AI in Customer Experience: Key Updates and Insights for Industry Leaders

Integrating AI and automation has become a game-changer in enhancing customer experience (CX) across industries. Over the last quarter, advancements in these technologies have continued to redefine how businesses interact with customers while driving efficiency, personalization and responsiveness to new heights.

Improved capabilities and broader accessibility have strengthened AI's role in analyzing and leveraging vast amounts of data. This has allowed AI-augmented automation to seamlessly execute tasks at unparalleled speeds, paving the way for more informed and effective decision-making processes. This synergy between AI and automation is streamlining operations and elevating the overall quality of customer interactions.

However, the growing reliance on AI and automation brings challenges and considerations that businesses must navigate carefully. This year has highlighted the necessity for clean, organized data, as it underpins the reliability of AI-driven insights. It has also been a reminder that human expertise and nuanced understanding remain irreplaceable, particularly in complex and sensitive areas. Balancing technological innovation with human insight remains essential for maintaining customer trust and ensuring that advancements in AI and automation continue to enhance, rather than detract from, the CX.

AI and automation

As discussed in Priority #3 of our full Customer Experience Priorities report from WWT Research, harnessing the power of AI is key to creating hyper-personalized experiences and gaining operational efficiencies. AI has become integral to the broader data spectrum, encompassing everything from enterprise data warehousing to predictive analytics. This growing AI maturity and a fear of missing out have boosted confidence in AI autonomy within organizations.

Automation is the workhorse of the AI brain, enhancing decision-making processes at speeds and volumes beyond human capability. For example, algorithmic trading, which automates stock trades based on pre-programmed rule-based instructions, highlights AI's ability to fundamentally transform an entire industry. However, the 1987 Black Monday crash reminds us of the risks and the importance of regulatory oversight and logical guardrails.

It is increasingly crucial for enterprises to have clean, well-organized data to ensure practical AI applications and foster high confidence in predictive decisions. In our CX Priorities report, we provide specific steps and guidance for developing a sound data strategy, which includes assessing your data availability, reliability, quality and security. Organizations should start by determining what data is available to them, where it's stored, what's missing and what's required to gain access. From there, they can begin to evaluate their data quality and hygiene.

AIOps (Artificial Intelligence for IT Operations) is revolutionizing root cause analysis in operational efficiency. By correlating numerous issues and quickly identifying problem areas, AIOps enables swift remediation and can promptly highlight and direct problems to the appropriate experts. This automation streamlines operations and improves the overall CX by minimizing downtime and resolving issues more efficiently.

The maturity of automation technologies has evolved from basic scripting to advanced, AI-enabled robotic process automation (RPA) or Intelligent Automation (IA), driving efficiencies across various sectors, including healthcare and manufacturing. Nevertheless, it remains crucial to exercise ongoing vigilance, ensuring that the adoption of these technologies aligns with their established capabilities while safeguarding institutional knowledge and maintaining customer confidence.

Human in the loop

An organization's data forms the backbone of AI applications and is essential for enhancing confidence in predictive decision-making. Equally important is human involvement in the AI process. 

Even with significant improvements in AI interactions, complete automation can lead to a sterile and impersonal CX. For example, one healthcare system specializing in oncology is revamping its call center to prioritize human agents supplemented by AI tools. This approach ensures patients receive empathetic and urgent care, reinforcing the importance of the human touch in critical services.

Keeping a "Human in the Loop" (HITL) is an example of a strategy that combines AI and human intelligence to create machine learning models. Humans are involved in setting up the systems, tuning and testing the model to improve decision-making, and then acting on the suggested decisions. This approach ensures the AI system constantly learns and improves while also ensuring the customer's needs are correctly understood and met.

Involving humans in the AI process can significantly improve the CX in several ways:

1. HITL can improve quality and accuracy. Humans can fix errors, provide context or expertise, and add a human element. This results in more accurate and high-quality interactions, which can significantly enhance the CX.
2. Research has indicated AI can create more empathetic experiences. However, without careful consideration of the specific roles that AI will have, there's a risk of developing worse outcomes for customers. Involving humans in the process ensures experiences do not lose the empathy and comprehension only humans can offer.
3. Employees taught how to work with AI can be more productive, reduce workloads, make fewer mistakes, increase efficiency and improve job satisfaction. HITL can also free up time for employees to acquire new skills and continue learning. These benefits ultimately impact customers.
4. HITL can reduce ethical and legal risks. This is especially vital in socially sensitive sectors like healthcare, justice and finance, where decisions have real-world effects.

Subject matter experts (SMEs) play a crucial role here, as their expertise and familiarity with business processes ensure that AI tools complement rather than replace their functions. For instance, radiologists can leverage AI to enhance diagnosis and treatment plans in healthcare, maintaining their essential role in patient care. This involves developing a deep understanding of the business to defend against the fear of automation replacing human roles.

As AI and automation evolve, their impact on CX will hinge on striking a balance between leveraging technology for efficiency and preserving the essential human element that defines quality service.

Personalization

Within personalization, the concept of the "next best action" has emerged as a key trend over the past quarter. This approach identifies and addresses customer needs across multiple touchpoints, whether in physical branches, online platforms, mobile apps or enrollment systems. The ability to offer personalized recommendations requires sophisticated data aggregation and analysis, enabling businesses to recognize triggering events and nurture customer journeys effectively. For example, let's suppose a customer opens a checking account and subsequently interacts through SMS and phone calls. In that case, businesses can gauge potential opportunities and guide them toward relevant products or services.

Virtual agents and conversational user interfaces, powered by large language models (LLMs), can enhance these personalized interactions by handling various inquiries and interacting more naturally and effectively with customers based on their characteristics. AI-driven agents provide a scalable solution that reduces the burden on human agents and increases customer engagement before escalating to higher-cost interactions.

Additionally, in the full CX Priorities report, we dive deeper into how an omnichannel and unified commerce strategy can help leaders eliminate silos, drive advanced personalization and create seamless experiences across digital and physical channels.

AI consolidation

Since the beginning of 2024, the consolidation of data and decision-making power driven by large cloud providers presents both opportunities and challenges. While it can lead to faster and more efficient outcomes, it also raises concerns about regulatory oversight and the potential for misinformation.

The risks associated with AI market consolidation can significantly impact CX and engagement in several ways:

• Personalization versus privacy: AI offers personalization but at the cost of compromised privacy, also known as the "personalization-privacy paradox." While customers appreciate personalized experiences, they also value their privacy. If a few large companies control most of the data, it could lead to privacy concerns.
• Stifling innovation and diversity: The centralization of AI expertise could decrease the diversity of AI applications. Smaller companies and startups might need help competing with large corporations' resources and data access, which could limit the variety of customer experiences and engagement strategies available in the market.
• Quality of CX: Large tech companies have successfully used AI to understand, shape, customize and optimize the customer journey. However, if these companies dominate the market, it could lead to a lack of competition, potentially resulting in a decline in the quality of customer experiences.
• Trust and loyalty: If customers perceive that their data is being misused or their privacy is compromised, customer trust, engagement and loyalty could decrease.
• Cost of implementation: The high cost of implementing AI, especially regarding data management and regulatory compliance, can be a barrier for smaller companies. This could limit the range of CX and engagement strategies available.

AI in the contact center

AI has significantly transformed contact centers (CC) over the past quarter, streamlining operations and enhancing CX. The top three benefits of AI in contact centers include speed, data utilization and agent empowerment.

First, AI improvements reduce complexity and accelerate problem resolution through chatbots' natural language processing (NLP), enabling faster interactions and solutions, real-time translation, and excellent customer service.

Second, AI facilitates the collection and analysis of vast amounts of customer data, breaking down silos and creating a cohesive customer data journey. This integration allows businesses to anticipate customer needs, personalize interactions and proactively address issues. For instance, when a customer contacts a call center, the system can immediately recognize a past-due order and tailor the conversation accordingly. Sentiment analysis further enhances this personalization by gauging customer emotions and adjusting responses, such as offering a discount after a frustrating experience.

AI's third significant contribution to the contact center is handling the increasing complexity of calls that reach human agents as more straightforward inquiries are resolved through self-service automation. This shift means agents manage more intricate issues, necessitating tools to improve efficiency and effectiveness. AI provides real-time guidance, automatic call summarization and seamless integration with CRM systems, reducing post-call administrative tasks and enabling agents to focus on delivering high-quality service. Technologies like Google's Contact Center AI (CCAI) suite and platforms like Genesys and Five9 offer comprehensive solutions covering self-service, real-time agent support and backend data integration.

Leveraging AI in the contact center offers nearly limitless possibilities. The key is to prioritize use cases that add the most value to the CX, whether that's increasing operational efficiency, empowering agents or delivering improved customer service.

The risks of LLM sophistication to the CX

The recent advancements in AI capabilities and the increased sophistication of LLMs align with Alan Turing's prediction that computers could eventually engage in conversations in a manner indistinguishable from true humans. Generative AI's recent breakthroughs in this domain present new possibilities and challenges, such as the risk of deepfakes. Organizations are now being forced to leverage AI as a safeguard against AI.

The rise of AI and biometrics also brings challenges, particularly concerning identity verification and security. While biometrics and behavioral biometrics have enhanced application processes, the potential for deepfakes to recreate faces or voices poses significant security risks. Implementing liveness tests and monitoring out-of-character behavior are critical measures to mitigate these risks.

Moreover, identity and access management (IAM) are evolving with the growing adoption of password-less systems. Traditional passwords are prone to security vulnerabilities and user management challenges. Innovations such as Okta, OneID and zero-trust frameworks are making CX more seamless across different platforms, reducing the need for frequent password resets and enhancing overall security. This shift addresses customer service issues and reduces the risks associated with password-based authentication, contributing to a more secure and user-friendly CX.

Conclusion

At WWT, we recognize the challenges, benefits and risks our customers carefully navigate in their AI journey. Through our pragmatic approach and methodology,  we support businesses in driving and experiencing significant advancements in CX while balancing technological efficiencies with the essential human element.

To fully harness AI's transformative potential while preserving the human touch that defines quality service, businesses must adopt a strategic approach prioritizing clear objectives and continuous improvement. We help businesses embrace these technologies with a customer-centric mindset, ensuring every innovation enhances the CX and builds lasting relationships. 

Now is the time to invest in AI and automation, personalization, and AI in the contact center — all to drive growth, operational efficiency, and a future where technology and human expertise work together seamlessly to exceed customer expectations.