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A Roadmap to Developing the Best Customer-Centric Al Application in 2024

January 02, 2024

A Roadmap to Developing the Best Customer-Centric Al Application in 2024

According to McKinsey & Company's report, 40% percent of respondents say their organizations will increase their investment in AI overall because of advances in gen AI. Improving the customer experience is one of the expected results of its application.

Want to build the best customer-centric AI app? In this article, we'll discuss the most valuable use cases, challenges, stages, and best practices to consider.

Why is a customer-centric experience so crucial for your business?

A customer-centric experience is a strategic approach focusing on the customer at the core of every business decision and action. It emphasizes understanding and catering to the client's needs and preferences and customizing the products, services, and interactions. 


A customer-centric experience is valuable for businesses for many reasons: 

  • Focusing on the customer's needs ensures that every interaction is designed to satisfy them. This, in turn, boosts customer satisfaction, as customers feel valued and understood.
  • Satisfied customers are more likely to stay with a brand that consistently meets their expectations.
  • Loyal and satisfied customers spend more over their lifetime as they continue to do business with a company.
  • When customers feel heard, and their issues are promptly resolved, they are less likely to switch to competitors. That means businesses can reduce customer churn. 
  • Businesses prioritizing customer satisfaction and experiences tend to build a strong and positive brand reputation, attracting new customers.

Popular AI-based technologies enhancing customer experience

What technologies help achieve all the benefits mentioned above? Let's look at the core ones:

Generative AI and Large Language Models (LLM)

Generative AI means using models like OpenAI's GPT-4 to create human-like text from given prompts. Such technologies offer assistance through chatbots and virtual assistants, generate personalized content and product recommendations, and analyze customer feedback to identify trends and areas for improvement.

Recommendation systems

Recommendation systems are AI-driven tools that analyze user data and preferences, offering tailored suggestions. They enhance customer experiences by delivering relevant product and content recommendations and reducing decision fatigue with personalized options.

Big Data Analytics (BDA)

Big data analytics involves processing and analyzing large datasets to uncover patterns, trends, and valuable insights. BDA learns customer behavior and preferences for personalized experiences, detects potential customer churn, and optimizes marketing campaigns.

How AI empowers a customer-centric experience

Companies continually seek innovative ways to enhance customer experiences and foster long-term loyalty. AI has emerged as a powerful partner in this process.

Customer need understanding

Meeting customer needs means aligning with their expectations. To detect them, you can use customer surveys, interviews, reviews, social media monitoring, and focus groups.

Further, the large volumes of data received must be properly processed:

  • Utilize generative AI models to analyze text data from customer reviews and social media posts. 
  • Employ natural language processing (NLP) techniques to delve into customer feedback and conversations. It helps detect prevalent themes, unmet needs, and pain points.
  • Examine historical customer data to predict future customer needs and preferences.

Customer journey optimization

The customer journey is a client's interactions with a business, from initial awareness to post-purchase engagement. Its optimization is critical for customer retention and loyalty, brand reputation, conversion rates, and revenue.

  • AI-driven tools analyze customer data, behaviors, desires, and preferences to construct a holistic map of the customer journey.
  • AI can assess customer data and interactions in real time so that you can deliver personalized content, offers, and support.
  • With predictive analytics, you can foresee customer requirements and proactively address potential concerns.

User interface improvement

User interfaces (UI) are crucial in shaping the customer experience, serving as the primary interaction point for digital products and services. Improving UI means better usability and user engagement, friction reduction, and enhanced accessibility.

  • AI analyzes user behavior and preferences to offer tailored navigation, simplifying content or product discovery.
  • AI algorithms optimize UI layouts for different devices and user preferences, ensuring a seamless platform experience.
  • AI-enhanced search understands user intent, yielding more relevant and precise results.
  • AI facilitates voice and gesture-based interactions, providing natural and alternative means of engaging with digital products and services.

Content generation

AI-generated content becomes a valuable tool for elevating marketing and customer engagement strategies. That means you can produce a diverse array of articles, social media posts, and promotional materials.

  • By automating content creation, you conserve time and resources, allocating them to other strategic aspects of business.
  • AI rapidly generates substantial volumes of high-quality content so you can expand your efforts and extend your reach.
  • AI-generated content can be customized to align with individual customer preferences and needs, making it more captivating.
  • With AI, you maintain a uniform brand voice and style across various channels.

However, it's crucial to remember concerns regarding the authenticity of AI-generated content.

A/B testing and optimization

A/B testing, or split testing, involves comparing multiple versions of webpages, emails, or marketing materials to determine the most effective one. AI can assist you in several ways:

  • AI generates multiple design, message, and content variants for A/B testing. This saves time and resources compared to manual creation.
  • AI-driven algorithms analyze extensive A/B test data, identifying patterns and trends.
  • AI monitors and adjusts A/B test variants based on real-time performance data, ensuring the most effective version is displayed.

However, the reliability of AI-generated insights hinges on the quality of A/B testing data. Also, you should avoid over-optimization, where minor improvements negatively affect other aspects of the customer experience.

Personalization and customization

Personalization and customization let businesses cater to individual needs and boost loyalty. Here’s how AI-generated content helps:

  • AI creates personalized promotions that resonate with specific customers, boosting conversions.
  • AI adjusts content based on individual preferences, providing a more engaging experience.
  • AI-driven support — chatbots or virtual assistance — improves customer satisfaction and loyalty by providing quick, personalized help.

Omnichannel customer experience

An omnichannel customer experience means providing seamless and consistent interactions across multiple touchpoints. Consistency leads to higher client satisfaction as users switch between channels without friction or loss of context.

Businesses can optimize these interactions using AI, ensuring a unified and satisfying customer journey. 

  • AI creates content and promo materials optimized for each channel by analyzing user behavior and preferences.
  • AI consolidates data from all channels to develop comprehensive customer profiles and satisfy individual needs.
  • AI analyzes customer interactions in real time, identifying trends for optimization across all channels.

Challenges of implementing AI-powered Applications

In addition to the numerous benefits, AI-powered solutions also come with several limitations and challenges:

Lack of empathy

AI is known for providing rapid and precise answers to customer inquiries. It's excellent at handling routine and straightforward tasks. However, in emotionally charged situations, customers may find AI-driven interactions unsatisfactory.

Human agents can discern and respond to these emotional nuances effectively. That's why you should balance AI's efficiency and human empathy capacity.

Poor context understanding

AI systems excel at processing structured data and straightforward queries. However, understanding the specifics of a unique situation, idiomatic expressions, or cultural context can be challenging. When AI systems misinterpret or fail to capture the full context, customers may receive responses that don't align with their needs.

It's essential to design AI systems with user-friendly interfaces that enable easy handover to human experts when nuanced understanding is required.

Dependence on data quality

AI systems are initially trained on data and continue to learn and improve through ongoing data input. When AI operates with poor data, responses may be inaccurate, recommendations irrelevant, and interactions frustrating.

Your task is to guarantee that the data is accurate, diverse, and reflective of real-world scenarios. It should be free from bias and errors.

Required time and costs

Tailoring AI systems to the unique needs of a business can be a time-consuming task. Effective setup, testing, and fine-tuning require careful consideration and effort. Also, customizing AI systems means financial investments, both in terms of software and hardware, as well as ongoing maintenance and training costs.

Careful planning will help you optimize the overall timeline and costs. 

Human supervision

AI systems should align with the company's standards, ensuring customer interactions meet established expectations. Ongoing audits help maintain quality and consistency in interactions, aligning them with the desired service level.

You should use human supervision to prevent any missteps or deviations. It will safeguard against potentially negative outcomes.

Data privacy and security

AI systems may handle sensitive client information, such as personal details and transaction history. Ensuring it is securely stored is imperative to prevent data breaches and unauthorized access. 

You must adhere to data protection laws and regulations to safeguard customer data and maintain legal compliance. Also, be transparent about data usage and inform them how their information will be utilized.

Roadmap for New Products


Crucial stages of developing/modernizing a customer-centric AI application

Over the years at Artkai, we've improved the development process, assisting our clients in creating effective customer-centric AI applications. Now it's time to explore these steps:

Discovery phase 

Market research is a crucial component of the discovery stage when creating a customer-centric AI application. It includes:

  • Opportunity analysis. Identify areas where your AI application can make a significant impact. It helps you understand the potential opportunities for addressing customer needs and pain points effectively.
  • Competitive analysis. Study your competitors, examining their products, services, and strategies. This will help you gain insights into what your AI application needs to offer to stand out and provide superior value to customers.
  • Industry analysis. Evaluate the industry or market in which your AI application will operate to understand the broader context and trends.

Also, it's crucial to learn about your target customers to ensure that the AI application will resonate with their actual needs. 

A comprehensive user persona portrait typically contains the following elements:

  • Demographics with age, gender, location, and income level
  • Educational background, job, and career path
  • Goals and objectives, both in their personal and professional life
  • Challenges and pain points
  • Specific needs, desires, and preferences
  • Behavioral insights, both online and offline
  • Motivations to make decisions and take action
  • Direct quotes or statements that capture the persona's mindset and attitudes
  • Media consumption, including social media platforms, news sources, etc
  • Communication style
  • Tech savviness
  • Expected app features and benefits
  • User journey

Then, you should define the specific problem or need your customer-centric AI application will address.

Planning phase

The planning phase sets the direction and foundation for the entire project. Follow these steps:

  • Create a comprehensive project plan that outlines the scope, timeline, and resource requirements. 
  • Define the specific goals and objectives you aim to achieve with the project, ensuring they are customer-centric.
  • Prioritize the features and experiences that directly cater to customer needs and preferences.
  • Define the KPIs related to customer satisfaction, engagement, and retention.
  • Develop a data strategy that outlines how you'll collect, store, and manage the data. 
  • Identify the technology stack required for implementing your chosen AI technologies. 
  • Allocate the budget and resources. This includes funds for technology, personnel, and any third-party services you might need.
  • Identify potential risks impacting the project's success, particularly in meeting customer-centric goals. Develop a risk mitigation plan to address these challenges.
  • Address any legal and compliance requirements relevant to your AI application, particularly those related to customer data privacy and usage.

Design phase

The design phase is crucial in creating an AI application that caters to customer needs and preferences. To make it real, you should:

  • Design the visual and interactive elements of your AI application to prioritize customer preferences and ease of use.
  • Incorporate personalization features like user preferences, historical behavior, and other data points to create a personalized journey.
  • Create prototypes or mockups to visualize how the AI application will look and function.
  • Design a responsive and intuitive user interface that aligns with customer expectations. The interface should be easy to navigate and engage with.
  • Conduct usability testing to ensure that the design meets customer-centric goals. Obtain feedback from real users to identify any usability issues and make the necessary adjustments.
  • Refine the design based on feedback and insights gathered during usability testing.
  • Ensure that the design aligns with the chosen AI technologies and that the user experience is enhanced by these technologies rather than hindered.
  • Design with accessibility in mind, considering users with disabilities or special needs.

Development phase 

In the development phase, you turn plans and designs into a functional and customer-centric AI application. 

  • Implement AI technologies and algorithms that support customer-centric goals. 
  • Create algorithms that allow for personalized recommendations and real-time support. Ensure data privacy and security measures are in place to build customer trust. 
  • Deploy the AI models and algorithms that were selected based on the problem definition. 
  • Integrate data sources and APIs.
  • Seamlessly integrate AI into the user interface or customer touchpoints where it will be used.
  • Implement monitoring and analytics tools to track key performance indicators (KPIs) related to customer satisfaction, engagement, and retention.
  • Define processes for collecting, storing, and processing customer data.

Quality Assurance (QA) and testing

The QA and testing phase is essential to creating a reliable and customer-centric AI application. By conducting thorough testing, you can identify and address any issues that may hinder the customer experience:

  • Rigorously test the AI application to ensure its functionality, usability, performance, and security.
  • Test the AI models for accuracy and fairness to avoid bias in recommendations. Ensure that the AI models provide reliable results.
  • Gather feedback from real users or test groups during testing to make necessary adjustments. 
  • Simulate real-world scenarios to assess how the AI application performs in practical customer interactions.
  • Identify and resolve any bugs, errors, or issues discovered during testing.
  • Assess the overall usability of the AI application. Is it intuitive, easy to navigate, and engaging for customers?
  • Evaluate the performance of the application, including response times and system reliability.
  • Ensure that sensitive customer information is protected and the application meets data security standards and regulations.

Deployment and maintenance

This is when your AI application goes live. You launch it into the world and start making a difference for your customers. However, your job doesn't end at deployment. You should continuously monitor the performance, particularly in terms of user satisfaction.

It's crucial to maintain an open channel of communication with your customers. You want their feedback, and you're ready to resolve any issues. Based on user input and emerging customer needs, you must regularly update and optimize the application to keep it at its best. 

Roadmap For existing products

Roadmap for Existing Products



Already have an application you want to modernize? Conduct an audit to help you understand the current state of your customer-centric AI solution. During this stage, it is essential to:

  • Examine the existing software code, data models, user interfaces, and the app's architecture.
  • Assess the performance, including its response times, accuracy, and overall efficiency in delivering customer-centric experiences.
  • Ensure that the app complies with data protection regulations and industry standards
  • Check the software's ability to scale as the business grows and evolves
  • Collect and analyze the app to gauge customer satisfaction with the AI application. This includes understanding if it effectively addresses their needs and pain points.
  • Measure and analyze key performance indicators (KPIs) relevant to customer-centric goals like client satisfaction, retention, engagement, and problem resolution.
  • Identify any gaps or shortcomings in the current AI application concerning customer-centric objectives.
  • Examine the quality and accuracy of data used by the AI application
  • Check if the used technologies, tools, and frameworks remain optimal for delivering a customer-centric AI experience
  • Uncover opportunities for optimizing the application and generate detailing of the audit findings, recommendations, and action plans for modernization.

Redesign phase

When it comes to enhancing your AI application, the redesign phase is a pivotal moment. To make it real, you should:

  • Start by incorporating personalization features to consider user preferences, historical behavior, and data points. This ensures a tailored and engaging AI application experience.
  • It is crucial to visualize the future. You can do this by creating prototypes and mockups that provide a clear understanding of how your AI application will look and function. By doing this, you can predict user interactions and refine the user experience accordingly.
  • Your design should seamlessly align with customer expectations, making navigation and interaction a breeze. Users should effortlessly engage with your product, and the interface should be a conduit to their satisfaction.
  • Conduct comprehensive usability testing. This step enables you to gauge whether the design aligns with customer-centric goals. Real user feedback is invaluable in identifying and rectifying any usability issues that may arise.
  • Take the insights and feedback gathered during usability testing to heart and refine your design accordingly. It's a dynamic process that ensures your product evolves to meet user needs and expectations.
  • It's essential to ensure that your chosen technologies enhance the user experience rather than hinder it. Your design should seamlessly integrate with AI technologies to deliver a more personalized and efficient interaction.
  • Consider users with disabilities or special needs to ensure inclusivity. A well-designed, accessible product opens the door to a broader user base and demonstrates your commitment to diversity and usability.

Development improvements 

The improvements for existing products are pivotal for staying responsive to customer needs and competitive in the market. During this stage, we:

  • Сlean up and reorganize the existing code, removing redundant or unnecessary ones, enhancing its clarity, and organizing it for improved readability and maintainability
  • Optimize the performance by improving algorithms, enhancing database queries, and fine-tuning code for better speed and responsiveness.
  • Refactor the architecture and code to enhance scalability.
  • Optimize data storage methods to improve data retrieval and processing. 
  • Integrate new features that align with customer-centric goals and enhance user experiences.
  • Create and implement AI models or optimize existing ones.
  • Conduct unit testing, integration testing, and system testing to verify that the app's functionality remains intact and optimized.
  • Document all code changes and improvements.

Best practices for delivering AI-powered applications

Implementing AI-powered solutions requires adherence to best practices that ensure effectiveness while addressing potential challenges. Let's explore them:

Combine AI with humans

Combining AI and humans allows you to scale your services to meet the demands of a growing customer base without sacrificing quality. 

Let’s take customer service as an example. AI is available 24/7, offering instant support at any hour. This means customers can receive assistance when it's most convenient for them, improving their overall experience. However, some issues may be intricate and require human judgment and decision-making. Also, human agents bring a level of personalization and empathy that AI, as of now, cannot replicate.

Consider continuous monitoring and improvement 

Regularly analyze how your AI systems are performing. Also, actively seek and incorporate customer feedback into your improvement process. Understand what your customers like and dislike about the AI service to identify areas for enhancement.

Use the insights from performance metrics and customer feedback to make necessary adjustments. This could involve refining AI algorithms, improving responses, or expanding the system's capabilities.

Use high-quality data

AI systems learn from the data they're exposed to during training. If this data is high-quality, the AI is more likely to provide accurate insights and responses. At the same time, it helps reduce biases that might emerge in AI systems.

Diverse data allows AI to understand and cater to the unique needs of different customers. It's the foundation for creating personalized support that enhances customer experiences. 

Prioritize investing in acquiring and maintaining high-quality data to improve AI performance and customer satisfaction.

Set clear expectations

Clearly convey how customers can use the AI system, what it's best suited for, and when to seek human assistance. Be open about the capabilities and limitations of your AI systems to manage customer expectations and prevent misunderstandings.

Educate your customers about the benefits of the AI system and how to make the most of it. This empowers them to utilize the tool effectively. Also, make sure customers understand the level of assistance they can expect from the automated system. 

Focus on user experience

The design of AI-powered solutions should revolve around the user. Prioritize intuitive interfaces that are easy to use. Users should be able to navigate and use the system without confusion.

Ensure that users find it not only helpful but also enjoyable to interact with. And your AI system is accessible to a wide range of users, including those with different abilities.

By focusing on user experience, you foster customer satisfaction and loyalty. That's why it's crucial to regularly gather feedback and make improvements. 

Implement robust security measures 

Prioritize the security of customer data, implementing measures to safeguard sensitive information from unauthorized access and breaches. They may include encryption, access control, firewalls, Intrusion Detection/Prevention Systems (IDS/IPS), multi-factor authentication, etc.

Stay vigilant in monitoring and enhancing data security. The digital landscape evolves, and so should your protective measures.

Wrapping up

A customer-centric AI application enhances customer satisfaction, fosters loyalty, and drives increased business profitability by delivering a personalized and efficient experience. Creating or upgrading such a solution requires in-depth research, selecting the appropriate technology stack, meticulous project planning, design, and development. That's why you need an experienced team.

Whether you need a new solution or an update to an existing one, Artkai will help in any case. Partnering with us guarantees an excellent customer experience and the software's efficiency, security, and scalability. Contact us to discuss your project.

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