How to use the Potential of Generative AI in B2B Business

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, and video based on patterns in existing data. This capability has numerous applications, for example in media design, manufacturing, and marketing, to name a few. It can produce innovative solutions and support human creativity by generating new content and insights.

Generative AI brings not only progress but also concrete challenges. The quality of generated content is not always reliable, ethical questions are often unresolved, and the dependence on large training datasets remains a critical issue.

This is especially evident in the creative field. The use of existing works to train models raises legitimate questions about the protection of artists. These questions have so far been insufficiently addressed in practice.

At the same time, it is clear that generative AI has already reached widespread adoption and is here to stay. The key question will not be whether we use it, but how responsibly we do so.

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 that previously reached their limits quickly due to rule-based responses can now do much more. As a result, more customer inquiries can be resolved without long waiting times on customer service hotlines.

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. If you use tools like ChatGPT, you should be aware that any data you input may be processed by OpenAI depending on the usage context. Therefore, it can be worthwhile to consider alternative models.

In addition to commercial solutions, there are now powerful open-source models such as Mixtral or DBRX that can be flexibly integrated into existing systems and give companies greater control over their data and infrastructure. At the same time, models like Claude are gaining importance due to their strong focus on safety, alignment, and responsible AI usage.

An important factor when selecting suitable AI models is their origin and regulatory environment. Models like Mixtral are developed by European providers and therefore operate fully within the context of GDPR and EU regulations, which is particularly advantageous for companies with high compliance requirements. In contrast, solutions such as DBRX or Claude are developed by US-based companies but must also comply with the requirements of the EU AI Act when used within the European Union. Ultimately, the key question is not which model is “best,” but which one best fits the specific use case, data protection requirements, and ethical considerations.

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.

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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.

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

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