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In the dynamic world of customer service, generative AI is climbing the ranks to become a pivotal element in corporate strategy and is seen as a very strong driving force for moving forward. A staggering 85% of business leaders predict that within the next two years, generative AI will be at the forefront of customer interaction. By the end of 2023, 63% of these leaders expect to have invested in generative AI applications to bolster their customer service teams. If you're navigating the customer service landscape, you might be curious about the role of AI, its implications for your career, and its overall impact on the industry. This guide is crafted to help you understand what generative AI really means and its significance in customer service.
What do we actually mean by Generative AI ?
Generative AI, a branch of artificial intelligence, specializes in creating new content, data, or outputs. It learns from existing data to produce results that mimic human-generated content, including images, text, and even music. While it may seem like a recent phenomenon, the roots of generative AI trace back to the 1950s.
Generative AI vs. Traditional AI
The term AI is often used broadly, but it's crucial to distinguish between AI and generative AI. AI refers to machines designed to perform tasks typically requiring human intelligence, such as speech recognition and problem-solving. Generative AI goes a step further, using data to create entirely new content. As AI continues to evolve, understanding the differences between machine learning, deep learning, neural networks, and large language models (LLMs) like OpenAI's GPT-3 becomes increasingly important. Put simply, the key difference between traditional and generative AI is that generative AI is able to create something new.
The Importance of Generative AI
Generative AI's potential is vast, from fostering creativity and innovation to enhancing productivity across industries. It's projected to boost business productivity by up to 40% by 2023. Its applications range from simulating real-world scenarios for research to suggesting novel molecular structures for scientific advancements. According to a report by McKinsey, Generative AI could enable automation of up to 70 percent of business activities, across almost all occupations, between now and 2030, adding trillions of dollars in value to the global economy and this number itself shows how crucial this tech is.
Generative AI's Role in Customer Service
The influence of generative AI on customer service is undeniable, with the global chatbot market expected to reach $994 million by 2024. Generative AI is reshaping customer service roles, creating opportunities for professionals to transition from traditional positions to roles like Bot Specialist or Conversational AI Specialist. AI will be so crucial for improving customer experience and create more delightful interactions with consumers that few months down the lane, we cannot expect a customer support business that does not tap into this tech
Phases of Implementing Generative AI in Customer Service
The deployment of generative AI in customer service can be broken down into three phases:
- Deploy: Launch conversational AI quickly by utilizing generative AI to scrape support documentation and provide answers to customer inquiries.
- Learn: Monitor bot analytics and insights to implement deeper integrations and increase automated resolutions.
- Improve: Under the guidance of a Director of ACX, delve deeper into AI chatbot strategy and leverage AI to inform business decisions.
The Advantages of Generative AI for Customer Service
Generative AI offers numerous benefits for customer service, including reducing operational costs, providing personalized recommendations, resolving inquiries at scale, and offering multilingual support. It ensures that customer insights are captured and utilized effectively, bridging the gap between CX organizations and customers.
Conclusion
Generative AI is transforming customer service, offering unprecedented opportunities for efficiency and innovation. By understanding and implementing generative AI thoughtfully, customer service leaders can harness its power to enhance the customer experience and drive business growth.
| Decision points | Open-Source LLM | Close-Source LLM |
|---|---|---|
| Accessibility | The code behind the LLM is freely available for anyone to inspect, modify, and use. This fosters collaboration and innovation. | The underlying code is proprietary and not accessible to the public. Users rely on the terms and conditions set by the developer. |
| Customization | LLMs can be customized and adapted for specific tasks or applications. Developers can fine-tune the models and experiment with new techniques. | Customization options are typically limited. Users might have some options to adjust parameters, but are restricted to the functionalities provided by the developer. |
| Community & Development | Benefit from a thriving community of developers and researchers who contribute to improvements, bug fixes, and feature enhancements. | Development is controlled by the owning company, with limited external contributions. |
| Support | Support may come from the community, but users may need to rely on in-house expertise for troubleshooting and maintenance. | Typically comes with dedicated support from the developer, offering professional assistance and guidance. |
| Cost | Generally free to use, with minimal costs for running the model on your own infrastructure, & may require investment in technical expertise for customization and maintenance. | May involve licensing fees, pay-per-use models or require cloud-based access with associated costs. |
| Transparency & Bias | Greater transparency as the training data and methods are open to scrutiny, potentially reducing bias. | Limited transparency makes it harder to identify and address potential biases within the model. |
| IP | Code and potentially training data are publicly accessible, can be used as a foundation for building new models. | Code and training data are considered trade secrets, no external contributions |
| Security | Training data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the community | The codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment |
| Scalability | Users might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resources | Companies often have access to significant resources for training and scaling their models and can be offered as cloud-based services |
| Deployment & Integration Complexity | Offers greater flexibility for customization and integration into specific workflows but often requires more technical knowledge | Typically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor. |
At Fluid AI, we stand at the forefront of this AI revolution, helping organizations kickstart their AI journey in enhanced Customer Support. If you’re seeking a solution for your organization, look no further. We’re committed to making your organization future-ready, just like we’ve done for many others.
Take the first step towards this exciting journey by booking a free demo call with us today. Let’s explore the possibilities together and unlock the full potential of AI for your organization. Remember, the future belongs to those who prepare for it today.
Fluid AI International Corporation (USA)