AI in Customer Experience for Businesses to Garner Loyalty, Reduce Churn, and More!

Who doesn’t like repeat customers? None that we know of. 

Yet, a broad estimation states that between 25%-45% of customers never make another purchase after their initial transaction, across different sectors. 

The reason behind this churn can be attributed to multiple factors. But one that makes a world of difference is customer experience. In fact, businesses that have implemented “AI in Customer Experience” have witnessed significant improvements in their efficiency, customer loyalty, revenue positivity, and overall customer satisfaction.

To add, here are some stats to help you stem the idea in your head:

• Around 90% of companies are using AI to enhance their customer experience, and in fact, by the end of 2025, it is estimated that 95% of all business customer interactions will have some element of AI.
• Companies that are using AI-powered services have reported a return of $3.50 for every $1 invested.
• AI-powered customer support has the capacity to reduce resolution time by 52%.
• Approximately 80% of customers have reported a positive experience after using an AI-powered customer service. 
• ServiceNow reported an 80% reduction in customer inquiries that were handled by AI agents, leading to a reduction of 52% in handling complex cases and millions in productivity gains.

To win, ensuring customer experience remains a vital part of sustaining long-term loyalty. And, with the inclusion of AI for the aforementioned purpose, things have become much easier.

But the road to implementation is as difficult as it can get, since no good thing comes easy. And, this is the reason a lot of companies struggle with the idea of “AI for Customer Experience.” 

Not to worry, because through our years of hustle in the IT industry, we have been able to aggregate all the critical data that can help our clients. And, critical data that can help you. So, if you’re serious about using AI for enhancing your CX, read on.

What is AI in Customer Experience?

The term ‘AI in customer experience’ is kind of self-explanatory. But, for your ease, let’s define it:

“Artificial intelligence in CX (Customer Experience) is using AI to understand, anticipate, and even respond to customer needs for multiple touchpoints.”

Basically, instead of relying on a human agent or rigid workflows, businesses take the help of AI to get faster, personalized, and scalable insights and responses to improve their customer experience.

How Artificial Intelligence for Customer Experience Differs from Traditional CX Solutions?

There are more than a few ways AI in customer experience is different from traditional CX solutions. However, instead of all taking the long route, we have decided to explain that to you in the easiest and shortest way possible. Below is a table to achieve the same:

What are Some Key AI Technologies in CX?

Here’s a list of AI technologies that are used for CX improvement. All of these technologies are currently being utilized by businesses to increase their customer experience throughput:

1. Natural Language Processing (NLP) → Chatbots, sentiment analysis, and voice recognition systems are developed using this technology.
2. Machine Learning (ML) → It learns from your customers’ behavior and helps you recommend products/services that may be a better fit for them.
3. Chatbots & Virtual Assistants → Helps in providing 24/7 support, giving benefits like self-service, resolving FAQs instantly, etc.
4. Predictive Analytics → Helps in anticipating churn, buying intent, or support needs for the customer. Further on, it helps in coming up with watertight plans based on historical business, for taking the next steps to business profitability.
5. Voice Recognition → This enables hands-free customer interactions, secure authentication, and personalized voice-driven experiences. It is also great from a WCAG compliance perspective for people with disabilities like blindness.

How AI Can Improve Customer Experience? – Benefits of AI in Customer Experience

There are multiple reasons why ‘AI in customer experience’ has spread its charm across industries, across the globe. Putting our nail on the few most important ones, here are the reasons businesses are taking the help of AI-enhanced customer experience:

• Personalization at Scale: It helps create tailored product recommendations, content, and, that too, real-time, which creates a higher propensity of target penetration.
• 24/7 Support: It is done by making use of chatbots, self-service portals, and voice assistants. These are always available, and with their help, businesses are able to avoid the cost and hassle of managing a 24/7 operational customer management team.
• Predictive Insights: As shared earlier, AI engines and models can factor in your historical data and anticipate customer needs, thereby preventing churn and recommending the next action to support business goals.
• Faster Resolutions: Traditionally, an operator was required to undergo tons of paperwork to resolve a customer query. Not today, with AI in customer experience, smart query routing, instant FAQs, and AI-assisted agent responses can be leveraged.
• Omnichannel Experience: Businesses use multiple channels to either market or sell their product. However, in a traditional environment, consistency always took a toll. But, with present-day AI, context-aware interactions across chat, email, voice, and social are possible.
• Sentiment Analysis: With the help of AI technologies like NLP, it is possible to detect the emotion behind a conversation. And, this can also help adjust the tone to improve the empathy of the statement.
• Fraud Prevention & Security: In the current massive data pools, figuring out suspicious activities manually can become close to impossible. However, with AI, triangulation of these suspicious activities is very much possible and that too faster, helping you retain the trust of the customer.
• Process Automation: In a business, there are several processes, like data entry, order tracking, refunds, etc., that are repetitive in nature. These can be automated with the help of AI agents for smoother CX.
• Voice & Speech AI: This technology delivers hands-free interaction and authentication using natural voice recognition.
• Feedback Analysis: AI is quick on its feet to process data from surveys and reviews, identifying and delivering to you areas of improvement.

AI Customer Experience Examples Across Industries + Real World Examples

AI in customer experience is a transformative power that has the capacity to directly affect multiple industries. And, it is doing so. So, just to help you gauge what all transformations are being made to what capacity, we have mentioned some examples of how artificial intelligence for customer experience can be used.

Also, we have shared a few real-world examples where this solution is being utilized in coalition, for the industries mentioned below.

1. E-commerce

Artificial intelligence and customer experience in e-commerce can help with several AI enhancements. In major, these enhancements would include product discovery, personalization, and customer support.

Real-World Examples:

• Amazon: The company uses AI to recommend products to its customers. For this, it processes their browsing and purchase history + intent.
• Flipkart: It offers AI-driven visual search, chatbot support, and tailored deals (quite similar to Amazon).
• Shopify (AI plugins):  AI is used by Shopify to automate abandoned cart recovery and for customer segmentation.

2. Banking & FinTech

In banking & fintech, AI for customer experience can actually improve security. Also, it can help with self-service support and even customer engagement.

Real-World Examples:

• HDFC Bank’s EVA: For the bank, the AI virtual assistant handles millions of queries from its customers.
• JPMorgan Chase: JPMorgan Chase uses customer experience artificial intelligence for fraud prevention and transaction monitoring to avoid any suspicious activities.
• Paytm: AI chatbots deployed by Paytm are primarily used for support, bill reminders, and any sort of dispute resolution that is required.

3. Healthcare

In healthcare, AI in customer experience is mainly used for patient interaction, diagnosis support, and offering triage (symptom checking, appointment prioritization, patient routing, etc.).

Real-World Examples:

• Babylon Health: Uses an AI-driven symptom checker and delivers telehealth assistance through AI. 
• Ada Health: The healthcare brand offers AI-based self-assessment tools for its patients.
• Mayo Clinic: AI helps match patients with available resources and specialists.
  

4. Telecom

In telecom, the CX improvement through AI is usually done to improve responsiveness and reduce dependency on human agents.

Real-World Examples:

• Vodafone’s TOBi: They have a chatbot that handles tasks like billing, upgrades, and support queries.
• AT&T: The company uses AI to predict outages and suggests any possible proactive resolutions.
• Verizon: AI-powered live chat and troubleshooting guides for customers.

5. Outsourcing & BPO Integration

Many outsourcing providers actually use AI to enhance their CX delivery across industries. In fact, the way these companies use AI in customer experience is multifaceted. Here are a few examples:

• They create AI-powered contact centers that have multilingual chatbots and virtual agents.
• Predictive analytics is used by these companies to reduce possible churn and even improve service quality.
• Sentiment analysis assistance for humans during live interactions.
• Automated workflows for tasks like refunds, onboarding, ticketing, and feedback loops.

Real-World Examples of Integration:

• Accenture: They design AI-driven CX models for other industries like banking, retail, and healthcare.
• Genpact: Genpact uses AI-assisted agent tools for faster resolutions within its project workflows.
• TCS & Wipro: Both these companies have built custom AI CX solutions for Fortune 500 clients.

What are the Metrics & KPIs used for assessing the Effectiveness of AI in CX?

Simply taking advantage of AI in customer experience won’t cut the deal. Instead, you need something to measure the effectiveness of the implementation. Therefore, below, we have provided tables of different types of metrics & KPIs (Key Process Indicators) that will help you assess the aforementioned.

📊 1. Customer Satisfaction Metrics

⚡ 2. Operational Efficiency Metrics

🌐 3. Usage & Adoption Metrics

💬 4. Experience & Sentiment Metrics

📈 5. Business Impact Metrics

Why Businesses Turn to Outsourcing for AI-Driven CX?

Utilizing AI in customer experience can be excruciatingly difficult for most organizations, especially for first-timers. However, with the help of an outsourcing company, this gap is bridged.

Why are we saying that it is excruciatingly difficult? Well, it requires specialized skills like data science, system integration knowledge, and compliance knowledge. So, even if a company ends up forming its teams for the aforementioned tasks, the other challenge is to vet them to deliver the desired results.

To add here are a few other reasons why most businesses choose the outsourcing route, as opposed to creating their own in-house teams:

• Ensuring compliance with different data privacy regulations like GDPR, HIPAA, etc.
• Outsourcing companies help blend human expertise with AI automation. This ensures a balance between empathy and efficiency that works.
• Provide cost-effective scalability, which is crucial for enterprises, especially those that handle high interaction volumes.

• Forming a team requires a high upfront investment, which involves expenses like AI platforms cost, data infrastructure cost, tool licensing, etc.
• Deployment timelines can be longer in comparison to an outsourcing company, where you need to build models, integrate legacy systems, test workflows, and so on.
• Connecting AI with your CRMs, ERPs, contact centers, and communication channels also requires heavy backend work and is complex + time intensive.
• Without proper expertise, projects are always at the cusp of underperformance or failure of measurable ROI.

Business Impact to Expect:

The business impact that one could expect after getting an AI-enhanced customer experience solution from their outsourcing business partner is:

• Decrease in terms of cost support.
• Increase in CSAT (Customer Satisfaction) score.
• Faster query resolution for customers.
• Handling of customer issues using AI without escalations.

How to Use AI for Customer Experience? – Implementation for Businesses!

Now, as far as the implementation is considered for any business prospect, there are a series of steps that one can take. The steps would obviously differ, considering even similar use cases differ for businesses because the value they derive from it differs. 

So, in this section, we have tried to provide generic steps that we at NewAgeSys use for creating our initial plan of implementation. So, let’s begin:

1. Identify CX Gaps & Use Cases

The first step of implementation is not about the technology but the impact you are looking for.

Therefore, start with auditing your customer journeys. This will help you figure out pain points, delays, high ticket volume, low satisfaction, and things like that. Use prioritization models like RICE, MosCow, etc., to select which features to work on first, and prioritize resources.

2. Define Clear Goals & KPIs

Now, based on your previous inference, set measurable outcomes. This could include metrics & KPIs like reducing response time, improving CSAT (Customer Satisfaction Score), NPS (Net Promoter Score), churn rate, etc.

3. Choose the Right AI Tools & Platforms

At this stage, we decide the AI tools and platforms that we will use based on factors like scalability, integration, and ROI. This would help you evaluate whether to build, buy, or outsource.

4. Integrate with Existing Systems

Assuming that your company has existed for a while now, it is highly likely that there will be a few systems in place. For example, CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), contact center platforms, ticketing tools, etc.

Connect them using APIs, middleware, or outsourcing the work to an integration specialist with the AI solution.

5. Prepare Quality Data

One of the major gripes that people have with integrating AI into their systems is finding good data for the system to be fed with. It’s not like you will be exporting data from an external source (not true for all cases). However, in most cases, the data stored for different siloes within an organization largely remains unstructured in nature. And, for the systems to train, you need to feed them clean, tagged, and structured historical data.

In fact, once the systems are laid out, you also need to figure out a way to feed real-time data to the systems. Additionally, there are challenges related to compliance management for compliance laws like GDPR, HIPAA, CCPA, etc., that oversee data collection from customers, and will be resolved in this stage.

6. Build Human + AI Collaboration

The aim of implementation should be a system that blends support teams with intelligent automation. For this, you need to train agents to work with AI copilots and assistive tools. Also, set up escalation workflows for complex or empathy-driven issues.

7. Deploy in Phases (Start Small)

Avoid full-scale disruptions.

• Run pilots in one channel or department.
  
• Collect feedback and improve before expansion.

Start deploying the solution. However, it should be done in phases. You run pilots in one of the channels or departments, collect feedback, and improve things before expansion. This would help you avoid any full-scale disruptions that could create problems later in the future. And most importantly, it won’t directly impact your customers. 

8. Monitor, Measure & Optimize

Start tracking the performance of the solution. Review KPIs like containment rate, resolution time, sentiment scores, etc., and use feedback loops to retrain models for any inaccuracy and holistic improvement.

9. Ensure Security & Governance

Add access controls, encryption, audit trails, etc., to maintain as much transparency as possible and employ fair data use policies. 

This would not only protect customer data but also your reputation. 

10. Scale & Evolve

Once all the above steps are fulfilled, it’s time to expand your solution to new channels, regions & languages, and new workflows.

Cost & ROI Considerations for Implementing Artificial Intelligence and Customer Experience Together!

Implementing AI for customer experience requires a lot of upfront cost that most businesses often factor in. However, what about the recurring costs, and when will the system actually start to either save or make you money that you spent? 

Through the tables below, we have tried to share a realistic picture for you to comprehend.

🔹 1. Cost Components of AI-Driven CX

If outsourced, most of these costs are reduced or converted to predictable monthly billing.

🔹 2. Key ROI Areas & Measurable Impact

🔹 3. ROI Timeline Overview

Ethical and Bias Concerns Beyond Privacy/Data – For Data Collection of Customers

Data collection from customers will be key for your AI solution to work and deliver an enhanced CX experience. However, there are concerns that you need to be aware of. Otherwise, it’s often too late when businesses actually arrive at these issues, and fixing that requires tons of rework.

• Algorithmic bias towards a certain group of customers is especially problematic if you are using existing AI models that may not have been fixed.
• Once the data is fed, the AI model kind of acts like a black box, so what’s really happening behind the scenes is rather opaque instead of transparent.
• To achieve personalization, AI models process customers’ historical data, behavioral patterns, etc., which can be seen as a manipulative practice.
• Consumers who are using inferior models of smartphones or any device in general can be excluded due to the low processing capabilities of their devices.
• Over-automation of the workflows can end up removing human judgment from the process.
• Behavioral tracking is consistent. However, it is not often meaningful, and for many, it could be a means of trespassing on privacy.
• The AI systems that are built today are still prone to incorrect sentiment or emotional interpretation.
• An AI system may deliver unequal benefits if we factor in different customer segments.

Training Requirements for AI + Team Adoption

AI systems work best when teams are laid out to support them actively. This demands you to focus on several critical areas in terms of training requirements for handling the system, + adoption of the right teams to do it. 

Therefore, let’s learn about all of it!

🔹 1. Core Training Areas for Teams

🔹 2. Adoption Enablers

• Role-Based Training: Custom sessions for agents, managers, and tech teams based on their roles.
• AI Copilot Familiarization: Tech teams should work in tandem with AI, and not compete against it. So, they need to be made familiar with what to expect and how to use it.
• Sandbox/Simulation Practice: Allow hands-on learning before the live deployment is done.
• Continuous Upskilling: Provide regular refreshers for the teams, as the tools evolve.
• Performance Feedback Loops: Agents must report AI errors or gaps for further improvement.

🔹 3. Leadership & Culture Alignment

• Set expectations early— Make sure your employees know that AI is there to support and not to replace them.
• Communicate the performance benefits (faster resolutions, fewer repetitive tasks) of using the laid-out system.
• Include KPIs that are tied to AI adoption (AHT reduction, CSAT lift, agent productivity).

🔹 4. Ongoing Support

What can (NewAgeSys) do for you?

We are in the business of outsourcing, and till now we have worked on more than 1000+ projects, in our long-standing experience of 30+ years. In fact, we have worked in a range of industries, which include transportation, healthcare, real estate, fintech, etc. 

And, yes, we have worked on numerous AI-based projects that directly or indirectly impact customer experience.

To add, some of the services we provide in the domain of AI are: Natural Language Processing, AI-driven Web and Mobile Applications, Smart AI Assistants and Chatbots, GenAI Consultation, Automation Solutions, etc.

And, if you are thinking about affordability, and where we are based? Well, we are based out of the U.S.. And, deliver solutions at an incredible price range of $25-$49 per hour.

How are we able to deliver such excellent pricing despite being a U.S.-based company? We have offshore development centers that help us keep our costs to a minimum. So, you get the pricing of an Asian company but the work ethic of a U.S.-based company.

Future of AI in Customer Experience

There’s a lot that is happening in the niche of “AI in customer experience,” as far as future prospects are concerned. For instance, digital humans, AI for immersive CX, autonomous CX systems, etc. However, with the list below, our aim was to share the most pressing events related to the future of the niche that would affect a business.

So, let’s begin!

1. Agentic AI for Proactive CX: AI agents are nothing short of a buzzword today. These are autonomous AI programs that have the capability to resolve customer issues without any human involvement. Some of the tasks these agents can handle are refunds, troubleshooting, and appointment scheduling. Plus, these systems are capable of proactively monitoring the systems to anticipate needs and even follow-up actions, thereby improving CX.
2. Generative AI for Dynamic Interaction: Generative AI has already changed the world a lot. However, this is simply the beginning. With the tech, companies are improving their customer experience through personalized responses, product recommendations, and content that sticks. Also, these systems have the capability to automate up to 70% of your customer interactions and improve customer satisfaction by 30%.
3. Hyper-Personalization in Real-Time: With real-time hyper-personalization, companies are able to drive more revenue today. Using AI algorithms that analyze browsing, past purchase, and behavior data, companies like Amazon are delivering tailored journeys and recommendations. In fact, in industries like e-commerce, this has become a gold standard.
4. Voice & Emotion Recognition for Empathy: Advanced voice AI listens and responds naturally. In fact, 74% of the consumers reported a better experience when AI was able to understand their voice inflections and tone.
5. CX Outsourcing 2.0: Hybrid model or the coalition between human agents and AI systems is the new ‘new.’ Here, they work together to handle routine inquiries using automation while reserving all the complex empathy-driven tasks for humans. 
6. Omnichannel AI and Multimodal Context: AI is now able to orchestrate journeys across web, app, voice, and in-store channels. This omnichannel AI customer experience has helped companies create a consistent experience for their customers across the board. Plus, multimodal AI is capable of leveraging text, speech, image, and behavioral signals to create a context-rich experience.
7. Data Privacy and Personalization Tradeoff: With us becoming more and more connected, the chances of external intrusions are also increasing. For this reason, nations from all across the globe are enforcing stricter regulations. This is forcing CX leaders to create a balance between deep personalization and privacy concerns, which are leading the world towards better data collection platforms and technologies, powered by AI.

Final Words

Employing AI for improving your customer experience is no longer a luxury most businesses can avoid. But, this doesn’t mean that each and every company needs to lay out heavy budgets to have their own proprietary AI-based customer experience solutions. In fact, in most cases, we ourselves ask the leads approaching us to go for an off-the-shelf experience, which will help them save money and time on integration. On the contrary, having a proprietary ecosystem of ‘AI in customer experience’ for some businesses is simply inevitable. 

So, to end the article, we will ask you to assess your requirements and think about whether your business even needs a proprietary solution. Be it anything, we’d be more than happy to assist you, whether it be the development of a full-fledged solution or consultation as to what your current organization lacks. Therefore, fingers crossed, and we hope to see you on the other side of the article.

FAQs:

1. Which industries benefit the most from artificial intelligence and customer experience integration?

In today’s day and age, we can put a finger on any industry that is customer-facing to benefit from the aforementioned integration; however, in industries that can either see direct benefit or rapid ROI, their customer volumes are generally high, such as Amazon, Walmart, Netflix, etc. 

So, putting a name on a few industries, here they are:

• E-commerce
• Banking
• SaaS
• Logistics
• Entertainment
• Travel
• Telecom

2. How can businesses decide when to start using AI in customer experience?

There are a few key indicators to justify this aspect, which largely deal with high-volume pain points. However, to name a few, they are:

• Large volumes of support tickets
• Response delays
• Personalization gaps in service delivery

3. What teams should be included when planning to use AI in customer experience?
 

While implementing artificial intelligence and customer experience together, cross-functional involvement of teams is highly important. Adding to it, some of the teams that need to be included while planning it are:

• CX Operations
• Compliance Team
• Sales Team
• Customer Support Leadership Team

4. How do you choose between building, buying, or outsourcing AI solutions for CX?

The best way to understand whether you need to build, buy, or outsource your AI solution CX requirements is by assessing:

• Readily available budget
• Deployment speed
• Scalability needs
• Integration complexity
• Available in-house AI talent

5. What compliance steps should a business take before deploying AI in customer experience?

Ideally, the compliance you need to follow depends on the region you are operating in. For instance, GDPR, HIPAA, SOC2, etc.

However, a few general steps one takes before deployment to ensure compliance are:

1. Legal audits
2. Assessment of data storage practices
3. Defining consent policies

6. What are some of the most essential KPIs leadership should track to enhance customer experience before deployment?

Here is a list of KPIs for the leadership to ensure that:

• Monitor AI containment rate
• First response time
• Deflection rate
• Agent assist usage
• Sentiment accuracy

Each of these should be followed in the first 30-90 days before the deployment happens.

7. How does omnichannel AI customer experience impact existing CRM or contact center workflows?

Well, to begin with, omnichannel AI customer experience doesn’t replace your existing CRM or contact center. However, what it does is that it enhances and streamlines them better. So, businesses that utilize the omnichannel AI often tend to get benefits like:

• Keeping all customers’ data connected across channels (chat, email, voice, and social).
• Automation of ticket creation, updates, and routing based on intent, history, and sentiment.
• It assists agents in real-time with suggestions for responses and knowledge surfacing inside CRM dashboards.
• Reduces agent workload by handling Tier 1 queries before they reach your contact center.
• Improves FCR, AHT, and SLA performance without making changes to your workflows.
• Integrates via APIs or middleware, so no platform replacement is needed.

8. How can companies future-proof their AI CX strategy as generative AI and automation evolve?

Some of the ways to make yourself future-proof in this regard are:

• Adopting a modular platform approach
• Investing in scalable data pipelines
• Choosing tools that allow API-based expansion and model updates

9. What budgeting model works best for AI in customer experience—CapEx or OpEx?

Most businesses prefer OpEx through outsourcing or SaaS AI platforms. Why? It helps in avoiding any heavy upfront capital investments, which is often a problem with in-house models.

10. How can businesses ensure AI adoption is embraced across teams instead of resisted?

Businesses can introduce copilots gradually within their ecosystem. To do so, they can do the following:

• Communicating productivity benefits to the team
• Measuring adoption after deployment
• Incentivizing teams with AI-based performance goals