Transforming the customer experience with AI at TechSee

TL;DR: Transforming the customer experience with AI at TechSee

• Full automation does not replace human connection in customer service.
• A ‘Voice-First’ approach limits AI’s potential; visual integration is essential.
• Automation must optimize the entire service system, not just one channel.
• Success metrics should focus on customer trust, not just containment.
• Human-machine collaboration is key to the future of customer service.

The importance of human connection in customer service

Human connection remains a crucial element in customer service, despite advances in artificial intelligence (AI). The customer experience is not based solely on efficiency, but also on empathy and understanding. As companies adopt automation, they have realized that the complete elimination of human agents can lead to a loss of trust and service quality.

Automation can handle routine tasks such as billing inquiries and password resets, but it cannot replace the human ability to build long-term relationships. AI can help summarize context and speed up decisions, but it lacks judgment and empathy. Therefore, the sustainable model is not one of agentless AI, but one where AI augments human capability.

This approach allows agents to focus on more complex and meaningful interactions, where empathy and understanding are essential. The key is to remove friction from agents’ work, enabling them to deliver more human and effective service.

Sustainable model: AI augmented by humans

The human-augmented AI model focuses on collaboration between technology and human agents. This approach not only improves efficiency, but also ensures that customers receive quality service. AI can take care of repetitive, low-value tasks, freeing agents to concentrate on more complex issues that require a human touch.

For example, instead of an agent spending time solving simple problems, AI can provide contextual information and data-driven suggestions, allowing the agent to focus on resolving more complicated issues. This not only improves customer satisfaction, but also boosts employee morale, as they feel more valued and less overwhelmed by mundane tasks.

In addition, this model allows companies to measure the impact of AI more effectively. By observing how AI and humans tr

work together, organizations can identify areas for improvement and adjust their strategies accordingly.

Limitations of full automation in contact centers

The idea of a fully automated contact center has proven to be a mirage. Although automation can reduce costs in the short term, it often results in increased expenses in other areas, such as field service. For example, reducing the number of calls may seem efficient, but if that results in an increase in technician visits, the savings quickly vanish.

True efficiency is achieved when AI optimizes the service system as a whole, not just a single channel. This means companies must connect their contact and field service cost structures to see the full picture. Automation must prevent repeat contacts and avoid unnecessary visits, which in turn improves customer satisfaction and reduces costs.

Therefore, it is essential for organizations to measure cost per resolution across the customer journey, rather than focusing solely on reducing calls. This comprehensive approach allows companies to identify where automation can have the greatest impact.

The ‘Voice-First’ approach and its consequences

The ‘Voice-First’ approach in AI development has been common, but it has been shown to limit the technology’s potential. Many organizations start with text and voice, leaving visual integration for later. However, this fragments the customer experience and can lead to failures in understanding context.

AI needs a visual foundation to understand what is really happening. For example, simple questions like “Why is my Wi‑Fi weak in the bedroom?” often end in a technician visit, not because of language failures, but because of context failures. A more effective approach is to integrate visual capabilities from the start, allowing each channel to inform the other.

Visual intelligence provides voice AI with the context needed to diagnose, explain, and verify results with confidence. This not only speeds up resolutions, but also improves the experience for both support agents and customers.

Integration of visual, voice, and text capabilities

The integration of visual, voice, and text capabilities is essential to create effective, seamless customer service. As organizations move toward a multimodal approach, combining these elements becomes the new norm. In 2023, only 1% of AI solutions were multimodal, but this number is expected to

reach 40% by 2027.

The most costly interactions often involve elements that must be seen, such as placing a router or aligning sensors. Visual AI can analyze image data instantly, allowing agents to join the conversation with a complete understanding of the issue. This not only improves accuracy, but also strengthens training data and eliminates the need to redo work.

Leading organizations are implementing standardized steps, such as visual input during interactive voice response (IVR) and visual verification once the issue is resolved. These steps create a feedback loop that improves accuracy and customer satisfaction.

Common mistakes in understanding AI

One of the most common mistakes in understanding AI is the belief that it can completely replace humans in customer service. While AI can automate tasks and improve efficiency, it cannot replicate human empathy and judgment. This has led many organizations to experiment with AI without a clear strategy, which often results in failures.

Another mistake is measuring AI success solely through containment—that is, how many customers remain on a self-service path. This metric does not reflect customer trust in AI. A more effective approach is to measure adoption and trust, observing how many customers choose to use AI again after a positive experience.

Organizations should visualize these funnels internally and link them to return on investment (ROI) to turn AI experiments into lasting successes. The key is to measure customer trust and their willingness to use automation again.

Appropriate metrics to measure the impact of AI

Traditional customer service metrics, such as containment, have proven insufficient to measure the true impact of AI. Instead of focusing solely on how many customers use self-service, organizations should consider metrics that reflect customer trust and satisfaction.

Recommended metrics include usage intent, adoption, and containment. Usage intent measures how many customers tried to use self-service, adoption measures whether they chose it again, and containment assesses how many issues were actually resolved. Together, these metrics provide a more complete view of AI effectiveness.

Organizations that visualize and analyze these metrics are the ones that manage to turn their AI experiments into sustainable successes. By focusing on customer trust and their willingness to use automation, companies can builr a solid foundation for the future.

The future of contact agents in the era of AI

The future of contact agents is evolving as AI takes on more repetitive tasks. Agents are beginning to take on more analytical roles, interpreting the data and insights provided by AI to solve complex problems. This shift also affects how agent performance is measured.

Instead of focusing on handle time or adherence to scripts, organizations are beginning to evaluate diagnostic thinking, collaboration, and the quality of problem resolution. Training focuses on data fluency and visual reasoning, enabling agents to work more effectively with AI.

This shift toward a human-machine collaboration model is fundamental to the future of customer service. Agents will not compete with AI; instead, they will partner with it, using both human judgment and machine intelligence to deliver faster, more reliable, and more human service.

Final Thoughts on the Transformation of Customer Service with AI

The Importance of Human-Machine Collaboration

Collaboration between humans and machines is essential to the future of customer service. As AI becomes a more common tool, it is critical that organizations find ways to integrate technology in a way that enhances human capability.

Challenges and Opportunities in AI Implementation

Implementing AI presents both challenges and opportunities. Organizations must be willing to adapt and learn from their experiences to make the most of AI capabilities in customer service.

The Future of Customer Service: A User-Centered Approach

The future of customer service will be user-centered, combining technology with human empathy. Organizations that manage to balance these elements will be better positioned to deliver exceptional experiences to their customers.