What is the difference between traditional and generative AI for businesses?

Traditional AI:

For businesses with strong technical expertise or the right support, traditional AI is about using your existing digital data to create models that automate your processes, optimize your operations, and improve your decision-making. It includes tools like predictive analytics, customer segmentation, and recommendation systems, which require sophisticated engineering and other AI-related capabilities.

Generative AI:

With generative AI, businesses can go beyond traditional data analytics to create new content, ideas, and information based on “patterns” learned from different types of data. She can learn from human language (text, voice), computer code, images, and even music. It is therefore able to reproduce these types of data quickly or to create new ones.

Furthermore, while most traditional AIs need to start from scratch and require business data to learn and become effective, many generative AI models have already learned patterns from vast amounts of data and are ready to be used as soon as they are adopted.

These models produce results quickly when prompted by a few words or phrases, a process called “prompt engineering.” Thus, generative AI can be used effectively by technical and non-technical professionals on certain platforms that allow this type of interaction.

The value of generative AI for businesses

Generative AI and traditional AI differ greatly in how they provide revenue opportunities and reduce costs for businesses. While both maximize revenue and reduce costs, the ability of generative AI to create and innovate offers new opportunities that traditional AI typically doesn't.

1 - Income opportunities

Product innovation:

Because generative AI can produce writing, art, music, images, or other materials, it can create entirely new products, features, or services for businesses. This opens up new revenue streams by offering unique products/materials that are impossible to achieve with traditional AI.

Automated creativity:

Businesses can use generative AI to automate creative tasks like marketing, design, and advertising, reducing the time needed to create new ideas and marketing content.

2 - Cost savings

Content and process automation:

Through this same creativity, while generative AI can create independently, it can also automate numerous productions, whether for marketing, sales, or internal documents such as reports or meeting summaries. It is also well known for automating processes that previously required human intervention, from email writing to customer support and customer experience chatbots.

R&D efficiency:

By generating new ideas, designs, or prototypes simply from written orders or other inputs, generative AI reduces the time and resources needed to bring new products to market or improve existing ones.

How to use generative AI: implementation

Compared to traditional AI, implementing generative AI in your business generally follows one of three approaches: buy a pre-packaged solution, use/modify an existing model, or build from scratch. Each path has its own benefits and challenges, and understanding these options can help you decide what type of generative AI would best fit your organization's needs.

1 - Buy a pre-packaged solution

As mentioned earlier, these are ready-to-use generative AI models that are well known to the public, such as ChatGPT, Claude, and Gemini. Buying these pre-packaged models is an attractive option for businesses looking for a fast and cost-effective AI solution. They offer less customization, but can be easily used by non-technical individuals thanks to prompt engineering.

These models are powerful, but your competitors can access them just as easily, leveling the playing field. To gain a competitive advantage, the following two types of implementations offer interesting solutions.

2 - Use and modify an existing template

Major providers of ready-to-use models, like OpenAI's ChatGPT, offer templates that can be integrated into your systems and software. Your business can use the template as is or modify it with your data and intellectual property. Both options allow businesses to adapt generative AI to your specific needs, to exploit your competitive advantage (data and intellectual property), and to better control the creations of the model.

3 - Create from scratch

Creating generative AI from scratch offers the highest level of personalization, but it's a complex and expensive process. This path is generally reserved for large organizations with significant resources to develop and maintain these systems. While the control and customization are incomparable, the investment in time, expertise, and financial resources makes this option impractical for most businesses.

Risks and challenges of generative AI

Confidentiality/security risks

Traditional AI and generative AI share similar privacy and security challenges, especially when it comes to managing personal or proprietary information. From collecting data to deploying the model, organizations need to ensure that robust privacy protections are in place. The key difference with generative AI is that prepackaged solutions, like ChatGPT, can store and use the data you enter. This can potentially compromise data security as this information could be absorbed by ChatGPT and accessible to others.

Major risk: hallucinations

Generative AI introduces a new risk: hallucinations. Unlike traditional AI, generative AI can produce content that seems accurate but is actually incorrect or logically wrong. Since she doesn't reason like a human, she can generate falsely convincing information. This poses significant risks for businesses that rely on AI for decision-making, content creation, or customer interactions. Ensuring the accuracy and reliability of results is crucial to avoid misinformation and its consequences.

Conclusion

Generative AI is a powerful evolution of traditional AI, allowing businesses to create content, innovate products, and automate creative tasks. This can lead to significant revenue growth and cost savings. However, it's critical to understand the best implementation strategy, whether that's buying a pre-packaged solution, modifying an existing model, or building from scratch.

Each approach has unique benefits and risks, some of which have not been covered in this article. To explore how generative AI can transform your business and navigate the complexities of its implementation, contact PlanAxion today. Our experts can guide you through the process, ensuring that your generative AI strategy is aligned with your goals.