A customer visits your website, browses a few items, abandons the cart, and later receives a message that reflects exactly what they were considering, at exactly the right moment. When they return, your system remembers their context and offers intelligent assistance without repetition. That’s the power of personalised customer service: an experience that knows the customer before they even ask.
This is what customers now expect as standard. And for organisations across industries, it represents a critical shift: customer experience has become inseparable from personalisation, and personalisation has become inseparable from AI.
why traditional “good service” no longer differentiates
For years, organisations equated good service with responsiveness, politeness, or rapid issue resolution. But customers have evolved. Today, they expect brands to understand them, anticipate their needs, and deliver experiences tailored to their individual context.
A global survey underscores the shift: 71% of customers expect companies to understand their unique needs, and 76% are frustrated when they don’t receive personalised experiences. This expectation cuts across sectors: retail, BFSI, telecom, healthcare and even government services. Personalisation has become a determinant of loyalty, not a value-add.
Yet, many organisations struggle with fragmented data, legacy systems, and inconsistent service across channels. Without an integrated AI-enabled foundation, efforts remain surface-level. Such attempts no longer suffice.
AI-powered customer experience: transforming data into scalable, human-like service
AI now sits at the centre of modern customer experience because it solves the scale and speed challenges that human teams cannot. It identifies patterns across millions of interactions, infers customer intent, predicts churn, and personalises journeys in real time.
Companies leading in personalisation generate 40% more revenue from these activities than their competitors. AI-powered customer experience enables organisations to deliver four core capabilities:
- real-time relevance
- proactive engagement
- frictionless omnichannel experiences
- scalable personal touches
Instead of relying on static segmentation, AI models update customer profiles continuously. Every click, query, delay, or hesitation becomes an input that adjusts recommendations and next steps.
AI can predict delivery delays, detect churn risk, flag unusual activity, or sense dissatisfaction from sentiment signals. It enables organisations to act before a customer raises a concern.
Customers no longer choose a single channel. They move from app to chat to store fluidly. AI ensures context travels with them, eliminating repetition and enabling effortless transitions.
AI automates tasks that enhance personalisation, from tailored messages to curated product suggestions, while reserving human expertise for complex or emotional interactions.
This blend of automation and empathy aligns with how modern customers want to be served: quickly for simple needs, thoughtfully for complex ones. When executed well, this creates a service model that feels intuitive, responsive, and genuinely helpful, at scale.
how to personalise customer service: a practical framework for organisations
Personalisation demands a strategic, enterprise-wide approach. Organisations seeking to scale personalisation must consider the following pillars:
unify and activate customer data
Personalisation is impossible without integrated data. Organisations should consolidate CRM, e-commerce, support, loyalty, and behavioural inputs into a single customer profile that updates in real time.
embed AI into the customer journey
AI tools should support journey orchestration, predictive engagement, routing, sentiment detection, and self-service. This is where the shift from reactive to anticipatory service truly happens.
design omnichannel consistency
Every channel must reflect the same understanding of the customer. A unified CX architecture ensures the conversation doesn’t “reset” when channels change.
maintain human-AI balance
AI can triage, predict and assist, but human agents deliver empathy and judgment. Designing the right balance protects both experience quality and operational efficiency.
measure value, not volume
Instead of tracking traditional activity metrics (tickets closed, emails sent), leading organisations evaluate improvement in:
- Customer lifetime value
- Retention and repeat purchases
- Cost to serve
- Net Promoter Score (NPS)
- Conversion and cross-sell uplift
This ensures that personalisation drives tangible business outcomes.
managing risks: privacy, over-automation and relevance
Customers are open to personalisation but only when it feels respectful and genuinely useful. Poorly executed AI can do more harm than good.
Organisations should guard against:
- Over-personalisation that feels intrusive
- Inconsistent recommendations
- Lack of transparency around data usage
- Generic automation replacing human nuance
- Channel experiences that contradict one another
Clear consent management, responsible data practices, and human oversight remain essential to building trust.
how can Infosys BPM help you lead the next wave of personalised CX?
As AI reshapes customer experience, Infosys BPM helps organisations move from reactive service to proactive, predictive, and hyper-personalised engagement. Through its Digital Contact Centre, omnichannel CX, analytics-led personalisation, sentiment-aware automation and AI-enabled journey orchestration, Infosys BPM builds the data, technology and governance foundations needed for autonomous customer journeys and real-time personalisation, ensuring organisations unlock scalable value and stay ahead of evolving customer expectations.
Frequently asked question
- Why is traditional “good service” no longer enough to differentiate customer experience?
- How does AI enable real-time, personalised customer service at scale?
- What are the key building blocks of an AI-first personalisation framework?
- How should organisations balance AI-driven personalisation with privacy and trust?
- What risks should leaders plan for when scaling AI-powered personalisation in customer service? <
Customers now expect brands to understand their context, anticipate needs, and tailor interactions in real time rather than just respond politely or quickly. Without AI-enabled personalisation across channels, service feels generic and fails to build loyalty even if response times are acceptable.
AI analyses behaviour, intent, and history across millions of interactions to update profiles continuously and recommend next best actions in the moment. This supports proactive outreach, dynamic offers, and context-aware assistance that feel “human-like” while remaining operationally scalable.
Core pillars include unified customer data, AI embedded in journey orchestration and routing, omnichannel consistency so context travels with the customer, and a deliberate balance between automation and human support. Organisations must also measure value through outcomes such as CLV, retention, NPS, and cost to serve not just ticket volumes.
They should avoid intrusive targeting, ensure transparency about data use, and give customers control over preferences and consent. Strong governance, clear guardrails, and human oversight help prevent over-automation or irrelevant recommendations that erode trust.
Key risks include over-personalisation that feels “creepy,” inconsistent experiences across channels, and generic automation replacing necessary human nuance. Mitigating these requires responsible data practices, model monitoring, and design choices that keep people in the loop for complex or emotional interactions.
