Generative Artificial Intelligence (AI) autonomously creates original content such as text, images or sound using, for example, Large Language Models (LLMs) that can be refined using specific data. It offers significant potential for customer services by generating accurate responses thanks to a better understanding of context, thereby improving the quality of interactions and user satisfaction.
Generative AI applied to customer service
Customer service is the perfect sector to implement generative AI. It can be used to improve and automate various simple tasks, such as sorting requests or making appointments. But that's not all: the use of generative AI can also take on more complex tasks, such as analyzing customer feedback or offering services like real-time assistance.
What are the challenges?
When a company decides to experiment with Generative AI, whether it involves integrating it into a product or using it in its processes, it faces many challenges that must be properly addressed.
The challenges for start-ups of integration into their products
When a start-up decides to use generative AI, it faces several challenges such as:
- Reduction of costs: Reducing the costs of using models (through optimization and IT architecture) is a major issue in ensuring the viability of business models.
- Building a barrier to entry or exit: As the generative AI market becomes increasingly competitive, it is crucial for a start-up to create a barrier to entry or exit to guarantee its longevity. It's all about finding your Unique Selling Proposition to differentiate yourself from your potential competitors.
- Positioning: The challenge of positioning is essential. Several models are available to these start-ups, ranging from specific development to software packages and tool-based consultancy. On the other hand, they are not financed in the same way and do not require the same resources... the key is to position yourself in a market that is not yet mature.
- Breaking out of dependance on proprietary LLMs: Freeing themselves from dependence on certain LLMs is essential for start-ups. Ultimately, the important thing is not the model but the ability to change it as the market evolves, to avoid any limitations imposed by third-party players.
The challenges for large groups of integration in their processes
Corporate businesses face different but equally relevant challenges when implementing generative AI in their processes:
- Localization, confidentiality and data accessibility: Data management is crucial to protect sensitive information and comply with regulations.
- Integration of the company’s internal data: Integrate internal company data and knowledge bases is necessary to get the most out of generative AI.
- A choice between internal and external developments: Companies must decide whether it is more advantageous to develop solutions in-house or to use external suppliers.
The expert’s point of view
SprintProject - specialist in innovation and start-ups - has identified more than 30 start-ups specializing in generative AI on a global scale. Our experts have measured and positioned these innovative solutions to assign each of them a level of technological and market maturity.
Would you like to find out more about the global distribution of these start-ups and discover how start-ups specializing in generative AI are financed? Contact us!
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