AI and Customer Experience & How to Integrate Them in 2024

Learn how to craft a seamless AI Customer Experience, its advantages, models and implementations in this in-depth guide.

Issac Thomas
February 6, 2024
10 min read

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What is AI customer experience? 

AI customer experience is the deployment of artificial intelligence across the customer journey to deliver a personalized, seamless experience to the customer, however, and wherever they interact with your business.  

Despite the initial challenges of time, cost and technological complexity in integrating AI in CX, the outcomes empower companies to deliver unprecedented levels of personalization at a scale that was unimaginable just a decade ago. The result is a data-driven approach that anticipates the customer’s needs, tailors the support experience and ultimately transforms the customer experience landscape. 

Evolution of AI in customer experience 

CX was a miniscule business aspect a few decades ago until COVID-19 set in and catapulted it into the league of key business differentiators. Today, CX is the top grosser for businesses, even ahead of pricing and product, according to Globalnewswire. However, this evolution didn't happen overnight; it underwent five major milestones detailed below: 

  1. It started with rudimentary systems like ELIZA in the 1960s, offering a glimpse into the potential for human-like interactions.  

  2. The 1980s saw the integration of interactive voice response (IVR) systems in inbound contact centers and call centers, empowering callers to self-solve routine issues and seek information via a predefined set of menus and recorded scripts. The internet era introduced early chatbots, and the 1990s witnessed the rise of natural language processing (NLP), enabling more intuitive interactions. The internet era introduced early chatbots, and the 1990s witnessed the rise of natural language processing (NLP), enabling more intuitive interactions.  

  3. Machine learning (ML) in the 2010s transformed support tools, constantly improving responses through data analysis. Predictive analytics and sentiment analysis infused personalization and customer empathy to CX. Conversational AI became a mainstay of AI customer support, adding efficiency and scale with a human touch. 

  4. In the late 2010s and early 2020s, generative AI emerged, allowing dynamic, real-time responses, and revolutionizing digital customer experience.  

Learn more: Understand how generative AI improves CX 

Traditional CX vs. AI CX 

Traditional CX relies on manual processes to better customer experiences, while AI CX leverages artificial intelligence, transforming customer experiences with automation, personalization and predictive capabilities for unparalleled efficiency and innovation. Below are the major differences between them.  


Traditional CX 



Primarily human-agent interactions 

Mix of human and AI-driven interactions 

Response time 

Relies on human speed and efficiency 

Rapid and automated responses 


Limited by human working hours 

24/7 availability for automated support 


Restricted by manual customer profiling 

Dynamic and deep personalization, driven by real-time customer behaviors 

Handling complexity 

Limited ability to handle complex queries like highly technical issues that require a trained expert 

Can handle intricate tasks like technical troubleshooting with machine learning as AI customer experience learns constantly and continuously with incoming data sets 

Learning and adaptation 

Human agents learn from experience and static data 

AI systems continuously learn and adapt from real-time customer interaction analytics and behavioral data 


Dependent on human workforce scalability 

Easily scalable with automation and AI 

Cost efficiency 

Costs associated with recruiting and training human labor 

Potential for cost reduction with automation, enhanced productivity and efficient resource allocation 


Slower to adapt to changing trends 

Adapts to changes leveraging machine learning and agile algorithms, ensuring relevance in dynamic markets 


Reactive approach to customer needs 

Proactive and preventive, anticipating and addressing issues 

Feedback Handling 

Manual handling of feedback 

AI systems can automate the analysis of feedback, extracting valuable insights at scale 

7 AI-driven customer experience technologies 

To craft an impeccable AI customer experience, you need to have some foundational AI technologies to deploy at each customer touchpoint and integrate consumer insights, processes and teams. 

Here are key AI technologies transforming the customer experience landscape: 

#1. Natural Language Processing (NLP) 

NLP enables computers to comprehend human language, including colloquialisms and emojis. It powers virtual agents and AI-driven chatbots for round-the-clock, sophisticated customer service. It comprises two sub-tasks - Natural Language Understanding (NLU) for extracting meaningful information from unstructured data and Natural Language Generation (NLG) for synthesizing coherent and contextually relevant human-like text, such as articles, summaries or responses 

#2. Sentiment analysis 

Sentiment analysis detects emotions in data to gauge customer perceptions of a brand or service. It analyzes feedback from platforms like Trustpilot, social media comments, customer surveys and news sources to provide valuable insights into customer sentiment for informed decision-making and brand reputation management

#3. Predictive analytics 

Predictive analytics understands patterns in customer behavior to anticipate future needs. It helps in optimizing sales, logistics, supply chain and brand promotions. For example, retailers use predictive analytics to anticipate footfall patterns based on location, events or seasons, ensuring adequate staffing and inventory in stores. 

#4. Deep learning 

A subset of machine learning, deep learning involves neural networks with multiple layers that can learn and make complex decisions. It is often used in tasks such as image and speech recognition, contributing to enhanced customer interactions. 

#5. Speech recognition 

Speech recognition technology converts spoken language into written text. This technology powers voice-activated virtual assistants, making it easier for customers to interact with devices and systems using their voices. 

#6. Robotics process automation (RPA) 

RPA involves using software robots to automate repetitive and rule-based tasks. In customer service, RPA can streamline processes, reducing response times and improving efficiency. 

#7. Computer vision 

Computer vision facilitates image recognition and Optical Character Recognition (OCR) for pattern detection in image-based big data. It identifies celebrities, brands and products on social media for targeted advertising and competitive analysis. It is utilized for diagnosing customer issues through image recognition. 

How to bring artificial intelligence and customer experience together  

Integrating artificial intelligence with customer experience requires strategic planning. You need access to customer data, robust IT structure and the best AI technologies to enhance your CX with AI. Follow the below steps to take your CX to the next level with AI customer experience.

1. Develop a CX Strategy with AI at the forefront

Begin by aligning your team's understanding of your CX vision and how AI can help you achieve it. Discuss expectations and methods to meet them. Based on the long-term vision and yearly organizational goals, deploy the right AI tools and build an efficient ecosystem to craft an incredible AI customer experience. 

2. Map user journeys

Understand user interactions across discovery, pre-sales, sales and support. Mapping it will help you create an effective AI customer journey. Invest time comprehensively mapping touchpoints to deliver an AI-based omnichannel customer experience. 

Learn more: Understand the customer journey mapping to serve them better

3. Assess the data infrastructure  

Examine your data architecture and evaluate if it can support AI integration and if it is robust and flexible. A solid foundation is critical for AI to analyze and generate reliable outputs

How Microsoft outsourced their CX management for big gains 

Microsoft faced the challenge of managing its extensive social media presence amidst a vast volume of data, totaling 115 million mentions annually. To address this, they sought a solution to consolidate social operations and streamline workflows disrupted by multiple-point solutions. Microsoft chose Sprinklr's Unified Customer Experience Management (Unified-CXM) platform, transforming its Customer Experience Center (CXC) into the hub for social engagement. Read the full story here >> 

4. Evaluate AI solutions 

Before finalizing AI solutions, analyze if the data is AI-ready using these four parameters: 




Check accuracy, completeness and consistency for reliable AI training and predictions. 


Assess if data aligns with AI project objectives for meaningful insights and performance. 

Volume and Variety 

Ensure sufficient data volume and diverse types for robust model training and understanding. 

Accessibility and Security 

Verify data accessibility and prioritize security to protect sensitive information, complying with privacy regulations. 

Recognize the various AI forms – Recommendation engines, Virtual assistants, Predictive search engines, Computer vision, and Sentiment analysis tools. Determine which technologies align with your business model. AI forms like recommendation engines help suggest personalized content, virtual assistants help with hands-free assistance and computer vision helps with signature verification and fraud prevention.  

Point to note: In this stage, beware of picking multiple-point solutions for different AI functions like sentiment analysis, predictive analytics, and more. The resulting insights and data will be fragmented, giving you a distorted picture of your present customer experience. It’s advisable to invest in a holistic, AI-driven customer experience management platform that offers all these capabilities and unifies insights and data seamlessly. 

5. Choose a build/buy approach 

Decide whether to integrate AI into your existing application or invest in a pre-made CX/AI solution. Building is suitable with a qualified in-house team or a reliable AI services partner, while buying is profitable with time constraints and vendor expertise.

6. Integrate with existing systems 

Seamlessly introduce AI into your current tech stack, like contact center CRMs, communication channels, analytics tools, etc. It should enhance, not disrupt, your ongoing operations. 

7. Monitor and optimize 

Incorporating AI is not the final step. You need to deploy customer experience analytics. Regularly evaluate and refine your strategy for ongoing improvement and a more effective future.

8. Stay informed on AI ethics  

As you implement AI, stay updated on the evolving standards and regulations related to AI ethics and data privacy to ensure compliance. Understand that “Responsible AI” is the intersection of trust, partnership and integrity between brands, vendors and consumers and is vital to all public-facing companies. 

Best ways to improve customer experience with AI for businesses 

AI customer experience impacts every touchpoint in the buying journey and, in the process, gives a tremendous boost to the marketing, sales and customer service efforts of a company in the following ways. 

#1. Marketing 

Marketers are adopting AI to optimize their marketing campaigns, and more than 40% of marketers are using AI for content production. Let’s discuss broad marketing use cases.  

  • Tailored campaigns and targeting 

AI harnesses vast datasets to unveil trends in customer behaviors, needs and preferences. This empowers marketers to swiftly perform customer profiling, audience targeting and content creation with precision, adapting to preferred customer service channels and moments of need. 

Insights for better customer experience
  • Automated email marketing 

 When it comes to email outreach, AI serves as a reliable ally, offering accurate customer segmentation and personalized content recommendations. This ensures that tailored messages reach the right audience at the opportune moments. 

Email augmentation in AI customer experience
  • Diverse customer engagement models 

At the core of marketing lies relationship building, where AI excels in crafting customized customer engagement models for diverse audience groups. Some of them are -  

  1. High-touch model  

In this model, customers have direct access to dedicated account managers who have personalized, one-on-one interactions with them. Enhance training and knowledge-sharing among account managers to ensure they provide expert-level guidance.  

2. Low-touch model 

This model automates processes and minimizes manual intervention with the use of self-service options. Continually update self-service resources and offer chatbots for immediate assistance. 

Interesting read: A definitive guide on AI self-service for 2023-24  

3. Retention model 

This model focuses on engaging and satisfying existing customers to reduce churn rates. Implement data analytics to identify customers who may be at risk of unsubscribing and tailor retention efforts based on their needs.  

4. Touch model 

A customer success manager manages client portfolios and offers guidance and support as required. Implement predictive analytics to help the CSM in addressing potential customer issues ahead of time.  

5. Hybrid model 

This model blends personal support for high-value customers with automation for others to create an effective customer service model. Segment your customers effectively to deliver personalized support to the right people. 

This tailored approach fosters meaningful connections and enhances the overall customer experience. 

#2. Sales 

Sales professionals are using AI to increase the probability of sales. Organizations saw a 50% increase in sales leveraging AI tools in their sales process. 

  • Personalized sales outreach 

Modern customers crave personal connections, and AI facilitates dynamic content creation tailored to individual customer profiles. Whether through emails or offers, personalized content, especially on crowded social media, captures attention.   

💡Sprinklr Pro-tip: Social sellers leveraging AI-powered solutions, such as Sprinklr AI+, produce customized posts that resonate with specific audience segments. 

JPMorgan Chase partnered with AI startup Persado to explore AI-generated ad copy. Contrary to expectations, the AI-produced content surpassed human-written copy, achieving double the click rates in many instances. It led to a remarkable 450% increase in click-through rates.  

  •  Upselling with tailored messaging 

Given soaring customer acquisition costs, brands turn to AI for more targeted upselling and cross-selling to existing customers. AI surfaces relevant product recommendations and messaging, tapping into proactive social listening to understand customer needs.  

With the advantage of engaging with known brands, upselling becomes more accessible than targeting random prospects. AI in sales transforms the approach, making it strategic, personalized and results-driven. 

#3. Customer service 

Organizations are using AI tools to enhance customer service. In fact, deploying AI chatbots was predicted to help businesses save 2.5B hours of work by 2023.  

  • 24/7 customer responses 

AI-driven self-serve tools like chatbots and voice bots provide round-the-clock support, handling multiple conversations concurrently. This ensures quick responses to routine queries, elevating customer engagement and satisfaction. 

24x7 availability AI customer experience
  • Accurate sentiment and intent detection 

AI tools adeptly detect sentiment and intent, allowing support teams to navigate conversations agilely, manage emotions and prevent escalations. These tools intelligently handle out-of-context queries, ensuring a smooth digital customer experience.    

Intent identification in AI customer experience
  • AI-led routing for efficient resolution 

AI facilitates the routing of cases to agents based on their skills, bandwidth, and experience. This ensures efficient resolution as supervisors objectively calibrate the team's proficiency level, assigning cases to agents best equipped to handle them.     

AI-score based routing - AI in customer experience
  • Consistent, on-brand communication 

Omnichannel bots respond in channel-native language while adhering to brand guidelines, creating a consistent brand experience across all touchpoints. This harmonious messaging builds trust and brand recall, cutting through the noise for increased visibility.  Your customers deserve exceptional AI customer experiences. That's where Sprinklr AI+ comes in. 

Planet Fitness's incredible transformation 

Planet Fitness, a prominent fitness center franchise, aimed to elevate its customer experience across multiple touchpoints. Partnering with Sprinklr in 2020, they sought a unified platform for social media management, customer service and brand reputation.  


Planet Fitness grappled with the surge in customer inquiries across diverse channels, impacting response times and overall service quality. In June 2023, they joined Sprinklr AI+ beta to explore AI's potential in enhancing member experience, workflow, and consumer insights. 


AI+ became a personal assistant for 38 social media care representatives, tailoring responses and refining Social Listening queries. It empowered them to save time, maintain consistent messaging, and proactively address issues, ensuring a seamless and responsive engagement. 


Sprinklr AI+ significantly lightened the workload, elevating response quality. Features like "Make it Longer" and tone modification enhanced customization, striking a balance between speed and precision. The focus on response quality markedly improved the overall customer experience. 

If you want to transform your customer experience with AI, take a free trial, and we will show you how to curate bespoke journeys for every customer. 

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