Generative Artificial Intelligence (AI) has made fantastic progress in recent years and is increasingly influencing various aspects of our lives. But how to use it in the business world?

Even before ChatGPT, there were impressive generative AI models. For example, AI entered our everyday lives in 2011 with Apple’s “Siri” launch. Also, in 2011, the computer program “Watson” beat human players in an American quiz show. Watson proved that it understands natural language and can answer complex questions quickly.

However, generative AI applications such as ChatGPT have influenced people’s imaginations of what AI can do in a way other systems have not. That is probably due to its broad and straightforward applicability – almost anyone can use it to communicate and be creative.

The latest applications of generative AI can perform various routine tasks, such as reorganizing and classifying data. But the ability to write text, compose music, and create digital art has made headlines – and inspired many people to experiment.

As a result, many B2B companies are now faced with the challenge of grappling with the impact of generative AI on their business, employees, internal processes, and the economy, with little context to understand it.

This article explores the opportunities that generative AI presents for B2B companies and the challenges that this technology brings.

Generative AI vs. Other Types of AI:

Generative artificial intelligence (AI) is a subfield of machine learning that focuses on analyzing data and generating new content.

What distinguishes generative AI from other types of AI is its ability to generate content that does not simply classify or categorize existing data. Generative models use various techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), or autoregressive models to generate data similar to the training data.

In contrast, other types of AI, such as classical classification or regression algorithms, focus on recognizing patterns in the data and making predictions. These types of AI models (or machine learning models) are designed to solve specific tasks or classify data into predefined categories without the ability to generate new data. That is the case, for example, with predictive (sales) analytics applications.

Generative AI enables the creation of new images, text, audio, or video content based on patterns in existing data. This capability has numerous applications in art, medicine, manufacturing, marketing, etc. Generative AI can produce innovative solutions and support human creativity by generating new data and content.

However, it is essential to note that generative AI also poses some challenges, particularly regarding the quality of the generated content, handling ethical issues, and dependence on extensive training data. Nevertheless, generative AI remains a fascinating and promising technology with many potential applications that continue to be intensively researched and developed.

Opportunities for Generative AI in B2B

The applications of generative AI for B2B companies are many and varied, offering tremendous potential for process optimization and increased efficiency:

– Process Automation and Efficiency: Generative AI can help automate recurring and time-consuming processes in the B2B environment. By generating reports, analysing large amounts of data, and automating administrative tasks, companies can increase efficiency and free up resources for strategic tasks.

– Personalize services and offerings: Using generative AI, B2B companies can personalize their services and offerings to meet their customers’ specific needs better. That can range from creating customized marketing content to tailoring products and services to individual needs.

– Innovative Solutions and Product Development: Generative AI enables B2B companies to develop new and innovative solutions, such as in the design process or prototyping. Companies can strengthen their competitiveness and differentiate themselves in the marketplace by generating ideas and designs.

– Communicate more effectively with customers: The advanced conversational and content creation capabilities of generative AI models can be leveraged in B2B organizations to improve customer communications. Chatbots and personalized customer interactions can be more effective, resulting in a better customer experience.

In this way, generative AI offers B2B companies the opportunity to optimize their business processes, develop innovative solutions and differentiate themselves in the market.

Using generative AI in B2B companies: What to look out for.

However, using this technology also requires a strategy to maximize its potential while addressing ethical and privacy concerns.

1. For example, if you use ChatGPT, you should be aware that you are making all the data you enter into ChatGPT available to OpenAI. You should also look at other models to implement a chatbot for customer communication. Here are two examples of open source models: The large language model “Mixtral” from a French company and “Galactica” from Meta. The advantage of Galactica is that 86% of its staff are trained in scientific papers. That means the output is more scientifically sound than models trained mainly with “web data” – such as ChatGPT.

2. Train your employees to use generative AI. It is not enough to provide access to ChatGPT+. Correctly entering prompts (input commands) prevents inaccurate and frustrating results.

Prompts can be questions and commands, influencing how generative AI systems respond to your request.

You should consider the following points:

– Open-ended questions lend themselves to creative approaches. They lead to vague and broad answers.
– If you clearly know what you want, be as specific as possible. That will also make the answers more precise.
– Determine the desired format (list, table, written text).
– Specify the tone: formal, informal, for a particular occasion, a particular style, etc.
– Specify the answer length: short and sweet, detailed, or a certain number of words.

But be careful! Even well-trained generative AI models are still AI models. That means they will never be 100% correct, even with the best prompt.

3. This brings us to the third point. Quality control is always necessary due to the “nature of AI”. Even if you have generated 50 perfect texts, an error will creep in at some point. It is up to us humans to find it.

4. In particular, consider the following threats and challenges to your business

– Ethics and accountability: Generative AI raises ethical issues, particularly around using generated content and potential misuse.

– Changes in the workplace: Automation through generative AI could make some jobs obsolete, requiring retraining and workplace adjustments.

– Data dependency: Generative AI models require large amounts of data, raising privacy and security concerns.


The Potential of Generative AI for B2B Companies – Conclusion

Generative AI offers tremendous potential for augmenting human capabilities and innovating in various fields.

However, this technology’s potential dangers and ethical challenges must be carefully considered and addressed. Responsibility lies not only in the development and use of this technology but also in the creation of appropriate policies and oversight mechanisms to ensure that the benefits of this technology are maximized.

Before you use AI, you should have a case. Whether it is “normal” or generative AI. What problem in your business should an AI system solve? Look for suitable systems and vendors.

For example, if you work in B2B wholesale or manufacturing, you have a lot of sales data that you should be using! AI-based predictive sales software is the best solution.


Further Read:

McKinsey (2023): The economic potential of generative AI: The next productivity frontier

Bosch: Die Geschichte der Künstlichen Intelligenz

Elektronik Praxis: „KI ist nicht KI – warum eine Unterscheidung sinnvoll ist“