The Emerging Trends of CX in 2024

Published on Jan 12, 2024

The customer experience (CX) landscape constantly evolves, and businesses must stay ahead of the curve to remain competitive. As we look towards the future, we must understand the emerging trends shaping the CX landscape in 2024.

In this article, we’ll explore the top customer experience trends that are expected to dominate the healthcare, ed-tech, and insurance industries in 2024.

The Importance of CX in the Healthcare Industry

The healthcare industry is no exception to the growing importance of customer experience (CX). Providing a positive and personalized CX is crucial in this industry, as it directly impacts patient satisfaction, loyalty, and overall healthcare outcomes.

One of the key trends in CX for the healthcare industry in 2024 is the shift towards patient-centric care. Healthcare providers recognize the need to focus on patient’s needs and preferences rather than adopt a one-size-fits-all approach.

Personalization

Personalization and customization will significantly affect the healthcare industry’s CX strategy. Patients will expect tailored healthcare experiences that address their specific needs and preferences. This could include personalized treatment plans, customized communication channels, and individualized care coordination.

UK-based healthcare company Babylon Health provides personalized care through its subscription-based mobile app. It leverages features such as 24/7 access to virtual consultations with doctors, AI-powered symptom checking, and customized health plans to boost user engagement.

Advanced-Data Analytics

Another vital aspect of CX in the healthcare industry is data and analytics. Healthcare providers can gain valuable insights into patient behaviors, preferences, and health outcomes by leveraging patient data. This data can then be used to improve care delivery, personalize treatment plans, and identify potential health risks.

Several healthcare companies leverage integrations with wearables and IoT devices to provide remote patient monitoring services. With a large amount of data available for each patient, doctors can gain better insights, positively influencing their treatment plans.

CX Trends in the Insurance Industry

The insurance industry has traditionally needed to adopt new technologies faster and adapt to changing customer expectations. However, with the rise of insurtech companies and increasing competition, insurance companies focus on improving the customer experience.

Personalized Policies

Similar to the healthcare industry, personalization will be a key trend in the insurance industry in 2024. Customers will expect insurance policies tailored to their specific needs and lifestyle.

This could include usage-based insurance, where premiums are based on actual usage rather than general risk factors, or personalized coverage options based on individual needs and preferences.

Insurtech firms such as Lemonade, Acko, and Ditto are at the forefront of personalized insurance services with tailored coverage and payment plans to match the needs of evolving users.

Embracing Digital Channels

With the rise of digital natives and the increasing use of technology in everyday life, customers now expect a seamless digital experience from their insurance providers. In 2024, insurance companies must embrace digital channels to meet these expectations.

This could include offering online policy management, digital claims processing, and chatbots for customer service. Insurance companies can improve customer satisfaction and retention by providing a convenient and efficient digital experience.

In India, IRDAI has pushed for the adoption or integration of ABHA by Insurance companies. With a focus on reducing data silos and streamlining processes for the end customer, several insurance companies are adopting the same into their digital systems.

Winds of Change with CX in EdTech

The tech industry is experiencing rapid growth and transformation, and customer experience (CX) is crucial to its success. Here are some key points about the importance of CX in the ed-tech industry

Improving student engagement

CX is essential in the ed-tech industry as it directly impacts student engagement. Ed-tech companies must provide a user-friendly and intuitive platform that encourages students to participate actively in their learning journey. By offering personalized learning experiences, interactive content, and seamless navigation, edtech platforms can enhance student engagement and motivation.

For example, Indian ed-tech firm Takshila Learning provides its students the option to learn through 3D simulations in online classes, gamification to drive motivation in completing quizzes, tests, and surveys, and AI-powered learning assistants, which provide tips, relevant resources, and query resolutions to the students.

Following trends from the past year, many use cases have been built through extended reality technologies such as AR and VR, which promote remote learning. You can find more information in our industry report.

Driving Accessibility and Inclusivity

In the ed-tech industry, CX also focuses on making education accessible to all. By leveraging technology, ed-tech companies can provide learning opportunities to students facing physical, geographical, or socio-economic barriers.

This includes offering multi-language support, closed captioning, and assistive technologies to ensure all students can access and benefit from educational resources.

The Indian government’s initiative Sunbird, built as a digital initiative for learning, provides essential tools for new-age tech firms. The open-source, configurable, and modular digital infrastructure is designed for massive-scale implementation. It has several modules, such as Bhashini, allowing real-time translation into multiple regional languages in India.

Discover how we successfully helped India’s leading online education provider implement Sunbird into their platform.

The Future of CX in 2024

The customer experience landscape constantly evolves, and businesses must adapt to stay ahead of the competition. By embracing emerging trends and leveraging technology, companies in the automotive and insurance industries can provide a more personalized, efficient, and convenient experience for their customers.

In 2024, we expect to see a greater focus on personalization, digital transformation, and the use of technologies such as AI and IoT. By staying ahead of these trends, companies can improve customer satisfaction, retention, and their bottom line.

Generic AI is no longer enough. Domain-specific AI is the new enterprise advantage.

From hospitals to factories to insurance carriers, organizations are learning the hard way: horizontal AI platforms might be impressive, but they’re often blind to the realities of your industry.

Here’s the new playbook: intelligence that’s narrow, not general. Context-rich, not context-blind.
Welcome to the age of domain-specific AI agents— from underwriting co-pilots in insurance to care journey managers in hospitals.

Why Generalist LLMs Miss the Mark in Enterprise Use

Large language models (LLMs) like GPT or Claude are trained on the internet. That means they’re fluent in Wikipedia, Reddit, and research papers; basically, they are a jack-of-all-trades. But in high-stakes industries, that’s not good enough because they don’t speak insurance policy logic, ICD-10 coding, or assembly line telemetry.

This can lead to:

Hallucinations in compliance-heavy contexts
Poor integration with existing workflows
Generic insights instead of actionable outcomes

Generalist LLMs may misunderstand specific needs and lead to inefficiencies or even compliance risks. A generic co-pilot might just summarize emails or generate content. Whereas, a domain-trained AI agent can triage claims, recommend treatments, or optimize machine uptime. That’s a different league altogether.

What Makes an AI Agent “Domain-Specific”?

A domain-specific AI agent doesn’t just speak your language, it thinks in your logic—whether it’s insurance, healthcare, or manufacturing.

Here’s how:

Context-awareness: It understands what “premium waiver rider”, “policy terms,” or “legal regulations” mean in your world—not just the internet’s.
Structured vocabularies: It’s trained on your industry’s specific terms—using taxonomies, ontologies, and glossaries that a generic model wouldn’t know.
Domain data models: Instead of just web data, it learns from your labeled, often proprietary datasets. It can reason over industry-specific schemas, codes (like ICD in healthcare), or even sensor data in manufacturing.
Reinforcement feedback: It improves over time using real feedback—fine-tuned with user corrections, and audit logs.

Think of it as moving from a generalist intern to a veteran team member—one who’s trained just for your business.

Industry Examples: Domain Intelligence in Action

Insurance

AI agents are now co-pilots in underwriting, claims triage, and customer servicing. They:

Analyze complex policy documents
Apply rider logic across state-specific compliance rules
Highlight any inconsistencies or missing declarations

Healthcare

Clinical agents can:

Interpret clinical notes, ICD/CPT codes, and patient-specific test results.
Generate draft discharge summaries
Assist in care journey mapping or prior authorization

Manufacturing

Domain-trained models:

Translate sensor data into predictive maintenance alerts
Spot defects in supply chain inputs
Optimize plant floor workflows using real-time operational data

How to Build Domain Intelligence (And Not Just Buy It)

Domain-specific agents aren’t just “plug and play.” Here’s what it takes to build them right:

Domain-focused training datasets: Clean, labeled, proprietary documents, case logs.

Taxonomies & ontologies: Codify your internal knowledge systems and define relationships between domain concepts (e.g., policy → coverage → rider).
Reinforcement loops: Capture feedback from users (engineers, doctors, underwriters) and reinforce learning to refine output.
Control & Clarity: Ensure outputs are auditable and safe for decision-making

Choosing the Right Architecture: Wrapper or Ground-Up?

Not every use case needs to reinvent the wheel. Here’s how to evaluate your stack:

LLM Wrappers (e.g., LangChain, semantic RAG): Fast to prototype, good for lightweight tasks
Fine-tuned LLMs: Needed when the generic model misses nuance or accuracy
Custom-built frameworks: When performance, safety, and integration are mission-critical

Use Case	Reasoning
Customer-facing chatbot	Often low-stakes, fast-to-deploy use cases. Pre-trained LLMs with a wrapper (e.g., RAG, LangChain) usually suffice. No need for deep fine-tuning or custom infra.
Claims co-pilot (Insurance)	Requires understanding domain-specific logic and terminology, so fine-tuning improves reliability. Wrappers can help with speed.
Treatment recommendation (Healthcare)	High risk, domain-heavy use case. Needs fine-tuned clinical models and explainable custom frameworks (e.g., for FDA compliance).
Predictive maintenance (Manufacturing)	Relies on structured telemetry data. Requires specialized data pipelines, model monitoring, and custom ML frameworks. Not text-heavy, so general LLMs don’t help much.

Strategic Roadmap: From Pilot to Platform

Enterprises typically start with a pilot project—usually an internal tool. But scaling requires more than a PoC.

Here’s a simplified maturity model that most enterprises follow:

Start Small (Pilot Agent): Use AI for a standalone, low-stakes use case—like summarizing documents or answering FAQs.

Make It Useful (Departmental Agent): Integrate the agent into real team workflows. Example: triaging insurance claims or reviewing clinical notes.
Scale It Up (Enterprise Platform): Connect AI to your key systems—like CRMs, EHRs, or ERPs—so it can automate across more processes.

Think Big (Federated Intelligence): Link agents across departments to share insights, reduce duplication, and make smarter decisions faster.

What to measure: Track how many tasks are completed with AI assistance versus manually. This shows real-world impact beyond just accuracy.

Closing Thoughts: Domain is the Differentiator

The next phase of AI isn’t about building smarter agents. It’s about building agents that know your world.

Whether you’re designing for underwriting or diagnostics, compliance or production—your agents need to understand your data, your language, and your context.

Ready to Build Your Domain-Native AI Agent?

Talk to our platform engineering team about building custom-trained, domain-specific AI agents.

Further Reading: AI Code Assistants: Revolution Unveiled

The Emerging Trends of CX in 2024