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The Impact of AI on Value Creation and the Role of Customer Service Representatives

Updated: Oct 11, 2023

In an era where artificial intelligence (AI) is rapidly reshaping industries, how will the essence of human interaction in customer service evolve to continue creating value? The ascent of AI promises significant transformations across all sectors and job functions, particularly in customer service. While AI holds the potential to trim costs and boost efficiency in contact centers, a counter-narrative emerges:

When does customer-facing AI negatively impact the value created by human-assisted service?

This article delves into the impact of AI on customer service representatives and unravels its implications for value creation.

Technology & Call Reduction

For the past two decades, I have been actively involved in both workforce management and designing and deploying technologies meant to drive call volumes out of contact centers. My first call reduction initiative leveraging technology involved removing payment calls from a telecommunications provider, NOW Communications. NOW Communications had an intriguing business model that provided landline phone service to those who did not qualify for service with BellSouth. It was a pre-paid service offering where customers were required to make deposits before receiving their phone service.

When we acquired NOW Communications, most of the inbound customer service call volume routed to our agents was for making payments. Now, occasionally, you'd need to assist people with trouble making a payment, but most of these calls were purely transactional, not requiring human-to-human interaction. Hence, this call type was a logical candidate for automation - let the machines (our IVR) take care of servicing the customer for this transaction type.

After building and deploying payment processing in the IVR, we sought to actively force these transaction types to be gated in the IVR. At first, we did allow people to bypass the IVR; agents could assist the customer by helping them navigate how to process their payments. Shortly after deploying the IVR functionality, we stopped allowing agents to process payments on behalf of the customer.

Yet, we still found some customers attempting to route around the IVR. Why? They didn't have the money for the full payment and essentially wanted to negotiate a promise to pay or make a partial payment. Logical, so we enhanced the IVR to take partial payments, giving more flexibility to answer the customers' needs.

End of story: after a short period of agents helping customers navigate the IVR to process their payment, "agent-assisted payments" ended, permanently removing payment calls from agent-assisted call volume. This transition marked a significant milestone in the journey toward automation, shedding light on the delicate balance between human interaction and machine efficiency. As we zoom out from this specific case, the broader narrative of AI's role in modern customer service becomes increasingly pertinent.

Why did we choose to push this call type to 100% automation? In this example, the company was losing money; the sheer volume of payment calls was a meaningful driver of losses. We were talking person to person to process a payment, which provided little value when we looked at the relationship between the customer and the company. Value was initially created when agents agreed to a partial payment (bypassing formal policy). Customers found value in talking agents into taking a small payment to keep the service on for a few more days. Once the partial payment enhancement was added to the IVR, there was little value left to be created by a person speaking with another person.

After eliminating payments, we still had many calls where it made sense for service representatives to speak with customers. Interactions where customers needed service repaired, their service moved to a new location, or they wished to upgrade or downgrade their plan were all perfect for human-to-human interaction. Customer-facing automation (for payments) allowed customer service agents to focus on adding value.

AI and Contact Center Value

As generative AI gains traction, various sectors are vying to harness these technologies to refine their operations. The applications are broad, encompassing enhanced security, anomaly detection, advanced data analytics, tailored next-best actions, real-time interaction analytics, improved quality, and compliance automation. To delve into value creation, I'll explore two primary ways generative AI can add potential value:

  • Agent-assisted AI

  • Customer-facing AI

Agent-assisted AI involves bots designed to support an agent's ability to handle customer interactions, driving greater efficiency and process accuracy while opening the door to managing more complex customer inquiries.

With customer-facing AI bots, the focus invariably shifts to attempting to deflect calls, reducing agent-assisted volumes.

Value and Agent-Assisted AI

Employing AI to aid customer service agents represents a win-win-win scenario for the agents, the company, and, ultimately, the customers. Contact center agents have long grappled with navigating disparate platforms, knowledge base systems, policies, and other desktop resources to address customer inquiries. Agent-assisted AI that sifts through knowledge and guides agents alleviates the agent's burden, allowing them to resolve customer issues more effectively. This not only quickens the process but also enhances the agent's ability to manage a broader array of call types. From an agent’s perspective, this translates into a more supportive work environment with reduced stress and complexity. From a company's perspective, it leads to heightened operational efficiency, potential cost savings, and an enriched customer service experience. The synergy of these wins heralds a positive shift in the customer service domain, marking a win on multiple fronts.

These attributes all contribute to added value.

Value and Customer-Facing AI

While the value in agent-assisted AI is evident, the same cannot be said for customer-facing AI. Indeed, many of us prefer the autonomy of rebooking a flight, making a payment, or checking on the status of a repair through automated means. However, those predicting a significant dip in call volumes due to the enhanced capabilities of AI bots often overlook a vital psychological aspect. When reaching out for service on matters that matter to us, we seek to engage with someone who cares. Although AI, like ChatGPT, maintains a polite demeanor, it falls short of human empathy. Attempts to train AI to mimic empathy often come across as disingenuous, potentially even insulting, when humans discern they are interacting with a machine.

The marginal value created through enhanced customer-facing AI has limits, given the extensive automation already in place over the last 20+ years. While self-service containment rates vary by industry, some have already achieved over 90% containment using technology. On the flip side, there are industries with containment rates below 50% - but this doesn't automatically signify a vast untapped potential for further automation. Some sectors inherently deal with matters of significant personal concern, such as health, financial well-being, or other critical life areas where the need for human interaction is paramount. Customers in these domains often seek the reassurance, understanding, and empathy that only a human conversation can provide, reflecting a fundamental preference for human engagement over automated interactions for matters of high importance. Moreover, in time-sensitive situations like ensuring on-time arrival to destinations, individuals tend to prefer human assistance to navigate through the uncertainties, valuing the nuanced understanding and immediate responsiveness that human agents provide.

Customer-facing AI: Potential Value Destroyer?

The human element in customer service cannot be overlooked. While customer-facing AI may offer incremental efficiencies and cost-saving measures, an over-reliance on AI technology can come with a significant downside — erasing the human element in customer interactions.

Take the case of Frontier Airlines, for instance. In November 2022, the company announced eliminating its call center support, presumably aimed at cutting costs. Want service? Leverage the website and its chatbots. When they announced eliminating call center support, the Frontier Group Holdings Inc (NASDAQ: ULCC) stock was trading at approximately $13/share. As I write this, Frontier trades at under $5/share. This drastic decline serves as a cautionary tale for corporations that are quick to replace human capital with automation without fully understanding the long-term implications of value created through human-assisted customer service.

The research on value creation is extensive.

Customer Experience and Value Creation

Lemon & Verhoef (2016) highlight the essential nature of understanding customer experience and its journey over time in an era of diversified customer touchpoints across multiple channels and media. The study posits that while managing the entire customer journey is challenging in today's customer-empowered scenario, organizations are making strides towards adopting flexible models to manage customer experiences better. These evolving organizational frameworks aim to break down silos and foster a more customer-centric approach, potentially leading to enhanced value creation for both the customers and the firms.

Despite the automation potential of AI, the essence of human interaction in customer service continues to be a pivotal element in value creation. This is especially significant in light of the nuanced understanding of customer experience and its management, as discussed by Lemon & Verhoef (2016). Their work serves as a crucial backdrop for comprehending the indispensable role of customer service representatives in delivering personalized experiences and fostering enduring customer relationships, thereby contributing to value creation.

Service Logic and Value Co-Creation

Grönroos (2008) explores the underpinning logic of value co-creation within the service realm, critiquing some of the foundational premises of what's known as service-dominant logic. He suggests that a deep understanding of the interaction between service providers and customers is crucial to identifying where and how value co-creation occurs, warning against a superficial approach that might render value co-creation a concept without substance.

This nuanced understanding of value co-creation is crucial for advancing practical business strategies and marketing approaches. Over-reliance on customer-facing AI could potentially overlook the nuanced interplay between service providers and customers that is central to value co-creation. This balance becomes even more critical as firms endeavor to leverage AI for enhancing customer engagement.

Managing the Co-Creation of Value

The process-based framework for managing value co-creation outlined by Payne et al. (2007) provides a foundational understanding of how organizations can leverage relationships between customer service representatives and customers to foster value creation. In today's digital landscape, the role of Artificial Intelligence (AI) in supporting customer service representatives is becoming increasingly significant. AI tools can enhance the capabilities of customer service representatives, making the process of value co-creation more efficient and tailored to individual customer needs. However, there's a fine balance that needs to be maintained to ensure that the essence of human interaction, which is central to value co-creation, isn't overshadowed by the use of technology.

In value co-creation, the interaction between customers and representatives is crucial as it drives learning, engagement, and, ultimately, the co-creation of value. While AI can provide data-driven insights and automate routine tasks, over-reliance on AI, especially in customer-facing roles, could potentially erode the relational value inherent in human interactions. Therefore, it's imperative that organizations judiciously integrate AI in customer service operations, ensuring that the technology serves as a support to human representatives rather than a replacement. This balanced approach aligns with the evolving needs of customers and the operational efficiencies sought by organizations, thereby fostering a conducive environment for sustainable value co-creation.

WFM Labs & AI

At WFM Labs, we prioritize the blend of automation with an employee-first approach. Businesses aim to enhance shareholder value through revenue growth and profit optimization. However, boosting profits by reducing human capital can be short-sighted. When considering investments in AI technology, the discussion shouldn't just revolve around the ROI from cost reduction. It's vital to analyze whether removing a certain transaction type cuts costs or if it also has the potential to lower revenue.

Transitioning customer service to mostly automated responses and chatbots can strip away the essence of service. The human touch, the ability to empathize, and the skill to offer tailored solutions are uniquely human qualities that machines can't replicate.

On the flip side, AI has the potential to bring about operational efficiency and cost savings in customer service operations by enhancing how our agents service customers. This distinction is essential. The human involvement in understanding customer needs, delivering personalized experiences, and co-creating value is crucial for maintaining revenue growth and building strong customer relationships. The goal should be to strike a balance between AI-driven automation and the human touch provided by customer service representatives to maximize value creation.


  • Grönroos, C. (2008). Service logic revisited: who creates value? and who co‐creates?. European Business Review, 20(4), 298-314. Link

  • Lemon, K. and Verhoef, P. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. Link

  • Payne, A., Storbacka, K., & Frow, P. (2007). Managing the co-creation of value. Journal of the Academy of Marketing Science, 36(1), 83-96. Link

  • Siltaloppi, J. and Nenonen, S. (2013). Role configurations in the service provision process: empirical insights into co‐creation of value. International Journal of Quality and Service Sciences, 5(2), 155-170. Link

  • Zhang, T. (2019). Co-creating tourism experiences through a traveler’s journey: a perspective article. Tourism Review, 75(1), 56-60. Link



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