Customer experience has always mattered. What's changed is the cost of getting it wrong.
46% of enterprises now invest more in AI for customer service than any other business function, according to CX Today's 2025 research. 88% of contact centres use some form of AI, and 70% of CX leaders say generative AI has led them to fundamentally re-evaluate their customer experience strategy. The tools have arrived. The question now is whether organisations are deploying them in the right places.
This piece looks at where AI is genuinely transforming CX teams - and where the gap between adoption and results is still wide open.
CX teams have been shifting focus from acquisition to retention for years. The economics are clear: retaining an existing customer costs a fraction of acquiring a new one, and loyal customers generate more stable revenue while driving organic growth through referrals.
What's changed is the expectation bar. Post-pandemic consumers don't just want good service — they want service that feels like it knows them. 71% of consumers expect personalisation, and 76% get frustrated when they don't find it, according to McKinsey research that has only become more relevant since it was published.
The challenge for CX teams is scale. Personalisation at the individual level, across thousands of daily interactions, isn't achievable through manual effort. This is the structural problem that AI is specifically designed to solve.
Personalisation has historically been a promise that CX teams struggle to keep at scale. AI changes the input. Instead of relying on what agents can remember or manually look up, AI algorithms analyse customer history, behavioural patterns, and real-time sentiment to surface the right response, recommendation, or next step at the right moment.
70% of CX leaders now believe chatbots and AI agents are becoming skilled architects of highly personalised customer journeys, according to Zendesk's 2025 research. The shift is from reactive service (responding to what the customer asks) to proactive service (anticipating what they need before they ask).
69% of organisations believe generative AI can help humanise digital interactions — making them feel warmer and more personal, not more robotic. The technology's role is to make scale feel human.
One of the most immediate and measurable impacts of AI in CX is on agent productivity. McKinsey's 2025 research found that GenAI-enabled agents achieve a 14% increase in issue resolution per hour and a 9% reduction in average handle time. Across a contact centre team, those numbers compound quickly.
The mechanism is straightforward. AI handles the cognitive load that doesn't require human judgement: summarising previous interactions, suggesting responses, flagging relevant knowledge base articles, and automating post-call notes. Agents spend less time on administration and more time on the conversations that actually require their skills.
There's a secondary benefit that often gets overlooked. AI-augmented contact centres report a 43% reduction in agent turnover, driven by reduced burnout and more meaningful work. When agents aren't spending their day on repetitive admin, they're more engaged, more effective, and less likely to leave.
For contact centre leaders managing the perpetual challenge of attrition, this is a significant operational return that doesn't show up in CSAT scores but absolutely shows up in recruitment and training costs.
Beyond individual interactions, AI enables CX teams to identify and fix the structural problems in their customer journeys. Predictive analytics surface where customers are most likely to drop off, escalate, or churn. Sentiment analysis across thousands of interactions reveals recurring pain points that wouldn't be visible in any individual conversation.
The opportunity here is significant - and largely untapped. Only 30% of contact centres are currently using AI to generate operational insights, and just 27% are using it within knowledge management. Most organisations are using AI to handle individual interactions, but not yet to improve the underlying processes that generate those interactions in the first place.
The cost case is compelling. Gartner benchmarks the median cost per self-service contact at £1.84, versus £13.50 for agent-assisted interactions — a 7x difference. Every interaction that AI resolves without agent involvement represents a direct cost reduction. Every process improvement that reduces contact volume compounds that saving over time.
The ROI case for AI in CX is well established - but it's contingent on implementation quality. 92% of businesses report improved CSAT after implementing AI, but 66% of businesses took more than six months to see measurable ROI from their AI implementations, according to Verint research. The tools work. Deploying them correctly takes expertise.
The Dialpad AI Contact Centre case illustrates what well-implemented AI looks like in practice. After deploying AI across their contact centre operations, Dialpad achieved over 380% return on investment - driven by improved agent productivity, reduced time spent on coaching, cost savings from lower turnover, and higher revenue from reduced downtime. The ROI wasn't from a single capability. It was the compounding effect of AI improving multiple parts of the operation simultaneously.
Research from Accenture and Deloitte shows that organisations deploying AI at scale realise 71% of their total ROI in years two and three, as systems mature and agent-AI collaboration improves. Year one is about getting the foundation right.
Ready to explore what AI can do for your CX and contact centre operation? Fortay Connect helps UK organisations identify the right AI use cases, select the right platforms, and deploy them in a way that delivers measurable results. Get in touch to start the conversation.
1. How is AI transforming customer experience teams?
AI transforms CX teams by automating repetitive tasks (note-taking, routing, post-call summaries), enabling personalisation at scale through real-time data analysis, and surfacing operational insights that improve the underlying customer journey. The result is faster resolution, higher CSAT, and agents freed to focus on complex, high-value interactions.
2. What ROI can businesses expect from AI in customer service?
ROI varies by implementation quality and use case. Gartner benchmarks self-service at £1.84 per contact versus £13.50 for agent-assisted, making deflection a direct cost saving. Dialpad achieved 380% ROI from a well-implemented AI contact centre deployment. Accenture and Deloitte research shows 71% of total ROI typically materialises in years two and three as systems mature.
3. Why do so many AI CX implementations fail to deliver results?
The most common failure is the adoption-integration gap: 88% of contact centres use AI, but only 25% have fully integrated it into daily workflows. Tools that aren't connected to existing systems, trained on relevant data, or adopted by agents don't generate returns. The gap between having AI and using it effectively is where most ROI is lost.
4. How does AI improve agent performance in contact centres?
AI improves agent performance by reducing cognitive load: auto-generating response suggestions, surfacing relevant knowledge base content, summarising previous interactions, and automating post-call notes. McKinsey found GenAI-enabled agents resolve 14% more issues per hour and reduce handle time by 9%. Reduced admin burden also correlates with a 43% drop in agent turnover.