Feature
The Gist
- Navigational challenge. Understanding the vast landscape of AI in customer experience can overwhelm CX leaders. They must sift through the noise to find valuable applications.
- People and skills. CX leaders need to invest in reskilling and nurturing talent to capitalize on AI's capabilities. Soft skills like empathy and communication remain irreplaceable.
- Planning for growth. Balancing long-term AI ambitions with short-term CX improvements is essential. Leaders must cut through the hype to focus on impactful AI investments.
No doubt you have read or seen a lot about the impact of AI in customer experience and its potential. Discussion ranges from personalized customer service chatbots to virtual CX assistants and other AI-powered tools to predictive analytics assessing when customers are likely to churn. The list is long.
Before we get into it, an apology to our readers. Sorry, but we are not going to list the top 50 use cases for AI-enabled CX. We’re confident that this has been done and repeated many times already. In fact, we see these use cases as part of the challenge leaders are going to face: detecting the signal through the noise.
The AI phenomenon we are witnessing reminds us of the mid-90s. New tech companies were popping up every week, and organizations struggled to keep pace with the rapidly changing landscape. Little did we know that this period would be dwarfed by the AI phenomenon that is now beginning to transform the business world. Every information-intensive aspect of every job will be AI-enabled within the next 12-24 months, making it crucial for leaders to become AI-ready. This mindset applies as much to CX as it does to anything else.
The rapid rise of tech companies in the '90s was just the beginning of an even broader technological revolution. Today, ChatGPT's astounding adoption rate, reaching 200 million users in a mere eight months, exemplifies the exponential growth of AI. This AI and machine learning revolution presents an unprecedented opportunity for CX leaders to become AI-ready and embrace the changes that will fundamentally reshape the economy.
The Rapid Pace of Adoption
The table below illustrates the remarkable pace by which this technology is being adopted relative to several well-known consumer technology products.
An Ongoing Discussion on AI in Customer Experience
This piece is just the first installment in a series of articles and research dedicated to exploring the fascinating world of AI in CX and its impact. As this technology quickly evolves, it is essential for CX leaders to stay ahead of the curve and continually adapt to meet the demands of customers.
Throughout the series, we will delve deeper into the questions we've raised in this article and explore how they relate to various aspects of CX. Customer-centricity and experience have become a cornerstone of modern business success, and the potential role of AI in enhancing CX cannot be overstated.
Here we'll frame what CX leaders need to do to seize the transformative potential of AI by leveraging “enterprise ready” AI tools and platforms and creating AI-enabled customer experiences. This transformation requires us to address essential questions that will not only determine the future of CX but also shape the very fabric of CX in an AI-driven world. In doing so, we will use a framework from our 2016 book on customer success titled “Competing for Customers.” The framework examines AI’s impact and implications on CX people, planning, processes, procedures and platforms.
Related Article: Implementing AI in Omnichannel Strategies for Seamless Customer Experiences
AI in Customer Experience: People
To harness the full potential of AI in customer experience, CX leaders must identify how AI can be integrated seamlessly into every aspect of how employees are managed, including recruitment and talent acquisition, employee development and retention and performance management. The essential question is, how can organizations AI-enable the delivery of an optimal employee experience? Embracing automation, data analytics and predictive modeling can significantly optimize workflows, leading to cost reductions, increased productivity and heightened focus on what is truly strategic.
The flip side of AI-enabling the key pillars of human resources is the empowerment of CX teams to drive appropriate AI enablement into every customer interaction and every supporting business process, whether it is “front office” or “back office.” The goal, naturally, is to lessen administrative burden, alleviate mundanity and apply more attention to what impacts customer outcomes.
As CX organizations strive to become AI ready, attracting, nurturing and empowering the right talent to capitalize on these new capabilities becomes pivotal. Leaders must invest in reskilling and upskilling employees to equip them with the tools necessary to thrive in an AI-driven work environment. We believe that the following skills will become even more critical in this AI-enabled world:
- Communication: AI can be used to automate tasks and improve the conveyance of information, but it cannot replace human communication. Businesses need employees who can communicate effectively with customers, both in person and online. AI can’t compete with the agility, creativity and originality of the human brain — at least not yet.
- Empathy: Businesses need humans who can understand and respond to the nuances of customer emotions and feelings.
- Problem-solving: Businesses need employees who can think outside the box, apply intuition, exercise common sense reasoning, and come up with truly break-through solutions to vexing customer challenges.
- Collaboration: Businesses need employees who can work effectively with others, inside and outside of the organization, to find and achieve common goals. And they need to be able to do so ethically and morally. AI is simply not able to handle the inherent abstraction and ambiguity of this.
- Adaptability: Employees need to be able to adapt to new technologies and processes, and they need to be able to acquire new skills as needed.
- Domain expertise: Businesses need employees who have deep knowledge of their industry or field and who can apply this knowledge to improve CX and ensure AI-enabled processes are yielding high quality outcomes.
Questions CX Leaders Should Answer: People
- What are the skills and knowledge gaps that need to be addressed in order for our people to be successful in an AI-enabled environment? This includes both technical skills, such as data science and programming, as well as those soft skills where AI is less than fully capable.
- How can we create a culture of continual learning and development so that our people can keep up with the pace of change in AI? This includes providing access to training and resources, as well as creating an environment where employees feel comfortable asking questions and taking risks.
- How can we ensure that our people are ethical and responsible users of AI? This includes setting clear guidelines and expectations, as well as providing training on the ethical and moral implications of AI.
- How can we mitigate the risks of bias and discrimination in AI systems? This includes carefully designing and testing AI systems, as well as carefully monitoring them for bias.
- How can we ensure that AI is used to augment human capabilities, rather than replace them? This means finding ways to use AI to automate tasks that are repetitive or dangerous and using it to do things that it actually can do better than humans (e.g., big data analytics) while freeing up humans to focus on more creative and strategic work.
AI in Customer Experience: Planning
Understanding the impact of AI on customer value at each step in the customer journey becomes paramount as we unveil novel opportunities to create lasting connections with our target audiences, elevate customer value and deliver enhanced and personalized experiences to customers.
Leaders have to reimagine their approaches to delivering customer value and chart new paths for sustainable growth. Balancing long-term aspirations of an intelligent experience engine with short-term opportunities to improve today’s customer experiences becomes a delicate art. The challenge lies in charting a course for organizational stability and profitability while fostering a culture to embrace the efficiency and innovation that AI can deliver.
It doesn’t take a genius to predict that there is not going to be a paucity of ideas. One of the biggest issues for CX leaders in the coming months, if not already, will be what to do with the explosion of suggested AI use cases coming across our desks. It’s already a challenge to weed through the sheer volume of AI possibilities and focus on what is worthy of thoughtful examination for capital investment and resource allocation.
What is possible and impactful versus interesting but fanciful or impractical? CX leaders need be able to cut through the noise to get to the signal. They then need to be strong portfolio managers of different types of AI investment for running the business, improving the business, growing the business and innovating the business — all within the context of CX.
The new generation of CX leaders will have to effectively communicate a clear focus and stance on how to apply AI to CX throughout the organization. They must articulate a cohesive AI-enabled CX vision that unifies all stakeholders and the organization's broader AI ambitions. This includes educating employees about the potential of AI, dispelling misconceptions and fostering a collaborative environment for a workforce that is empowered to contribute to AI strategies to yield customer and company results.
Questions CX Leaders Should Answer: Planning
- How do I best empower my organization to drive an AI enablement action plan? These plans need to balance the strategic and the tactical — and be based on proven, scalable solutions.
- How do I most effectively communicate our CX AI enablement vision in terms that improve our outcomes as well as customer outcomes, and in a way that considers the overarching AI ambitions of the company.
- What change management activities are going to be most effective on our journey to AI enablement?
- How will I strike a balance between supporting the business today and fostering innovation for the future? There are strategic and tactical aspects to both.
- How do I best resource the different types of AI enablement investments?
AI in Customer Experience: Processes
Given the increasing importance of CX to organizations of all sizes and across all industries, providing an optimal experience requires businesses to collect and analyze data from their customers across all touchpoints, including online interactions, in-store visits and customer service engagements. These data can be used to identify customer needs, preferences and pain points, as well as to predict future behaviors and trends. Notionally, AI presents an enormous opportunity to improve data collection and analysis, offering real-time insights and highly personalized recommendations.
However, there are some significant challenges that need to be addressed when using AI to enhance CX. One major challenge is ensuring that the data is of high quality. If the data is not accurate, complete, or timely, AI models will not be able to make accurate predictions or recommendations. This requires a robust data validation process and continual monitoring to ensure integrity. Another challenge is integrating AI solutions with existing systems and processes. This can be difficult, especially if existing systems and processes are not well-documented or if they are not fundamentally compatible with the AI solution. Significant changes to the existing infrastructure may be necessary, which can be time-consuming and costly. Furthermore, the integration must be done in a way that maintains the security and privacy of customer data, adhering to regulations and industry standards.
Forward-looking CX leaders must answer a number of questions to properly address these challenges. They must consider what types of data are most relevant to their goals, how to ensure that data is collected and processed ethically, and what technologies and methodologies are best suited to their specific needs.
Questions CX Leaders Should Answer: Processes
- How can organizations streamline their data collection processes across various touchpoints to ensure consistency and comprehensiveness? Are there any gaps or redundancies in the current data collection methods that need addressing?
- What mechanisms and processes can be established to continually validate the accuracy and timeliness of the data? How can organizations ensure that the data being fed into AI models remains free from corruption or external manipulation?
- Is there suitable data governance or product and journey data architecture to overcome some of the shortcomings and hazards of AI deployment? AI generally lacks sufficient contextual understanding, emotional understanding, morality and ethics. These gaps create unusual risks that need to be carefully mitigated.
- Given the sensitive nature of customer data, what processes need to be established or enhanced to ensure data security, especially when integrating AI solutions? How can organizations stay updated and compliant with ever-evolving data protection regulations?
- How can organizations establish a feedback loop in their data processes to continually learn from AI-driven insights and recommendations? What processes are in place to act on these insights and iteratively improve data collection and analysis methods?
AI in Customer Experience: Procedures
Albeit not the most exciting of topics, decisions for how to best shape organizational roles, responsibilities and accountabilities are essential in building an effective AI-enabled CX organization. Leaders face the fundamental decision of whether to establish a centralized AI team, designate AI leaders in each CX function, or explore alternative models such as hybrid structures that combine centralized and decentralized elements. Selecting the most appropriate organizational structure will depend on each CX function’s unique context, goals, operational requirements, level of maturity and the overall strategic alignment with the organization's mission and practices.
A robust procedural framework is necessary to ensure responsible and ethical AI adoption. Leaders need to establish clear roles, responsibilities and accountabilities throughout the AI enablement process. This includes defining who will be responsible for data collection, model development, implementation, monitoring and ongoing maintenance. It also involves setting up governance structures to oversee AI projects, ensuring compliance with legal and regulatory requirements and developing guidelines for ethical considerations such as bias mitigation and transparency.
By fostering transparency and adhering to ethical AI principles, organizations can build trust with customers and stakeholders, safeguarding the reputation and longevity of their AI-driven ventures. This trust-building process requires continuous communication with all stakeholders, including employees, customers, regulators and partners to ensure that the AI initiatives align with societal values and expectations.
Questions CX Leaders Should Answer: Procedures
- How should we organize our exploration and application of AI? Should we have a centralized AI function or should we decentralize AI responsibilities across different CX domains?
- What are the roles and responsibilities of the AI function? Who will be responsible for developing AI models, deploying AI solutions, managing AI data and implementing the AI governance model?
- How will we ensure the ethical use of AI? How will we prevent AI from being used to discriminate against customers or employees? How will we prevent AI from yielding incorrect or inappropriate answers because of a lack of proper context or common sense?
- How will we measure the success of our AI initiatives? How will we know if our AI initiatives are improving customer outcomes and improving our own business?
- What is the best AI enablement funding model? Should it be a corporate SG&A line item, or should each functional organization fund the CX AI enablement programs they desire? After all, CX is inherently cross-functional.
- How will we manage the risks associated with AI? What happens if an AI model makes a mistake that harms a customer or an employee?
AI in Customer Experience: Platforms
Let’s be clear. It’s not a question of whether CX leaders should use AI to enable their organizations. Our research tells us that CX organizations are already using AI, whether leaders know it or not. CX organizations are building AI enablement into their existing tools and processes. And employees are leveraging “shadow AI” to assist with their work and customer engagement without company knowledge or approval. Furthermore, “embedded AI” — the use of common third-party tools that are rapidly AI-enabling their products — is driving AI adoption in unknowable and, therefore, difficult-to-control ways.
The integration of all manner of AI into an organization's existing technology landscape is complex. CX leaders need to maintain, enhance and even replace their current systems while facilitating agile AI innovation and deployment. A phased approach, carefully considering the positive and negative consequences of AI adoption, is essential to minimize disruptions, exposure to risk and bad outcomes. Ensuring the trustworthiness, security and quality of AI technology use is of utmost importance.
Organizations must invest in robust cybersecurity measures, AI algorithm validation, data privacy protocols and something akin to “constitutional” AI (or CAI) rules and policy formulation. By instilling confidence in AI systems, CX leaders will successfully drive widespread adoption across the organization and with customers.
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Leaders must explore how AI platforms can amplify the human judgment, context, creativity and originality that makes CX a potent competitive advantage. Embracing AI as a complementary force will empower CX leaders and employees to automate processes, make data-driven decisions and enable even more focus on the high-level cognitive tasks that machines cannot replicate.
Questions CX Leaders Should Answer: Platforms
- How can we integrate AI into our existing technology landscape in a way that is seamless and secure? This includes considering the different types of AI technologies that are available, as well as the specific needs of our organization.
- How can we ensure that our AI tools are trustworthy, reliable and protective of our customers? This includes investing in cybersecurity measures, algorithm validation and data privacy protocols.
- How can we develop the best policies and procedures for managing the use of AI technology, including acknowledging and dealing with shadow AI and embedded AI?
- How can organizations ensure a seamless integration of AI solutions with their existing data management systems? Are there processes in place to handle potential incompatibilities or to migrate data from legacy systems effectively?
- How do we walk the tightrope between building an enterprise ready AI Stack, and managing the ongoing presence of shadow and embedded AI?
What’s Next for AI in Customer Experience?
Embracing the AI revolution is no longer an option but a strategic imperative for CX leaders. By addressing the five key themes of people, planning, processes, procedures and platforms, CX leaders can pave the way for AI readiness in their organizations. This transformative journey requires not only a keen understanding of the technical aspects of AI but also a commitment to fostering a culture of innovation, collaboration and responsible AI adoption.
As we embark on this series of articles, we welcome your input and thoughts to foster an ongoing and dynamic discussion. Together, let us explore the intricate relationship between AI and customer experience and how organizations can forge a customer-centric path in the AI-driven landscape. With your valuable insights and our collective wisdom, we can shape a CX future that harnesses the full potential of AI and deliver unparalleled customer experiences.
In our next AI in customer experience articles, we will:
Unpack the connection between AI and each of the pillars of CX, including customer feedback & analytics, customer engagement, customer success, customer advocacy, customer-centered transformation and customer strategy and operations.
Dive into real-world case studies, examining successful implementations of AI in CX across various industries, highlighting the strategies and critical capabilities that have led to tangible business outcomes.
Analyze the importance of governance and ethics. As AI becomes increasingly integral to CX, ensuring that it is implemented ethically and responsibly will be a pressing concern. We will explore the principles that underpin trustworthy AI deployment.
Engage in thought-provoking conversations. We welcome your input and thoughts as we explore these complex and evolving topics on AI in customer experience. Together, we can create a dynamic and ongoing discussion that propels us all forward in the age of AI readiness.
