Transitioning Customer Experience Towards Agentic Artificial Intelligence
The implementation of Generative AI (GenAI) in customer service has offered valuable insights for businesses transitioning to Agentic AI. Here's a look at some key lessons and considerations for a smooth transition.
Key Lessons from Implementing GenAI in Customer Service
- Embracing Imperfection as a Starting Point
Traditional enterprise rollouts often favor meticulous testing and gradual deployment. However, embracing imperfection as a starting point can facilitate quicker adaptation and improvement of AI systems.
- Improving Efficiency and Experience
GenAI streamlines customer service operations by automating responses and enriching knowledge bases, leading to faster, more personalized service experiences. This efficiency focus can guide Agentic AI in prioritizing customer satisfaction.
- Enhancing Agent Performance
AI tools like GenAI can assist customer service agents by providing real-time assistance, improving accuracy, and reducing cognitive load. This empowers agents to focus on complex issues and deliver empathetic support.
- Dynamic Knowledge Management
GenAI can analyse trends and generate new content, ensuring that help portals remain current and relevant. This approach can be extended to Agentic AI for continuous learning and improvement.
- User-Centric Design
Agentic AI should consider user motivation and agency, focusing on how users learn and interact with AI systems. Understanding different types of user motivation and fostering cooperation among users is crucial.
- Integrating AI into Workflows
Tools like interaction summaries and sentiment tracking are most effective when embedded directly into workflows, reducing low-value tasks and enhancing overall customer experience.
Transitioning to Agentic AI
As businesses consider the move to Agentic AI, there are several key areas to focus on:
- User-Centric AI
Design systems that appreciate human agency and cater to different user motivations and goals.
- Collaborative Decision-Making
Implement AI to foster cooperation among users and provide decision-making advice while mitigating cognitive biases.
- Continuous Learning
Use dynamic knowledge management to ensure AI systems remain updated and relevant.
- Integration with Existing Systems
Embed AI tools into workflows to enhance efficiency and reduce grunt work for agents.
- Emphasis on Human Touch
Ensure that AI solutions enhance, rather than replace, human interaction and empathy in customer service.
Processes will need to be redesigned to remove unnecessary steps when implementing agentic AI, according to recent reports. Customer service reps will need to learn new ways of working as process flows change to map with the autonomous behaviours of agentic AI systems.
Security has an even more important role in agentic AI as the stakes are much higher. Companies need to fully test current security controls to ensure they work with agentic AI technology. They must also consider and protect against scenarios in which the agents interface with outside contacts and can be duped into completing actions beyond their intended use.
Organizations evaluating the use of autonomous AI agents should be aware that the bulk of the work in implementation consists of operational tasks such as change management, people management, process reengineering, and orchestration of a cross-functional team.
According to Deloitte, 58% of businesses are highly concerned about using sensitive data in GenAI models and managing data security. This concern is even more pertinent in the context of agentic AI, where the AI is taking autonomous action. Companies carrying a data debt in the form of inconsistent, incorrect, outdated, or incomplete data across systems should address this issue before implementing agentic AI.
Early GenAI adopters have learned that they need to do their own adversarial tests of their GenAI systems. This practice should continue with agentic AI to ensure system reliability and security.
While off-the-shelf GenAI systems that translate customer communications or summarise customer interactions are low-risk investments, according to a 2024 report by McKinsey & Co., the captured value is modest, only about 3% to 5% of the whole customer operation. To achieve even greater results, customer service leaders are contemplating the move to agentic AI.
Some customer service centers have reduced the time to issue resolution by 50% when using a GenAI assistant to resolve issues requiring access to product or diagnostic solutions or to individual customer information. GenAI-driven copilots can also improve productivity for customer service reps, but gains may be incremental and could max out quickly. To achieve sustained improvements, customer service leaders are looking towards agentic AI.
Agentic AI could route issues to cross-functional AI agents, increasing process efficiency and boosting customer satisfaction. An example of an agentic system is one that spots a customer delivery that's behind schedule, alerts the customer, and offers a discount to hedge against disappointment.
Only 23% of businesses say they're highly prepared for managing GenAI risk and governance, according to Deloitte. As businesses transition to agentic AI, it's crucial to invest in training and change management to ensure a smooth transition and to maximise the benefits of this transformative technology.
- The implementation of Generative AI (GenAI) offers valuable lessons for businesses as they transition to Agentic AI, with user-centric design, dynamic knowledge management, and integrating AI into workflows being key areas to focus on.
- As businesses adopt agentic AI, it's essential to address security concerns, redesign processes, and prioritize continuous learning and collaboration to ensure a smooth transition and maximize the benefits of this advanced technology.
