Attitude and AI knowledge will be the deciding factors when applying for a job

The professor outlined who in the organisation needs to be aware of what factors and what their responsibilities will be in relation to AI.Senior managers need to understand AI, its strategic implications, how it will affect the business model, what new competitors it may bring and what impact it will have on efficiency. It will be important for the management to create a secure IT environment for the use of AI (have a company privacy policy on AI) and an internal AI policy in collaboration with HR. And in the EU, the AI regulation will come into force by 2026, with which requirements will also have to be met.

Team leadersshould be careful not to allow the "wild west" use of AI to take root in the company. Policies on this should be adhered to. At the same time, employees should have the opportunity to voice their concerns, try out tools and learn from each other. There should also be an opportunity to discuss motivations and ethical problems.

HR has given up on the process of listening to attitudes when recruiting, providing training, working to create AI policy, ensuring compliance. It will also be important to work with IT to monitor and optimise the use of AI tools in corporate and HR processes, and prepare the organisation to optimise business processes as well.

Process and change management

Martin Giesswein outlined how stakeholders can successfully support each other in this process: making decisions and carrying out the necessary change management. For AI to operate successfully - which will now primarily mean not replacing but collaborating with AI, which companies expect to primarily increase efficiency - it is essential that the initiative starts from the top, with the business leader. It doesn't hurt to appoint an area responsible for implementation - currently the IT or business analytics department in many companies - although HR could be an equally good candidate, especially if it is also responsible for organisational development and talent management. However, there are many things that everyone needs to work together on.

HR has a crucial role to play in preparing the organisation for these changes. Trainingis one of the primary areas for this, as it can help to reduce employees' fears and thus increase acceptance of the new tools, the University of Vienna professor stressed. For example, the experience of how much easier it is to use AI to put together a presentation, which a lot of people don't like to do, can be a big help and even save time.

Employees can even be involved in the development of AI policies: which tools can be used, how to protect personal data, who to contact if a problem arises. It's worth taking time for knowledge sharing, when colleagues can share their experiences and learning. Also buddies can be identified among those who have a knack for using these tools to help others.

A manifesto, which some companies have already done, to say that they want to use AI to increase productivity, for example, to be better, but they don't want to replace employees with it, could play a role in adoption. The Austrian Press Agency, for example, has done this, involving everyone in the development of the guidelines, which is good because if everyone takes ownership of the decision, it's easier to communicate it externally. Transparency in this area is very important, and small steps in everyday application and adaptation.

It's also worth measuring usage, Martin Giesswein also cited a practical company example where they measured that they saved an average of 53 minutes a week using AI, which is roughly 4 hours a month. One could conclude from this, for example, that it is worth taking up an AI subscription because the monthly fee is cheaper than the cost of labour for four hours.

Required leadership skills and competences

1. Credibility

The leader must also attend training and use AI tools, not just expect his/her team to do so.

2. Empathy

Don't make colleagues feel that the tool is being forced on them, but that they can accept it and benefit from it.

3. Business acumen

Don't just implement for the "hype", but to get a sense of the business potential the manager sees in it. AI is currently in its "infancy", it is not perfect, but it is worth identifying some tasks and processes for each business area where it can help. It is not worth overstating expectations of results, there will be continuous improvement, but for now it is worth focusing on one or two best practices for use.

4. Ongoing communication

More is better than less, the professor stressed. It is important for colleagues to see how processes and even individual tasks change as a result of use.

5.Further training and not retraining

Recruitment processes will be affected differently in the short and longer term. For the time being, companies will not be looking specifically for AI competencies in every field - business analytics may be an exception - but it is worth mentioning in job advertisements or building into the recruitment process that a basic understanding of AI and experience in using its tools can be an advantage.

As for colleagues, it's too early to talk about retraining - we're not at that stage yet - we need development and training instead. If, for example, an accountant is involved in a project where an AI tool can help the company account for travel expenses (simply by taking photos of the invoices, the AI will calculate the travel time, maybe even give tips on how to save on expenses next time), it will gain experience and help with implementation in another area. You don't immediately become an expert in AI-enabled business forecasting, but you can develop controllers and salespeople in this direction.

There is no need to change the whole job, AI will take over some tasks and workflows from a job, not the whole job, so there is no need for a complete retraining, but development is needed more than anything else, stressed the lecturer at the Executive Academy of the Vienna University of Economics and Business (WU).