, supported in-life, rapidly recovered, refreshed, and redeployed based on forecast demand.

The operational impact is clear:

Faster onboarding with ready-to-deploy devices
Reduced risk through controlled offboarding and certified data security
Lower cost per agent by maximising reuse and limiting capital spend
Improved productivity through consistent configuration and support
Measurable ESG benefits through extended device life

Turning a Constraint into a Lever

When device management is treated strategically, it stops limiting growth and starts enabling it. Onboarding accelerates, offboarding becomes predictable and auditable, and costs align more closely to active headcount rather than peaks and troughs.

In a market where speed, compliance, and efficiency define success, rethinking device management is no longer optional. A modern, circular approach to IT and asset management is becoming a foundational capability for BPOs looking to scale without adding risk or complexity.

Want to know more?

The Missing Link in BPO Operations (Branded)

AI regulation is no longer just a tech or compliance issue, it’s becoming a boardroom priority.

In the US at a federal level the government seems to be actively opposed to AI regulation and in the UK, despite an interesting Private Member’s Bill, there’s no sign of any overarching AI law. But while the US and UK are still debating their approaches, the EU is ahead of the game with the world’s first comprehensive AI law: the EU AI Act. If you do business in or with Europe, this will affect you.

Why Should You Care?

No EU presence? Doesn’t matter. If you have EU customers or suppliers, you’ll likely be contractually required to meet the Act’s standards

Remember GDPR? The EU’s data privacy rules became the global benchmark. Expect the AI Act to have a similar impact

The Risk-Based Framework: What’s In, What’s Out

1. Unacceptable Risk: Banned

Social scoring, manipulative AI, and biometric categorisation based on sensitive traits are prohibited
Watch out: Using “black box” AI for things like fraud prevention or dynamic pricing could put you at risk

2. High-Risk AI: Strict Controls

Applies to recruitment, education, healthcare, credit scoring, policing, and safety-critical infrastructure
Requirements: Detailed risk assessments, transparency, human oversight, and conformity checks before launch
Don’t assume you’re exempt: Even apparently innocuous recruitment screening tools could be caught by these rules

3. General-Purpose & Generative AI: New Obligations

Foundation models (like ChatGPT or image generators) must ensure transparency, label AI-generated content, manage systemic risks, and clarify use of copyrighted data

4. Limited-Risk AI: Transparency Required

Chatbots and similar tools must clearly inform users they’re interacting with AI.
Heads up: Many bot providers still advise clients to hide from customers that they’re talking to machines —this will need to change

5. Minimal-Risk AI: Largely Unaffected

Spam filters, video game AI, and similar tools are mostly out of scope

The Compliance Challenge

For UK and global businesses, the message is clear: even without local laws, EU standards will shape your obligations. Cross-border operations will face growing compliance pressure, just as they did with GDPR.

Balancing Innovation and Compliance

The real challenge? Staying innovative while meeting new regulatory demands. Businesses must:

Identify which AI systems are in scope (which will include understanding exactly which parts of the business are using AI, to do what)
Ensure transparency and risk management
Be ready to demonstrate compliance to customers and partners

Need Help Navigating the EU AI Act?

At Customer Contact Panel, we help organisations find compliant, effective AI solutions—so you can innovate with confidence and accountability.

For a broader perspective on why the EU AI Act matters globally and how it is shaping AI governance beyond Europe, see:

The EU AI Act: The only show in town – and tickets are mandatory!

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.

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

What is the AI doing in Pure Voice AI?

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

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

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

Key benefits: 24/7 service

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

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

Smoothing demand

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

Multi-lingual cover

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

Implementation considerations

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

What can we expect to see?

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

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

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

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

What’s next? More of the same, that’s what! As we all know, that’s the nature of the regime; it’s here for keeps.

We know from the FCA’s reviews of firms’ mandatory Consumer Duty Board Reports that their initial assessment of the industry’s response to the requirements of Consumer Duty has been broadly “ok for starters, but you can try harder”!

The FCA’s update on its review of the Consumer Duty rules promised some simplification and removal of some arguably unnecessary, prescriptive requirements (in line with the Treasury’s ‘cut red tape’ agenda), but the range and depth of the permanent change in the treatment of customers that the FCA wants to see will remain.

This is to be expected and no doubt most firms are focused on the FCA’s specific callouts for Board Report improvements such as:

Improving the quality of data – and the insights derived from it
More fully reflecting the needs of different consumer cohorts, especially those with vulnerabilities
Ensuring that boards are challenging – and seen to be challenging – the business to meet the Consumer Duty’s requirements
Clarity on the timescale, action owners and data to drive planned improvements

But one further area for improvement will be particularly relevant to colleagues in the customer experience and/or contact centre space – “Comprehensive view across distribution chains”.

Yanking the chains

The FCA has long recognised the importance – and potential for the risk of service and experience failure – in distribution and supply chains. Many financial services organisations will have already had to review their supply chains to meet the FCA’s expectations around Operational Resilience.

Meeting the outsourced, sub-contracted and third-party challenge

In the context of Consumer Duty, though, the focus needs to be less on the dangers of total failure than the more subtle risks of poor visibility and exchange of information, and inconsistent consumer treatment and experience.

The way in which financial products and services are sold, delivered and supported can often involve multiple partners in the supply chain – covering sales, payments, customer service, claims, redemptions and other functions.

At nearly every point of the customer journey the way in which consumers are supported and interacted with, both through human-to-human dialogue and automated channels, creates a Consumer Duty risk.

Outsourced and sub-contracted relationships need to be managed to ensure that the standards of consumer data and insight; advisor training and empowerment; online and automated information and decision making; consumer recognition; fairness; and effective compliant recognition and resolution; are all delivered as well as they are in-house. To do so will require a blend of initiatives and efforts, including:

Contracts and service–schedules; contractual management Information and KPIs
Data and information security assurance, including payments (and the news that Marks & Spencer’s recent £300m cyber-attack is being blamed on a 3rd party supplier’s error highlights the criticality of this area)
The quality assurance and provision of guidance and information to both customers and advisors
The ability to share and identify customer profiles and features (especially vulnerability factors)
Advisor training and coaching
The provision of self-serve and assisted support tools and concessionary measures

(and all of these are an ongoing commitment, not just a ‘one time fix’)

In Summary

Managing complex customer supply chains can be tricky at the best of times, but adding in a raft of demanding regulatory expectations and requirements makes it more difficult still.

Have you already met this challenge or are you still assessing how to better go about it? Let us know. Get in touch, we’d love to chat.

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

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

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

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

What is the AI doing to detect vulnerability?

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

Key benefits: Categorisation and risk scoring

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

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

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

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

Record accuracy

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

Compliance alerts

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

Real-time vulnerability detection

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

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

Implementation Considerations

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

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

Measuring Success

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

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

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

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

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

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

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

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

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

What is the AI doing in Auto Coaching?

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

Key benefits: Efficiency

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

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

Coaching quality and consistency

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

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

Personalised rapid development

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

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

Automated role play

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

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

Implementation Considerations

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

Measuring Success

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

In summary, the key benefits are:

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

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

• Higher job satisfaction and reduced attrition among agents

• Higher job satisfaction among team leaders

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

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

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

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

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

What is the AI doing in Auto QA?

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

Let’s examine the benefits.

Key benefits: Comprehensive coverage

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

Resource optimisation

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

Consistent evaluations

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

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

Real-time feedback

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

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

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

Implementation considerations

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

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

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

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

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

Measuring Auto QA success

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

In summary, the benefits are:

· 75% reduction in QA processing time

· 50-100 x increase in evaluated interactions

· 15-25% increase in evaluation accuracy and consistency

· Greater and faster improvement in agent performance and CSAT

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

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