AI adoption in customer experience is accelerating across Europe. But scaling successfully requires more than adding automation to existing processes. Frank Sherlock, VP International at CallMiner, explains why organisations need to start with business outcomes, customer intelligence and a clear understanding of where AI should automate, augment – or hand over to humans.
Frank, could you briefly introduce yourself, your role at CallMiner and the perspective you bring to the discussion around AI in customer experience?
Frank Sherlock: I have spent my entire career in and around technology and the contact centre, so I have witnessed many changes in CX technology over the years. As VP International at CallMiner, my role is to ensure that we deliver on our commitments to customers, create meaningful value for them and grow our business across EMEA with new enterprise clients.
I bring a customer-facing perspective from working with organisations across Europe that are focused on turning AI into measurable business value. The conversation has moved beyond experimentation. Leaders now want to understand how AI can improve customer satisfaction, efficiency, agent performance and overall business performance. For me, the opportunity lies in using AI to better understand customers, act on insights faster and scale innovation while keeping humans in the loop.
AI has moved from isolated experiments to broader implementation across customer service and contact centre operations. Where are organisations currently making the most meaningful progress?
We see a lot of headlines about automation, and there is no doubt that automation, when done effectively, can significantly improve customer experience. But the biggest leap forward is not simply about automating more. It is about using AI at the very beginning of the automation journey to understand which parts of the customer experience should be automated, which should be augmented by AI and which should remain with people.
«Organisations should analyse everyday customer conversations to identify the tasks best suited to virtual agents, while allowing human agents to focus on complex, sensitive or high-value interactions.»
That leads to smarter workflows, better allocation of resources and stronger outcomes across customer experience, employee performance and overall efficiency.
From Automation to Strategic AI Adoption
At the same time, many companies appear to be under considerable pressure to scale AI quickly. What is driving this urgency ? Customer expectations, competitive pressure, efficiency targets or something else?
It is a combination of all three, but efficiency alone does not explain the urgency. There has been enormous enthusiasm around AI, and many organisations feel they need to move quickly without always being clear about what they actually want AI to achieve. The risk is that AI becomes a technology-first initiative rather than a business-outcomes initiative. That is where projects can fail.
The organisations making the greatest progress are those that start with the outcomes they want to improve and then identify where AI can support those goals. Customer expectations, competitive pressure and efficiency targets are all important drivers. But beneath these factors is a broader recognition that AI can fundamentally change how organisations understand their customers, support their employees and run their businesses.
CallMiner recently surveyed 200 leaders from CX, contact centres, compliance, risk, governance, security and data protection across Western and Central Europe. What prompted the research, and what did you want to understand beyond the general enthusiasm surrounding AI?
The research was prompted by AI’s rapid transition from experimentation into real customer interactions. Organisations across Europe are already using AI to improve customer experience, efficiency and growth, but adoption alone is not enough.
We wanted to understand whether leaders feel genuinely prepared to scale AI with the necessary trust, visibility and control – particularly when operating across different markets, languages and regulatory environments. In short, the research examined the gap between enthusiasm for AI and organisational readiness to scale it responsibly.
Balancing AI Autonomy and Human Accountability
As AI becomes more autonomous, where should organisations retain human judgement, escalation paths and accountability?
There will always be situations in which human judgement is essential, particularly when interactions involve customer vulnerability, disputes, financial hardship or complex emotional needs. However, that does not necessarily mean every sensitive interaction has to begin with a human agent.
AI and virtual agents can be highly effective and, in some cases, can create an experience that customers perceive as particularly empathetic. For example, we have seen virtual agents achieve higher collection rates than human agents in financial services because some customers may feel more comfortable interacting through a calm, consistent and judgement-free experience. The key is finding the right balance.
Organisations need clear pathways to human agents whenever customers require additional support. At the same time, they should use conversation intelligence to learn from interactions handled by virtual agents. Those insights can help improve virtual-agent performance, strengthen escalation pathways and ensure that human accountability remains in place where it matters most.
For CX leaders who recognise the need to scale AI but are unsure whether their organisation is sufficiently prepared, what should they assess and prioritise first?
The first question I would ask is whether they truly understand what is happening across their customer interactions today. A good place to start is often with low-value, high-volume tasks where automation can make an immediate difference without introducing unnecessary risk.
But those decisions should not be based on assumptions. Organisations should use actual customer conversations to identify repeatable queries, common friction points and tasks that consume significant agent time but do not necessarily require human judgement. Once they have that visibility, they can prioritise the use cases where AI or virtual agents can improve efficiency and reduce effort. AI works best when it is built on real customer intelligence – not guesswork.
For more details on the research findings and insights discussed in the interview, you can access the full report here.
Meike Tarabori
Im Januar 2019 übernahm Meike Tarabori die Position als Chefredakteurin des cmm360, das renommierte Schweizer Magazin für Customer Relations Stars und Service Champions. Als erfahrene Expertin für Marketing und Kommunikation mit Abschlüssen in Business, Marketing und deutscher Literatur hat sie wertvolle Erfahrungen unter anderem bei Unternehmen wie KUKA Robotics und zuletzt beim Cybathlon ETH Zürich gesammelt. Im Rahmen eines umfangreichen Rebranding-Projekts verlieh sie dem cmm360 seine aktuelle, moderne Ausrichtung. Seitdem hat sie nicht nur die Onlinepräsenz des Magazins erfolgreich etabliert, sondern kontinuierlich neue Formate wie die Podcasts «Nice To Meet You», «Meike's Raumzeit» und «ICT Talk» entwickelt. Darüber hinaus fungiert sie als Organisatorin des Schweizer Customer Relations Awards, eine Plattform, die innovative Projekte zur Gestaltung nachhaltiger Kundenbeziehungen und einzigartiger Kundeninteraktionen würdigt und auszeichnet.
