Exploring the three levels of GenAI utilisation in companies

Unlocking the competitive advantage of generative AI

Generative artificial intelligence (GenAI) is transforming industries by enhancing efficiencies, driving innovation and creating new competitive advantages. However, not all GenAI applications are created equally. Businesses must understand the different levels of AI integration to fully leverage its potential. In this article, we will explore the three levels of AI use cases within companies.

Level 1 - Leveraging publicly available AI Tools

The first level of AI integration involves using publicly available GenAI tools to increase efficiency in various business operations. This is currently the most common use of AI across industries. Tools such as ChatGPT and Dall-E can be used in customer service, supply chain management, marketing... In fact, today AI tools are available for almost every job or task and there are several websites to help you to find such tools.

Why level 1 matters

Implementing Level-1 AI tools offers immediate benefits, such as improved operational efficiency, cost savings and enhanced decision-making. All companies should consider integrating these tools to stay competitive. In the short term, businesses that adopt these technologies quickly and effectively can gain a temporary advantage over competitors.

Limitations

However, relying solely on publicly available AI models poses limitations. Since these tools are accessible to everyone, the competitive advantage they provide may be short-lived. To realise long-term growth and differentiation, businesses need to move beyond basic AI implementations.

Level 2 - Customising an existing AI tool for your data

The second level of AI use involves creating customised generative AI tools that leverage the unique data and expertise a company has accumulated. This could include product-specific AI applications such as conversational interfaces (chatbots) or tailored recommendation systems.

Advantages of custom AI solutions

By developing proprietary AI solutions, businesses can address specific challenges and opportunities unique to their operations. Customised AI tools can enhance customer experience, streamline internal processes and unlock new revenue streams. Moreover, because unique data is used, they offer a more sustainable competitive advantage by differentiating the company from those relying only on publicly available AI tools.

Getting started

At its simplest form, a level-2 implementation combines a generative AI tool with internal data sources. For example, several companies invest today in building a GenAI solution that brings a set of documents (user manuals, strategy documents, meeting reports, ...) “alive” by constructing a conversational chatbot around it. 

Level 3 - Creating continuous data feedback loops

The third is a more advanced level of AI integration. It involves establishing automatic and continuous data feedback loops. In this scenario, generative AI tools continuously learn and improve from real-time data generated by customers using the product or service. Systems like that show self-learning capabilities that can lead to compounding competitive advantages.

The power of feedback loops

A well-designed feedback loop enhances the capabilities of AI with minimal human intervention. As more customers use the AI-enhanced offerings, they generate valuable feedback, which in turn improves the AI tool (the so called “network effect”). This cycle leads to better user experiences, increased customer satisfaction and, ultimately, more users.

Challenges and considerations

Not every GenAI system is self-learning or must be self-learning. But if desired, achieving level-3 integration requires meeting certain conditions that are related to data quality and the cost of reliable data acquisition. Businesses must ensure that the data collected is unique, high-quality, and relevant to their specific context. This level of integration is complex and may require significant investment in data infrastructure and AI development.

This publication was written with the support of the Brussels Capital Region and Innoviris.