The impact of Generative AI on your work: Find out what new skills you need

As discussed in the previous newsletter (Part 1), the role of the CIO has changed significantly in recent years. This trend will only continue in the coming years. With the rise of generative AI and other advanced technologies such as machine learning and robotic process automation (RPA), the role of the Chief Information Officer (CIO) is shifting from IT management to strategic use of technology to achieve business objectives. The CIO of the future will need to pay more attention to optimal processes and the role and functioning of digital employees, making the role more like that of a Chief People & Operations Digital People. This applies not only to the CIO, but also to team and department managers. Even the work of the average employee, which we call the physical employee here, will change. So, is your job changing too? The short answer is yes.

Examples of AI in action

We already see these changes taking place in various industries. In banking, employees are training AI models for fraud detection instead of searching for patterns themselves. In healthcare, nurses and doctors are using AI to make cancer diagnoses and analyze patient records. In the retail sector, employees are improving inventory management and customer service by analyzing patterns in customer behavior and predicting future trends. Below we discuss further expected changes.

Manager digital colleague

The new role of the physical employee in managing AI systems requires a combination of technical skills and strategic insight. Physical employees will increasingly act as managers of their digital colleagues. They will be responsible for defining tasks and ensuring the quality of execution by their digital colleagues. This means that physical employees must be adept at prompt engineering, and have a good understanding of business processes and data management (quality, structure, security). They must be able to creatively formulate clear commands that enable AI systems to operate accurately and effectively within prescribed parameters.

What skills will you need soon?
Skills for the future

Digital skills, creativity and communication
Employees must become more digitally proficient and be able to creatively develop prompts that steer digital employees toward desired outcomes. This requires both linguistic understanding and a deep understanding of business processes, data nuances and their interpretation by AI systems. It starts with “technical” knowledge of the mechanisms of (Gen)AI and how they are trained with data. This is necessary to ensure that the digital employee delivers reliable, usable and quality results. Physical workers must also be able to make adjustments (or have adjustments made) where necessary, for example by adjusting data sets, working on data quality or changing model parameters. This can also be done by taking a critical look at the own (root) prompts given to the digital colleague.

Collaboration
To harness the full potential of AI, interdisciplinary collaboration is also relevant. Employees cannot be the expert in everything, so they must not only develop their technical and creative skills, but also be able to collaborate effectively with colleagues from different disciplines. Multidisciplinary teams, including IT specialists, data analysts, legal experts and ethicists, are therefore important for successfully implementing AI solutions.

Ethics
The deployment of AI raises important ethical and responsibility issues. Therefore, it is also relevant to involve ethicists in interdisciplinary teams. In addition, employees working with these systems must also be aware of the ethical implications of their work with AI systems. They need to understand how to manage and protect data to comply with privacy regulations such as the General Data Protection Act (GDPR). This includes ensuring data privacy, for example, by anonymizing personally identifiable information (PII) while training AI models.

Bias and transparency
Employees should be aware of their own bias and the risks of bias and discrimination in AI systems. AI systems can introduce unintended bias, which can lead to discriminatory outcomes. Physical staff should be able to monitor AI systems for bias and ensure that training data is diverse and representative. This helps prevent AI systems from disadvantaging certain groups and ensures fair and equitable outcomes. The human touch always remains relevant in this.

Ethical conduct also includes accountability and transparency. Physical workers must be able to explain and be accountable for AI decisions. This can be supported by implementing explainable AI (XAI) methods that provide insight into how decisions are made. This allows both colleagues and stakeholders to understand how outcomes were arrived at.

Continuous learning
The rapid development of AI technologies requires physical employees to continuously learn and update their skills. Regular training and workshops help employees stay up-to-date with the latest AI tools and techniques.

We also recommend continuing education and retraining, which does not require everyone to be fully retrained immediately. This can start with basic training and training that gradually grows with the pilots and implementations in the organization. Note that training (Generative) AI is something very different from the ChatGPT training you see a lot. For high unemployment, provided we start training on time, we do not have to fear we think. AI replaces some tasks, but also creates many new tasks and roles that require new skills, such as data analysis and machine learning.

Learning from mistakes is an essential part of continued learning. Pilots and implementations are therefore crucial to create these learning moments. Realizing and using AI can also lead to, among other things, mistakes that provide valuable learning moments. It is also important to learn to assess AI outcomes by actually working with AI systems.

Critical assessment skills and feedback loops
Another important skill for physical workers is the ability to critically assess AI outcomes. They must understand how outcomes are created, be able to quickly intervene and make adjustments when the digital colleague deviates from expected standards or when unforeseen problems arise.

A digital colleague may suddenly deliver different outcomes than before after an update to the tool or model. This requires an ongoing dialogue with the digital counterpart, with the physical employee monitoring performance, implementing feedback loops and making continuous improvements in interactions with the digital colleague.

Conclusion
The new role of the physical worker requires a combination of technical expertise and soft skills, such as adaptability, critical reflection, problem-solving ability and interpersonal communication. In addition, knowledge of required data, data quality, business processes, AI operation and control is essential.

Physical employees must be able to communicate effectively with both digital colleagues and less digital physical colleagues and stakeholders in the process. This ensures that the deployment of AI is aligned with the organization’s strategic goals. The physical employee is the hub that integrates, manages, coaches and trains AI, ensuring harmonious, quality and productive collaboration.

In the next newsletter, we will continue with Part 3, The Requirements for AI as a Digital Employee. We discuss how generative AI and other forms of artificial intelligence are evolving into full-fledged colleagues, and what impact this has on day-to-day operations and strategic decision-making. the collaboration between the physical and digital colleague and the new role for managers.