How to Improve User Experience Using AI Chatbots
From product development and product management perspectives
As I write this, OpenAI has just released Operator, a new generative AI tool that showcases how AI is pushing boundaries, making chatbot implementation a must for businesses aiming to meet rising customer expectations, brining AI to next level to the next level. So exciting!
AI-driven chatbots are evolving from simple tools into essential parts of product management (PM) and customer experience (CX) strategies.
However, technology acceptance is not a gradual things. When I call customer support, I directly seek human agents all the time, as from the past experience, I believe this is the most efficient way. People like me may have such pre-judgement towards AI. Webex’s survey shown that 55% of customers avoid self service.
AI adoption in CX also shown mixed results: while many report a reduction in customer satisfaction, the potential for scalability, personalization, and efficiency makes it a key focus for the future.
This article explores actionable ways to maximize the value of AI chatbots from both product and marketing perspectives. While AI chatbots have come a long way, their true potential lies in thoughtful design, seamless implementation, and ongoing improvement.
Designing AI Chatbots
Effective AI chatbots start with thoughtful design. But what the chatbot should achieve? How it will meet user needs?
The primary goal of designing chatbots is to reduce friction in the customer journey. Long wait times, repetitive questions, and inconsistent service often frustrate users. A good chatbot solves these problems by being intuitive, proactive, and helpful. For example, it should think like a human by rephrasing unclear queries or asking clarifying questions.
Personalized Interactions
Traditional customer service models often result in generic responses that don’t take individual customer needs into account. AI, on the other hand, can analyze user preferences, behaviors, and histories, chatbots to offer tailored responses that enhance relevance and satisfaction.
Like social media, different user have different feed, AI can create personalized experience based on patterns in a customer’s inquiries. For example, if a customer frequently contacts support for billing issues, the system can prioritize these types of concerns and provide faster, more relevant assistance. Personalization not only improves the customer’s experience but also builds trust and loyalty, as the service feels more tailored to the individual’s specific needs.
Rufus, Amazon’s new generative AI-powered conversational shopping assistant, can addresses questions often asked by sharing answers based on the helpful information found in product listing details, customer reviews, and community Q&As. It should also understand users’ preference. For example, a “review-heavy” customer like me, would find the question “What do customers say?” (in the picture) attracting. Once I click that questions, I am expecting an unbiased summary of key takeaways from the reviews.
Another benefit of AI-driven personalization is the elimination of wait times. A report by Porch Group Media highlights that 56% of consumers identify long wait times before getting help as a significant frustration when interacting with customer service. In the situation of agents shortage, AI systems can chime in and handle routine inquiries instantaneously, customers no longer need to wait on hold or navigate complex menus. This not only improves the customer experience but also allows businesses to handle a higher volume of queries without sacrificing quality.
Proactive Customer Care
Traditional customer service is largely reactive, requiring users to initiate contact and navigate tedious processes. In contrast, AI chatbots take a proactive approach by anticipating potential issues and addressing them before they arise.
For example, AI-powered systems can anticipate customer needs and provide agents with actionable prompts. If a customer’s usage is nearing the threshold for a status upgrade, the system can calculate ways to satisfy this upgrade proactively. When the customer reaches out, the agent can address their immediate question while simultaneously offering support that deepens the relationship, potentially avoiding future calls. Some businesses may even allow the AI chatbot to take the initiative, such as sending a nudge to upsell or providing solutions to anticipated needs before the customer contacts support.
This is my personal experience. Aritzia is a clothing retail company who has both physical and online channel. They can accept exchange for sale items, but customers has to contact Concierge. One day as I was calling for exchanges, to my surprise and without me mentioning, the agent asked about if I want to place an order for the things in my chart. I found this is a really good upselling strategies to improve customer experience in a proactive way. Just image, if they connect your online profile when you shop in-person, this can not only make customer more convenient but also enable cross-sell. A lot of retailer is doing this: connecting a customer’s online profile with in-person shopping data. This would further improve convenience and enable cross-sell opportunities.
Another practical use of AI-driven proactive care is seen in predictive analytics. Chatbots can detect delayed shipments or unusual account activity and notify customers with solutions in advance, preventing frustration and building trust. To illustrate, a customer awaiting a shipment might receive an AI-generated update that not only acknowledges the delay but also provides alternative options or compensations.
Proactive customer care isn’t limited to digital interactions. A reimagined AI-supported service model spans all touchpoints, including:
Self-Service Interfaces: AI-powered chatbots can handle routine queries and provide instant resolutions through apps or websites.
Agent-Assisted Channels: In physical branches or on social media platforms, AI can support employees in real-time, equipping them with insights to deliver high-quality outcomes.
This multi-channel approach ensures consistency and efficiency, enabling businesses to meet customer needs wherever they interact.
Cognitive Skills and Responsiveness
One of the most valued traits of conversational AI is its ability to process information effectively and demonstrate understanding. AI should excel at taking in information, comprehending what’s being asked, and responding appropriately. For example, when users ask poorly worded or confusing questions, the AI should not provide a quick, canned response. Instead, it should ask clarifying questions or rephrase the query to ensure understanding, just as a human would. This approach address the cognitive competency and improves engagement and ensures that users feel heard and understood.
Additionally, offering users choices is key to making interactions feel collaborative. For example, in a shopping app, instead of giving a single recommendation, the AI could present multiple tailored options, empowering users to feel in control.
Fairness also plays a role in cognitive competency—users need to trust that the AI considers their unique needs and treats them equitably. For example, a hiring AI should account for diversity and inclusion to ensure fairness in applicant evaluations.
Balancing Emotional Intelligence & Level of Automation
AI is now capable of detecting different emotions, such as happiness, anger, and frustration. This ability allows it to express warmth and care, building a stronger connection with the user. However, an important caveat here: it’s crucial to avoid making the AI too human-like, as this can make users feel uncomfortable or even creeped out. The ideal balance is for the AI to be human-like enough to be relatable but not so human-like that it feels unnatural or artificial.
How Should AI Respond to Show It Understands the User’s Emotions?
Research has shown that positive emotional expressions from human agents can spread good feelings to customers, and the same effect can occur with AI-powered chatbots. Some conversational AI companies are exploring the space of using AI as emotional support. This further confirmed emotional intelligence potential that AI has.
However, these good feelings can sometimes be canceled out if users experience a negative reaction to unexpected emotional expressions from a chatbot.
Expectations play a critical role in determining whether a chatbot’s emotional expressions create a positive or negative experience. Different types of customers may react differently to the same AI-expressed emotions.
Product managers (PMs) should first understand customer behavior and preferences to ensure AI responses are personalized and contextually appropriate. They must also consider product consistency. For example, if a user is frustrated, the AI should follow the user’s preferences by either (1) acknowledging the frustration and offering help or (2) forwarding the user to a human agent for resolution.
In the mix use of AI and human agents, PMs should also establish clear workflows to manage the handoff between automated systems and human agents effectively. While the chatbot can handle routine tasks efficiently, nuanced or sensitive issues should be directed to humans for resolution. This includes:
Role Definition: Clearly define what tasks the AI handles, such as repetitive inquiries, and when human intervention is required for complex or emotional cases.
Dynamic Escalation Triggers: Use real-time analysis to detect frustration, unresolved queries, or sentiment shifts, prompting escalation to a human agent when necessary.
This approach ensures the AI aligns with user expectations while maintaining the right balance of automation and human-like interaction.
By designing chatbots with these principles, businesses can address key customer pain points like long wait times, repetitive tasks, and inconsistent service. The impact is clear: improved user satisfaction, reduced churn, and increased engagement.
2. Implementing AI Agents
Once the design is finalized, implementation focuses on turning the vision into a functional system. This stage is crucial, as poor implementation—such as weak integrations or inadequate training—can lead to inconsistent experiences.
Go-to-Market Considerations
As briefly mentioned above, performance expectancy is a key driver of technology acceptance, significantly impacts user satisfaction and their intent to continue using the system. Thus, marketer should avoid overstating chatbot performance during marketing efforts. Highlight the chatbot’s strengths while maintaining transparency about its limitations. Use friendly language to remind users that the chatbot is continuously learning and improving.
Ensure the chatbot’s interface is intuitive and clearly demonstrates how it enhances the user experience. For example, if the chatbot resolves 80% of routine queries instantly, emphasize this value in marketing efforts.
Psychological ownership also leads to greater engagement and loyalty. Companies can create features that make users to feel that sense of ownership over their interactions with the chatbot. This could include customization options or acknowledgment of user preferences.
Safety and Trust
Establishing safety and trust is an essential aspect of implementing AI chatbots. PMs should ensure that the chatbot’s role and capabilities are clearly communicated, helping set realistic expectations for users. Transparency about how user data is collected, processed, and stored is equally vital in building confidence. For example:
Proactively Communicate Data Security Measures: Share clear policies about data protection and implement robust encryption to alleviate privacy concerns. Displaying messages like “Your privacy is our priority” during interactions involving sensitive data reinforces trust.
Detailed Documentation for Transparency: Provide technical specifications, FAQs, and product notes to help users understand how the chatbot generates solutions. Transparency in AI decision-making is particularly important for research and science professionals, who often require insight into the algorithms and methodologies behind AI recommendations. By offering clarity and actionable insights, businesses can empower users to make informed decisions.
Building safety and trust is equally essential. Clearly articulating the chatbot’s role and capabilities helps set realistic expectations. Additionally, being upfront about how user data is collected and used ensures that customers feel informed and secure while interacting with the chatbot. Users must feel confident that their personal information is secure and won’t be misused. Proactively communicating data protection policies and implementing robust encryption measures can alleviate concerns.
Transparency further reinforces trust. I believe PM is more a science than an art. I heard this a lot when I talking to people in the research and science field that they need to understand how AI generate the solution. Most of AI companies has really detailed tech specs, product notes, FAQ released online. The transparancy helps AI provide customers the support for decision-makeing.
3. Monitoring and Continuous Improvement
AI chatbots are not static solutions. Their success hinges on ongoing monitoring and iterative improvements to keep up with user expectations and business goals. Without continuous adaptation, chatbots risk losing relevance and failing to address evolving needs.
Key steps for monitoring and improvement include:
Track Key Metrics: Monitor customer satisfaction (CSAT), net promoter scores (NPS), resolution times, and abandonment rates. These metrics provide valuable insights into performance.
Leverage Feedback Loops: Use customer interactions and feedback to retrain the chatbot. This ensures it evolves alongside changing user behaviors or new query types.
Conduct A/B Testing: Test changes to conversation flows, response styles, or features to identify what works best.
Expand Capabilities: Regularly update the chatbot to handle additional queries, integrate new data sources, or support new languages.
McKinsey’s new model for customer service also highlight the importance to consider the nuances through out the design, implement and monitor phrases—from communicating with customers before they even reach out with a specific need, through to providing AI-supported solutions and evaluating performance after the fact.
By prioritizing monitoring and improvement, businesses can ensure their chatbots remain valuable assets that drive loyalty and reduce churn over time.
Conclusion
AI chatbots hold immense potential to revolutionize user experience through automation, personalization, and proactive care. However, their success depends on thoughtful design, seamless implementation, and continuous improvement. By addressing customer needs at every stage, businesses can create chatbots that not only meet expectations but exceed them. Tools like OpenAI’s Operator hint at what’s possible, and with a structured approach, businesses can leverage AI to deliver faster resolutions, meaningful interactions, and measurable outcomes. The key lies in balancing the efficiency of AI with the empathy of human agents, ensuring a holistic approach to customer experience.