top of page

AI's Impact on Customer Experience and Operations

Customer experience (CX) and customer operations (CO) are currently undergoing a significant transformation due to several converging factors within the world of Artificial Intelligence (AI). In this text, we will explore the impact, role, and obstacles that AI poses on CX and CO, based on key insights and findings from leading consulting firms and academics. Additionally, we will include feedback from Pipeminds' experience working with clients and R&D in our laboratory.


Impacts on Customer Experience and Operations:


Customer expectations are rising. Today's tech-savvy customers expect seamless, personalized experiences across all touchpoints (McKinsey & Company, 2020). According to a study by Salesforce, a staggering 73% of customers anticipate companies to deliver a personalized experience that caters to their preferences. This necessitates a shift from product-centricity to customer-centricity, requiring operational agility and responsiveness (Verhoef et al., 2009). Vonage reports that 60% of customers now expect a company to respond to their inquiries within an hour. These findings emphasize the importance of businesses to have efficient and personalized customer service channels across various platforms. In laymen terms, customers are needier than ever and demand what they want, when they want it or they will go somewhere else because shopping around has never been easier. But, on a positive note customers behaviours can still be tracked and thanks to the help of automation and personalization via AI customer expectations and needs can be met and even exceeded leading to more customer loyalty and higher customer value generation.


Businesses need omni-channel Integration in order to stay in front of their customers. Customers navigate through various channels (online, mobile, physical stores) during their journey. Integrating these channels into a cohesive experience is crucial, demanding operational efficiency and data-driven insights (Bain & Company, 2019) [3]. Data Structure is imperative to ensuring scalability. If a businesses data is unclean and siloed in various locations organizations will have more headaches than wins when they implement AI into their organization. But, if they get their data structure and processes right, then they will minimal struggles reaching their customers across multiple channels from WeChat in China to Third-party Commerce Sites and beyond.


Automation, self-service options, and data analytics are reshaping customer interactions. Customer operations need to adapt by leveraging technology for faster resolution and proactive customer engagement (BCG, 2021) [4]. Whether businesses try for full automation or a hybrid, which Pipemind recommends, AI is definitely able to promote faster resolution and proactive customer engagement when trained on structured data both from within an organization and public data. Simply put, a computer is faster than a human, so when businesses train AI for their unique business use cases, your staff are empowere to provide better customer support.


The Role of Artificial Intelligence (AI)


AI is a game-changer, impacting both CX and operations, especially when businesses train their AI with business use cases that have an end business goal, such as revenue or customer satisfaction. Since a Characteristic of AI is that it learns and improves with time, coupling that with it’s ability to provide personalization. Using AI each customer could then receive a personlized CX when interacting with your brand that aims to achieve an end goad such as maximize lifetime revenue. It could do this via product recommendations, and marketing messages, all of which could leading to higher customer satisfaction (Lemon & Verhoef, 2016) [5].


One ways businesses are facilitating these personalized experiences is through chatbots and virtual assistants. From an operational standpoint, AI-powered chatbots can handle routine inquiries, freeing up human agents for complex issues, improving efficiency (Pirttimaa & Wretenborn, 2019) [6]. Plus, these AI-powered digital assistance are signifantly improved from the chat-bots we have all be subjected to int he past with the evolution of generative AI, so CX and subsiquently customer satisfaction is improved.


In addition, the use of AI-powered knowledge management systems can greatly enhance Customer Operations (CO). These systems can assist staff in. answering questions quickly and accurately, ensuring that they provide consistent brand messaging. As a result, senior staff can focus on strategic planning and not have to worry about whether their staff arehe latest business information. Furthermore, AI-driven knowledge management tools can help reduce training needs by streamlining the onboarding and training process.


Finally, AI’s ability to learn from it’s interaction with customers means businesses AI ROI will improve over time becasue of AI’s predictive analytics capabilities, which will become accurate with time. This means businesses will be able to predict customer needs and proactively address potential problems, enhancing customer experience (Kumar et al., 2018) [7].


Major Obstacles to Overcome:


Despite the potential benefits, challenges remain for businesses when it comes to data silos and integration, AI ethics and transparency, and employee resistance.


Siloed and fragmented data across departments hinders a holistic view of the customer journey, making it difficult to personalize experiences (Van den Berg et al., 2014) [8]. When businesses are thinking about implementing AI to address capitalize on it’s opportunities to solve the changing customer landscape they need to ensure they have good data structured within an architect that will scale with their business so they can continue to get a solid ROI on their AI investment in the future as well. Once the data is organized in a central location and data processes are designed to incorporate future data into the system in a manner it can be used in the future, then a business will be ready, at least from a data perspective, to scale their business with AI and access it’s value throughout it’s organization.


Concerns exist around AI bias, data privacy, and the explainability of AI decisions. Addressing these concerns is crucial for building trust with customers (Bostrom & Bryson, 2014) [9]. The Ethics of AI, and what that will look like as the world becomes more intrenched within this AI revolution will become more defined and businesses need to be ready to adapt those standards. But, from a operational stand point dealing with AI bias (ex. halucination) and transparency is a problem for day. AI, like Large Language Models, are really good at being convincing, both when they are right and wrong. So, it’s important we train and monitor our model to interact with customers honestly and truthfully becasue the last thing you want to do as a business is loose your custoemrs trust. With regards to transparency, people typically fall into two categories, excited or suspicious, when it comes to AI. Transparency helps main trust with both, so businesses should be encouraged to educate their customers on their AI involvement and what it means for their customers value.


Shifting from traditional practices to AI-powered operations might lead to employee resistance. Effective training and change management are necessary (Eriksson et al., 2019) [10]. Don’t be fooled, AI will not replace humans, and your staff needs to hear this. It will however replace humans from doing certain tasks, businesses will need to retrain people so they add value to your end customer either directly or indirectly in a different manner. It’s also a smart idea to keep your organization engaged and invovled in the direction of where your company is moving, a poor work environment can reak havoc on your organizations productivity.


Conclusion:


The customer experience landscape is evolving rapidly. Businesses that prioritize customer-centricity, integrate technology seamlessly, and address ethical considerations surrounding AI will be well-positioned to thrive. Continuous collaboration between CX and operations teams is essential for navigating these complexities and creating lasting customer value.


References:

Bain & Company. (2019, April 10). The omnichannel imperative: Why retailers need to get serious about seamless customer experiences. https://supportyourapp.com/blog/my-supportyourapp-career-growth-max/ 

BCG. (2021, January 19). The customer experience imperative in 2021. https://www.bcg.com/publications/2023/five-principles-to-improve-customer-experience 

Bostrom, N., & Bryson, J. (2014). What is artificial intelligence? Ethics and policy of artificial intelligence. https://global.oup.com/academic/product/superintelligence-9780199678112

Eriksson, T., Wincent, J., & Witell, L. (2019). How firms overcome resistance to customer experience innovation: A capability perspective. Journal of Service Research, 22(3), 394-414. https://www.sciencedirect.com/science/article/pii/S0040162521000263

Kumar, V., Manchanda, P., Chintagunta, P. K., Grewal, D., & Rust, R. T. (2018). The transformative business of artificial intelligence. Journal of Marketing, 82(1), 71-97. https://link.springer.com/chapter/10.1007/978-3-031-24687-6_124 

Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-

0 views0 comments

Comentarios


bottom of page