B2B Companies Transforming Into 'Generative AI-Centered'

B2B companies seek important strategies to maintain competitiveness amid the trends of digitalization and advancement. Generative AI, triggered by ChatGPT, which has shaken the world for almost a year now, is emerging as one of the key elements shaping the future of B2B companies. Generative AI refers to technology that creates new content, such as text, images, audio, and video, through machine learning. Generative AI is no longer treated as a new tool but as a universal tool for enhancing corporate productivity and work efficiency. In various industrial settings, it is not difficult to find small examples demonstrating that generative AI can be applied to any task. In fact, creating content or generating images using generative AI is a task that many people have experienced at least once. However, it seems difficult to dismiss these tasks as merely the domain of other companies or different job roles as these tasks become more realistic. In some companies, employees are encouraged to integrate generative AI into their tasks.

Many companies are currently adopting or have already integrated generative AI at the corporate level, yet it is often observed that they are dependent on unclear possibilities without defined goals. This is similar to parents buying a toy for their child and just observing them play without offering any direction. Sometimes, we may find ourselves in a situation where we aren’t entirely clear on what and how to effectively use generative AI. Unlike IT systems designed for specific purposes, such as email or messenger, working with generative AI can be a challenging task, like painting one’s knowledge on a blank canvas.

The fundamental challenge faced in using generative AI for personal work is not simply using it but the pressure to apply AI to more meaningful and creative tasks. In fact, our perspective hasn’t diverged much from the few examples that OpenAI provided when ChatGPT was first introduced. In other words, many people might only use ChatGPT that OpenAI first showcased in a real work environment. It is because they still don’t have a clear sense of how to fully leverage generative AI.

This also tends to increase the pressure on employees. However, I believe this phenomenon is a natural process in the early stages of adopting AI. Just as most employees experience volatility, uncertainty, complexity, and ambiguity (VUCA) pressures when new digital technologies or tools are first introduced, it is inevitable that they will face anxiety due to AI. This confusion is likely to occur when a strategic direction for generative AI has not been established, when it has been defined but not effectively trained, or when users have not been exposed to various use cases or examples. In this article, we will briefly examine the necessary considerations for implementing generative AI and some B2B use cases.

Considerations for the Successful Implementation of Generative AI

To effectively use generative AI in B2B companies, the following five key principles should be taken into account:

① AI Leadership

Leadership plays a pivotal role in establishing and executing generative AI strategies. Thus, leadership must have a clear grasp of how to generate business value with generative AI and communicate that method effectively. Furthermore, leadership helps ease employees’ concerns regarding generative AI and encourages them to actively adopt new technologies.

② AI Strategy

To effectively use generative AI, it is essential to establish a clear vision and objectives. Instead of a vague introduction to generative AI, we should move towards addressing real business problems or innovating the existing workflow. This strategy clearly outlines the purpose of using generative AI and specifies the data and technologies required to achieve that purpose.

③ AI Organizational Culture

Organizational culture plays a vital role in the successful adoption and utilization of generative AI. An open and experimental culture gives employees the flexibility and agility needed to effectively leverage generative AI. The adoption of generative AI should be done gradually. A significant shift to introduce generative AI across all tasks may lead to resistance from employees. It is recommended to begin with small projects or particular departments, gradually building successful case studies. At the same time, it is advisable to encourage failures in generative AI projects and accumulate knowledge from failures by fostering a culture of learning and improvement through mistakes.

④ AI Training

Employee training and development are very important. To maximize the performance of generative AI, it is essential to properly understand generative AI and educate people on fundamental knowledge, usage methods, and strategies for applying it in their work. This requires continuous training programs and workshops, and it is effective to learn through practical application cases in real work.

⑤ AI Ethics

Finally, it is necessary to consider ethical and legal aspects when adopting and using generative AI. Standards should be established for the appropriate use and management of data tailored to specific companies, as well as for transparency in generative AI.

Case Studies of Generative AI Implementations in B2B Companies

Siemens: Automated Product Design

Siemens, as a leader in industrial automation and digitalization, continuously fosters innovation through generative design AI. At its core, AI swiftly suggests a range of design options based on the input of customer requests, such as product characteristics, performance, and cost. This has been significantly helpful in deriving creative designs and more efficient product structures that would be hard to conceive using traditional methods.

Generative Design Screen in Siemens NX (Source: Siemens)

IBM: Supply Chain Optimization

IBM Watson Supply Chain Insights provides services that go beyond standard supply chain management, focusing on risk management and seizing opportunities. Through AI-based solutions, it is possible to analyze data across the entire supply chain, identify potential supply chain risks in advance, and develop appropriate response strategies. This helps prevent large-scale production disruptions and significantly reduces inventory costs, providing tangible benefits.

Dashboard Screen in IBM Watson Supply Chain Insights (Source: IBM)

GE: Predictive Maintenance

GE Predix offers services that predict equipment performance degradation or long-term problems based on sensor data from industrial equipment in large industrial facilities, such as those in the oil, gas, power generation, and aviation sectors. Unlike conventional periodic inspection methods, AI continuously monitors equipment conditions in real time and conducts maintenance only when necessary, leading to significant cost savings and extending the life of the equipment.

Data Sensing Screen in GE Predix (Source: GE)

Salesforce: CRM Optimization

Salesforce Einstein is an intelligent CRM platform that automates data entry and predictive analytics using AI. It analyzes customer data to provide various insights, such as estimated sales, the potential churn of customers, and the performance forecasts for next quarter. This enables the analysis of customer relationships and supports the development of personalized services and marketing strategies.

Analysis Screen in Salesforce Einstein (Source: Salesforce)

Conclusion

One of the advantages of using generative AI in B2B companies is its capacity to deliver customized solutions. This enables the development of specialized products or services by analyzing customer needs and market trends rather than offering existing standardized products or services. This allows for real-time responses based on the vast amounts of data collected by AI in a short time. Furthermore, a growing number of companies are leveraging AI at the corporate level for supply chain optimization, demand forecasting, and product planning. There are increasing cases where generative AI is used to predict efficient product shapes or components during the product design process, leading to the initiation of new product development based on these predictions.

Generative AI should be used not merely as a technology tool but as a strategic partner for companies. It is essential to change the company's leadership, strategy, and the mindset of its employees, which requires continuous education and communication, as well as employee participation and cooperation. For companies to thrive in the age of generative AI, it’s crucial to not only embrace technology but also foster an organizational culture where both people and technology grow together.

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