The second half of 2025 saw a sharp acceleration in conversations about AI. While AI dominated the conversation at our roundtable, one theme cut through consistently: a growing disconnect between business ambition, technological momentum and the real needs of customers. 

Boards are often pushing for rapid returns or exercising extreme caution. Technology vendors are promising transformation. Yet CX metrics still struggle to reward loyalty and long-term value, leaving CX leaders to reconcile competing pressures with limited levers. 

Against this backdrop of uneven readiness for next-generation CX, we also heard clear examples of organisations making progress. Those succeeding are addressing these tensions through stronger governance, better-aligned metrics and more collaborative partner models. 

This paper draws directly on those discussions to surface the CX challenges that matter most in 2026 and beyond, and to share practical experience on how to address them. 

When AI holds up a mirror to CX

AI is revealing the true state of customer experience. Where journeys are well designed, data is connected and governance is clear, automation delivers value. Where those foundations are weak, AI simply scales existing problems faster. 

Leaders shared examples of blanket automation strategies being rolled back, CX teams managing downstream fallout from decisions they did not own, and metrics that reward efficiency while quietly destroying long-term value. At the same time, we also heard from organisations getting it right, moving quickly but thoughtfully through clear ownership, outcome-based metrics and strong change management. 

Why alignment now matters more than ever

As CX becomes increasingly hybrid, with human and AI blended across journeys, legacy thinking starts to break down. Traditional operational metrics struggle to explain value. Governance models lag behind technology. Cyber and data risks grow quietly in the background. And CX leaders are often held accountable without the authority to influence decisions upstream. 

The organisations that will win in the next phase of CX are those that: 

• Put strategy and use cases before technology 

• Treat CX as a value multiplier, not just a cost centre 

• Align boards, technology, CX and partners around shared outcomes 

• Build solid data and security foundations before scaling AI 

• Measure what truly matters to customers and the business 

From fast adoption to sustainable advantage

This whitepaper explores the real decision gaps holding organisations back and offers practical guidance on how to close them. Drawing directly from practitioner insight, it covers governance, metrics, partner models, change management and cyber security, alongside seven practical steps CX leaders can take now. 

Transformation is not optional. But speed alone is not success. The real inflection point is whether organisations can align people, metrics and leadership quickly enough to make AI work for customers and commercial outcomes alike. 

The whitepaper is free to download and available below. 

We would love to continue the conversation. Follow us on LinkedIn and share your experiences.

Much of the conversation about the future of customer contact is dominated by technology.
AI. Automation. Analytics. Bots. Faster. Cheaper. Smarter.

Yet when we step back and look honestly at where organisations are struggling, the challenge is rarely technology-first. It is people-first.

Most businesses already know what needs to change. The harder truth is that knowing does not reliably translate into doing. Strategy decks are written, tools are procured, pilots are launched – and still the outcomes lag behind ambition.

That execution gap sits squarely in the people layer.

The Gap Between Knowing and Doing

Across sectors, the pattern is remarkably consistent. Leaders understand that customer expectations are rising, that work is becoming more complex, and that traditional operating models are under strain. Teams on the ground feel it every day.

And yet progress often stalls.

This is not because organisations lack capability or intent. It is because many are still trying to solve today’s problems with yesterday’s assumptions. Training models remain largely ‘one and done’. Roles have evolved faster than the support structures around them. Managers are asked to lead a more complex, emotionally demanding workforce while being measured on metrics designed for a simpler world.

Holding the line on those legacy measures creates lagging outcomes – first for employees, and then inevitably for customers.

Future Fit organisations recognise that execution failure is rarely a technology issue. It is a people issue, reinforced by culture, incentives, and leadership capability.

The Agent Role Has Already Changed

The frontline role in customer contact is no longer primarily transactional.

Routine interactions are increasingly handled through automation, self-service, or deflection. What remains with humans is more demanding:

• Edge cases that fall outside standard rules
• Emotionally charged conversations
• Complex judgement calls
• Moments where reassurance, interpretation and empathy matter most

Yet many organisations are still hiring, training and measuring agents as if the job has not fundamentally changed.

Future Fit thinking starts with a simple acknowledgement:
the agents we need now – and in the future – are different.

They need stronger judgement, emotional intelligence, and confidence navigating ambiguity. They also carry a higher emotional load, often in remote or hybrid environments where informal support and loyalty are harder to build.

If we do not redesign roles, support, and leadership around this reality, burnout becomes structural rather than incidental.

AI’s Role: Reducing Friction, Not Replacing Humans

In a Future Fit model, AI is not the solution. It give the power to unlock it.

Used well, AI should make work more human, not less. That means:

• Removing friction from the agent day
• Reducing cognitive load
• Surfacing the right knowledge at the right moment
• Guiding decisions without dictating them
• Supporting judgement rather than automating it away

This requires intentional design. Clear purpose for each use case. Strong guardrails around how data and insight are used. Ongoing development rather than ‘set and forget’.

Crucially, it also requires honesty with employees. When AI is positioned as something being done to people, resistance is inevitable. When it is designed and communicated as something done for them, adoption follows. Ideally, it should be co-created with employees – let them see their fingerprints all over the final solution.

Future Fit organisations understand that AI does not remove responsibility from leaders. It increases it.

Metrics will set the mindset of employees – they send the message about what matters most. And these mindsets shape behaviour.

Culture Follows Metrics

One of the most common failure points in transformation is misalignment.

Technology changes. Roles change. Customer expectations change.
But metrics stay the same and still the investment in people lags.

Future Fit organisations are ruthless about asking:

• Do our measures reflect the work we actually want people to do?
• Are managers incentivised to enable value, or simply control volume?
• Are we measuring activity, or outcomes?

As technology takes care of the repeatable, human value becomes the differentiator. That demands new definitions of productivity, stronger coaching capability, and leadership that understands how to create space for quality, not just speed.

Culture does not shift through slogans. It shifts through what is rewarded, tolerated, and prioritised.

From Transformation Programmes to Continuous Evolution

There is no finish line.

Future Fit organisations do not treat change as a programme with an end date.

They treat it as ongoing evolution:

• Continuous improvement rather than big-bang transformation
• Listening deeply to employees as well as customers
• Taking analytics upstream to fix root causes, not mask symptoms
• Investing in leadership capability alongside platforms and tooling

They also recognise a hard truth: AI can paper over cracks — or expose them. The difference lies in whether organisations are willing to look honestly at how work is really done.

The Question That Really Matters

Future Fit is not about asking, “What technology should we buy?”

It is about asking:

• Why do customers come to us?
• Why do they stay?
• What do our people need to deliver on that promise – today and tomorrow?

Get the people element right, and process and technology fall into place.

Get it wrong, and no amount of AI will save you.

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)

This Location Watch report draws on insights from Ryan Strategic Advisory’s May 2025 CX Technology and Global Services Survey (Peter Ryan, 2025) and ArvatoConnect’s Onshore-Offshore: Why the CX Value Equation is Changing (James Towner, 2025). As well as CCP’s relationship with scores of UK outsourcing decision makers and over 240 global BPOs.

The Rise of Offshoring

Since the 1990s, offshoring has become a dominant trend in business process outsourcing. Companies initially turned to India for its low labour costs, English proficiency, and large talent pool. In the 2000s, India was joined by the Philippines as another low-cost hub, particularly suitable for customer service and voice-based operations, leveraging its Western (especially US) cultural alignment. More recently, South Africa has gained attention for its quality, favourable time zones, and relatively lower cost base compared with Europe, providing a viable alternative for UK and European clients.

Yet, despite the global rise of offshore destinations, the UK has maintained its position as a key outsourcing market, valued not for cost alone but for quality, governance, and operational reliability. Its mature infrastructure, strong compliance standards, and professional capability continue to make the UK a premium outsourcing environment, where strategic partnerships prioritise service excellence and trust over purely economic considerations.

Value-Driven Outsourcing Partnerships

In 2025, UK enterprises show a clear preference for value-driven outsourcing partnerships that combine advanced technology capabilities with proven operational excellence. Ryan Strategic Advisory’s May 2025 CX Technology and Global Services survey found that AI proficiency, know-the-customer analytics, and competitive pricing are now the top three competitive differentiators for BPO providers. UK buyers emphasised the importance of strong client references and sector-specific expertise, underscoring the country’s preference for relationship-based, high-governance engagements.

Budget Stagnation and Operational Challenges

A notable trend emerging in the UK market is budgetary stagnation. Over 60% of UK CX leaders indicated that their 2025 budgets will remain flat or decline. This is accompanied by concerns over agent attrition and declining service levels, particularly in voice and digital delivery channels. As a result, many UK enterprises are reassessing delivery models, prioritising investment in AI, automation, and analytics to improve productivity without sacrificing quality. The consequence is a heightened focus on “cost-neutral transformation”, shifting spend from headcount to enabling technologies without increasing overall CX budgets.

Research also highlights that poor AI rollouts can alienate agents: 26% of UK contact centre staff are considering leaving due to unclear AI integration strategies, emphasising the need for transparent change management and training (ArvatoConnect, 2025, Impact of AI on Agents).

Onshoring and Reshoring Trends

While offshoring continues to feature in many delivery strategies, particularly to India, the Philippines, South Africa and Egypt, the latest research indicates that some UK buyers are developing a renewed focus on onshore delivery. ArvatoConnect’s 2025 findings report that:
• 73% of UK brands would choose to onshore CX if cost were not a factor
• 34% are actively planning to reshore services that were previously relocated overseas within the next year.

Key drivers behind this transition include:
• Improved staff retention (31%) and access to local talent and cultural familiarity (26%)
• Customer preference for localised support (26%) and better service quality (21%)
• Simpler management structures, regulatory confidence, and access to advanced technologies (25%)

Correctly planned and executed, onshoring is increasingly seen as a future-proof strategy rather than nostalgia. Proximity improves employee engagement, cultural alignment, customer trust, and ensures tighter compliance control, especially for highly regulated industries.

AI, Automation, and Cost Parity

This rebalancing reflects a shift from a cost-driven model to one focused on resilience, agility, and customer intimacy. AI and automation are now reducing the cost of UK-based service delivery by up to 30%, narrowing the traditional economic advantage of offshore operations:
• AI-powered digital agents in the UK: £16 per hour
• Offshore human agents: £15–£17 per hour (depending on which location)
This near-parity redefines the value equation for outsourcing decisions.

Strategic Insights from ArvatoConnect

As ArvatoConnect’s Chief Growth Officer, James Towner, notes:
“Offshoring’s economic promise is fading. Today’s smartest brands are strategically resetting and planning to reshore customer experience for cultural alignment, talent retention, customer preference, and tech-driven agility.”

The emerging model blends 70% digital/AI interactions with 30% human advisors, focusing human talent on empathy, compliance, and complex issue resolution.

Hybrid and Onshore Investments

Ryan Strategic Advisory’s global survey observed limited enthusiasm for expanding offshore capacity among UK enterprises. Instead, organisations are investing in hybrid and onshore models, leveraging automation and analytics to enhance efficiency.

• BPOs are re-emphasising UK delivery centres in cities such as Manchester, Glasgow, and Newcastle
• Investments are going into next-generation CX hubs integrating AI, cloud contact platforms, and multilingual service delivery

And as ArvatoConnects research suggests, providers are piloting AI-enabled ‘micro-hubs’ that balance cost efficiency with high-quality onshore delivery, while maintaining compliance and engaging the local workforce. Of course, the most innovative offshore BPOs are just as focused on automation and AI-driven investment as their UK peers, but technology may be serving to ‘level the playing field’”

Conclusion: The UK’s Resilient Outsourcing Ecosystem

The UK’s BPO and onshoring landscape combines technological sophistication, regulatory stability, and deep sectoral expertise, creating a solid foundation for high-value service delivery.

As brands continue to prioritise data protection, cultural coherence, and high-quality service, the UK’s position as both an outsourcing and reshoring leader is set to strengthen through 2026 and beyond. In the mid-term, the integration of automation, AI support for agents, and reductions in volumes and handling times will provide an opportunity to bring more operations closer to home. This positions the UK not just as a premium delivery location, but as a cost-efficient, technology-enabled alternative to traditional offshore destinations.

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.

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.

In our earlier whitepaper, we explored how AI adoption is reshaping customer contact – an area in which great risk and reward intersect. Six months on, the case for agentic AI has grown stronger, particularly in sensitive customer interactions where honesty and trust are essential.

Drawing on academic research and industry data, we now understand that AI can do more than just automate processes. It can unlock “more honest” conversations, especially in situations where fear of judgment by others or shame might inhibit disclosure.

This isn’t just a theory! Research from Stanford, MIT CSAIL, and NUS Business School reveals a striking trend: people are more open with AI than with human agents in contexts like mental health, financial distress, addiction, and relationship issues.

Why?

Because AI doesn’t judge.

Stanford calls this the social desirability bias, where people moderate their speech based on perceived perceptions. Removing this perception leads to greater honesty.

The ‘confession booth effect’, a term coined by NUS, also demonstrates this. In anonymised AI conversations, people admitted behaviours they hid from humans, like not reading terms and conditions or sharing passwords. In an insurance use case, initial disclosure accuracy rose by 40% when AI agents led the conversation.

MIT CSAIL found that people expend less mental energy managing impressions when talking to AI. This frees cognitive bandwidth for self-reflection and better problem-solving.

Now taking this approach, judgment-free AI agents can be implemented in high-trust, high-friction industries, such as mental health screening, legal triage, financial support, and trust & safety work. These artificial agents are more scalable and effective than humans.

The paradoxical truth is that people often feel more ‘heard’ by AI than by humans, because they don’t feel the need to pretend.

Yet traditional AI platforms struggle with emotional nuance, privacy, and secure escalation. It is essential to overcome these hurdles with strict compliance (GDPR+), contextual accuracy, and human-aligned escalation protocols.

The case for AI grows when combined with market data

Perhaps considered in the context of the long forecast demise of voice as a channel, recent research from the Synthflow white paper brings together a number of key usage stats which when considered with the findings of the academic research support the notion that AI voice will be here to stay?

 

What does all this mean for CX leaders?

• Trust is key. Sensitive topics require the psychological safety of customers to be part your AI solution.
• Design your AI agent around customer fears, not just FAQs.
• Measure resolution accuracy and emotional sentiment, not just AHT.
• Voice AI, when built correctly, can be the most honest channel for customers.
• Integrated agentic AI ensures a consistent experience across platforms.

Customers don’t need AI to sound human. They need AI to “feel safe”. The leading approach to agentic AI will redefine what honest, efficient, and compliant customer interactions can be – especially in a world in which truth drives trust, and trust drives revenue.

Organisations that have adopted AI in their contact centres have often seen significant improvements, such as halved response times, 40–70% operational cost reductions, and increased contact handling capacity. However, as some partners have noted in their recent engagements with members of the the CCP team, these gains can be followed by a flattening curve and then a performance dip, if not implemented correctly.

We have revisited our February 2025 whitepaper, ’2025: A Year of Difficult Conversations’, in which we explored how AI, automation, and digital transformation would drive new operational and ethical challenges in customer contact. We previously highlighted the tension between cost optimisation, customer experience, and why thoughtful project governance will be required.

We thought it would be good to consider what may have changed and what lessons should be revisited.

Six months of continued observation and implementation across the market have revealed risks that automation without the appropriate planning and controls can have on your future operating model are more nuanced. While short-term AI gains are impressive, traditional approaches may erode long-term value through burnout, agent attrition, and customer dissatisfaction. This is the ‘AI Paradox’: the risk that productivity gains today may fuel tomorrow’s operational decline.

Beneath the surface, a gradual yet detrimental erosion of the human layer is occurring. Collaborating with AI often leads to front-line staff experiencing reduced recovery time, increased complexity in remaining ‘manual’ queries, and escalating customer expectations. Without adjustments to team structure, support, or metrics, burnout becomes a growing threat.

This productivity half-life, a period where efficiency peaks and subsequently declines due to human strain, is no longer merely a theoretical risk. Businesses are starting to witness this AI-driven degradation in tangible figures: within 18 months of implementing traditional AI, attrition rates rise by 65%, customer satisfaction scores decline by 20-30%, and agent engagement scores fall concurrently as the technology matures.

Agentic AI presents a more sustainable alternative. Instead of perceiving AI as a replacement for human input, CCP’s partners are illustrating how task-completing AI agents can alleviate the burden on agents, facilitate judgment-free conversations, and ensure capacity for the most significant human interactions when needed. Consequently, it not only yields improved outcomes for customers but also contributes to enhanced retention, reduced training expenses, and a more resilient workforce.

Mitigating the AI Burnout Trap: Lessons from the Last Six Months

• Implement phased AI rollouts with human impact measures.
• Adopt agentic AI that empowers humans, preserving judgment for complex cases.
• Shift success metrics from AHT to FCR, CSAT, and agent engagement.
• Involve agents in AI workflow design and iteration.
• Regularly audit the AI-human balance: check whether tech amplifies or exhausts people?
• Track attrition, training costs, and productivity when calculating your ROI.
• Lead with transparency and ethics when deploying conversational automation.

Make certain you are on the right course

In short: if your AI roadmap doesn’t include agent wellbeing, then you’re building in risk. Efficiency must be sustainable, not just measurable.

Six months on, the market is beginning to learn this the hard way. The good news? There’s still time to course-correct. The AI paradox isn’t inevitable it’s just the result of decisions made without the full picture.

If you’d like to discuss in more detail how you can leverage the experience of our team and our partners, then feel free to contact us.

The health & wellbeing sector has always been rooted in human connection. Whether it’s supporting someone on their fitness journey, guiding a patient through treatment, or reassuring a customer about a sensitive health concern, the role of empathy is central.

But as the industry expands fuelled by digital-first healthtech, growing demand for wellness subscriptions, and rising consumer expectations, customer contact teams are under strain. The question for leaders is clear: how can we scale, stay compliant, and still deliver a deeply human experience?

From Cost Centre to Care Hub

Contact centres in health & wellbeing have traditionally been seen as a cost to control. Yet every conversation from a dietary query to a mental health support call has the potential to strengthen or weaken customer trust.

Forward-looking organisations are reframing service operations as a growth driver. For example, brands in this sector are exploring:
– Streamlined renewals and cancellations to reduce friction in subscription journeys.
– Smart routing for repeat callers, ensuring recurring issues are addressed quickly.
– Consistent omnichannel service so customers feel supported whether they call, chat, or message via an app.

When the contact centre is positioned as a core part of the brand experience, it moves beyond cost reduction and becomes a foundation for loyalty.

Automation with Empathy

The volume of routine contacts in this sector is significant – booking appointments, tracking deliveries, resetting passwords, updating payment details. These are tasks that can be handled by AI and digital workers, delivering instant, 24/7 responses.

The real opportunity is in blending automation with human empathy:
– Real-time agent assistance: AI surfaces the right knowledge at the right moment, helping advisors answer health or wellbeing queries accurately and sensitively.
– Vulnerability detection: AI can flag signs of distress in a caller’s tone or language, prompting the advisor to adapt their approach or escalate where appropriate.
– Conversation wrap-up & QA: Every interaction can be automatically summarised, with 100% of calls checked for compliance and quality, giving leaders confidence that standards are met consistently.

This partnership between people and technology doesn’t replace the human connection. It amplifies it, giving advisors the space to focus on empathy while automation handles repetitive, time-consuming tasks.

Scaling Securely & Sustainably

The growth in health & wellbeing services from digital fitness programmes to home diagnostics demands agile operating models. Customer demand can spike rapidly, whether during seasonal health peaks or major product launches.

To stay ahead, organisations are:
– Leveraging flexible sourcing models (nearshore, offshore, hybrid) to expand capacity quickly and cost-effectively.
– Adopting workforce management (WFM) tools to optimise scheduling and keep wait times short.
– Embedding compliance and security (PCI DSS, GDPR, sector-specific regulations) to ensure every interaction is safe and brand-protective.

By combining these capabilities, health & wellbeing brands can scale without losing sight of what matters most: trust, care, and the customer’s wellbeing journey.

The Strategic Shift Ahead

The contact centre is no longer just a helpdesk. It is becoming the front line of wellbeing experiences, where automation drives efficiency, and skilled advisors deliver empathy. For leaders in the sector, the challenge (and the opportunity) is to design operations that are both sustainable and human-centred.

At Customer Contact Panel, we connect organisations with over 220 delivery providers and 115 technology partners. We help health & wellbeing brands navigate their options, align technology with their customer journeys, and build resilient, customer-first operations fit for the decade ahead.

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.