Integrating generative artificial intelligence in companies is one of our most critical technological challenges. Generative AI tools such as ChatGPT or Microsoft Co-Pilot are here to stay. Many companies use or plan to use AI tools and technologies in their business or work processes. In this article, I describe tips and steps for the sustainable implementation of generative AI in companies.

Generative AI tools such as ChatGPT or Microsoft Co-Pilot are here to stay. Many companies use or plan to use AI tools and technologies in their business or work processes. The current Deloitte study ‘State of GenAI in the Enterprise’ (Q1/2024) states that 31% of the managers surveyed expect a fundamental change in less than a year through the use of generative AI, with around 48% expecting this in one to three years.

Identifying suitable use cases is the be-all and end-all for the successful use of generative AI in companies. A basic understanding of what AI models can do and how they can be integrated into existing systems is crucial. Companies need to analyse their specific needs and challenges to identify areas where AI can add value. This requires not only technical expertise but also a deep understanding of the business processes and the industry.

The process of identifying suitable use cases includes assessing data availability and data security, clarifying the problems to be solved, and understanding the potential impact of using such tools.

Many companies are currently in the ‘proof of concept’ phase. This phase is about testing the feasibility and value of AI projects in a controlled environment. This phase often involves smaller, lower-risk projects to demonstrate the potential of the technology and develop an understanding of the resources and skills required.

Companies should appoint AI officers within the organisation to provide a central point of contact for all AI-related activities and questions.

Companies expect the integration of AI to increase productivity. Generative AI tools such as digital assistants enable the automation of routine and time-consuming tasks, allowing employees to concentrate on more complex and value-adding activities.

AI tools also enable the rapid analysis and interpretation of large volumes of data. Generative AI can recognise patterns and trends that would be difficult for human analysts to identify. This allows organisations to gain valuable insights that lead to improved strategic planning, risk assessment, and, ultimately, more informed decision-making.

However, it is also essential to consider governance, regulatory compliance and ethical considerations when implementing generative AI. While these tools can potentially increase efficiency and productivity, organisations need to ensure that AI complies with applicable data protection regulations and that the results are trustworthy and free from bias.

Clear guidelines and best practices should be established for handling data in compliance with the GDPR when using generative AI tools:

• How is personal and company data protected?
• How is personal data protected?
• What happens to the data entered in the AI tool?
• Who can access it? Where is the server located?

Companies are responsible for stimulating an ethical debate about the impact AI can have on employees and society. AI tools should improve working conditions and not lead to unjustified surveillance or the replacement of workers without adequate retraining or further training opportunities. AI-supported decision-making processes in human resources, such as hiring or promotion, should be free of bias and promote equal opportunities.

The use of AI should be sustainable and offer long-term benefits for the organisation, its employees, and society as a whole.

In general:
Generative AI tools should be viewed and evaluated as digital assistants. They promote creativity and problem-solving and increase productivity by automating tasks and thus saving time. AI tools should complement human decisions but not replace them. Humans have the final decision-making power, control, and responsibility.

A major obstacle to using AI in companies is the lack of expertise. To truly innovate and remain competitive, organisations need more than just AI technology. They also need to focus on developing digital skills and competencies. To do this, companies first need insight into their teams’ AI capabilities.

Once they know their strengths and weaknesses, they can develop a training programme that closes the gaps and gives employees the skills to use AI tools effectively. Finding AI experts on the market is a gamble. Organisations that develop AI talent from their existing workforce are building the exact AI skills they need.

Employees should be empowered to handle AI tools and become proficient. They should be aware of the challenges involved, such as AI hallucination, possible bias effects, data protection, data security and copyright, and know how to deal with these challenges.

A skillset for GenAI readiness refers to the skills and knowledge that individuals or organisations need to effectively use generative AI technologies, fully exploit the potential of generative AI and remain competitive. This includes both technical and non-technical skills:

Learning how to create high-quality input (prompts) in order to communicate with AI in a targeted manner is central. Invest in learning prompting techniques and frameworks. This is often underestimated, and you get results with AI tools that can only be used to a limited extent. With specific prompt methods, the potential of AI can be fully utilised. Prompt engineering – i.e. the ability to design effective prompts – is a specialised technical skill that is highly important for interacting with AI systems.

In addition, a certain level of data expertise is essential. This includes handling data, including analysing, cleansing, and processing it, as well as understanding data structures and management.

It also includes problem-solving skills, i.e., the ability to identify problems that can be solved with generative AI and develop creative solutions. Critical thinking and analytical skills are essential to assessing AI results and ensuring their quality and reliability. Creativity and innovation skills open up the possibility of using AI tools for new, innovative applications.

Change management skills are also part of the skill set required to manage organisational changes brought about by AI and to lead teams through transformation processes. Personal resilience plays a vital role in dealing with the dynamic changes in the digital world of work in order to prevent stress and burnout.

Sustainable integration of generative artificial intelligence (AI) in companies requires a well-thought-out strategy that incorporates technology, people, and processes. Here is a practical step-by-step guide:

Step 1: Assess the current situation
Analyse current workflows to identify areas that could benefit from AI. Carry out a skills analysis of your employees to determine their existing knowledge of AI.

Step 2: Strategic planning
Define clear goals for integrating AI into your organisation. Develop an AI strategy that aligns with your business goals and values. Create a realistic timeline for implementation.

Step 3: Select the tools
Select suitable AI tools and platforms that integrate with your existing systems or create your tools using existing AI models, such as a dedicated enterprise GPT.

Step 4: Risk assessment and compliance
Assess the risks associated with data protection, security and ethical issues. Ensure that the use of AI tools complies with applicable data protection laws.

Step 5: Pilot project
Start with a pilot project on a manageable scale to gain initial experience. Then, measure AI’s performance and impact using predefined KPIs.

Step 6: Employee development
Train your employees in the use of the new AI tools. Promote an understanding of AI’s potential and limitations. Support the development of AI skills through targeted further training measures.

Step 7: Change management
Communicate the changes and the expected benefits openly and transparently. Involve employees in the process at an early stage and collect feedback regularly.

Step 8: Scaling
After a successful pilot phase, scale the use of AI to other business areas. Adapt processes and integrate AI more deeply into workflows. Implement continuous monitoring. Invest in the continuous improvement of AI tools and capabilities.

Investing in AI technologies and integrating AI tools into business and work processes is essential for companies that want to remain competitive in an increasingly digitalised market. Even more important, however, is investing in the digital skills and understanding of their employees regarding these innovative tools.

The key to the successful integration of AI lies not only in the technology itself but also in the ability of organisations to effectively adapt these technologies to their specific needs and processes. It is of great importance to identify suitable use cases, carefully manage change processes and consider ethical aspects when dealing w