Artificial Intelligence in Workforce Scheduling

The Biggest Challenge Is Not the Technology

The introduction of AI into shift planning is far more than just a software project. It is a cultural shift. A study by the University of St Gallen and PwC shows that organisational acceptance, transparent communication and change management are often more crucial to the success of AI projects than the technical capabilities of the systems used.

Staff want to understand why they have been assigned to specific shifts and the rules governing the planning process. If the impression arises of an opaque «black box», acceptance declines – even if the planning is objectively correct. 

Transparency therefore becomes a crucial factor for success. Employees must be able to understand which criteria are taken into account and how their preferences are incorporated into the planning process. AI can prepare decisions, but communicating them remains a human task. 

Why Fairness Is More Important Than Efficiency

Companies often invest in AI to plan more efficiently. However, staff rarely judge duty rosters on the basis of their efficiency. They judge them on the basis of fairness. Who takes on the most weekend shifts? Who regularly works on public holidays? Who gets their preferred days off? 

This is precisely where AI presents a major opportunity. While people make subjective decisions, whether consciously or unconsciously, an intelligent planning system can allocate shifts according to clearly defined and transparent rules. If fairness is specifically defined as a target metric, this strengthens trust in the planning process and reduces conflicts within the team. 

In modern workforce management systems, fairness can be modelled using clearly defined rules. These include, for example, distributing weekend shifts, public holiday shifts or last-minute assignments as evenly as possible. This makes planning decisions more transparent and verifiable on the basis of objective criteria. 

A practical example is a fairness system. If staff members take on particularly demanding assignments – such as working on Christmas Day or a standby shift taken at short notice – these are recorded in the system. When planning future rotas, the system can give priority to staff who have taken on such assignments more frequently in the past. This makes it clear why individual staff members are treated differently when it comes to allocating preferred days off or public holiday shifts. 

Planning rules can also be enforced. For example, the system takes into account the applicable rest periods between two shifts or limits the number of consecutive late or night shifts. At the same time, the allocation of such shifts is balanced out over longer periods. This prevents individual staff members from having to take on unpopular off-peak shifts disproportionately often on a permanent basis. 

What We Can Learn from SBB

SBB shows that this is more than just a theoretical concept. Hardly any other operations in Switzerland have more complex requirements. Thousands of trains must run on time, yet at the same time, train drivers and on-board staff rightly demand predictable time off. With modern collective labour agreement models and digital planning tools, the railway company demonstrates how to strike this balance. 

The key concept is «flexible working hours». Under defined models, staff can choose whether to receive overtime or premium pay in cash or to convert it into additional holiday days. New deployment concepts also aim to minimise downtime through smarter coordination of journeys. The key takeaway for you is this: even in a system where time is of the essence («The train won’t wait»), flexibility can be created through intelligent planning. When staff recognise that their preferences are being taken into account in a transparent manner, this can increase acceptance of the duty roster and have a positive impact on satisfaction and employer attractiveness.

The Biggest Risk: Poor Data

No matter how powerful AI is, it is only as good as the data it is trained and configured on.

If qualifications are not kept up to date, or if existing imbalances have never been corrected in the past, AI will not solve these problems but may well exacerbate them. That is why successful AI implementation does not start with the software, but with clean data, clear rules and defined fairness criteria. 

The Scheduler Remains Indispensable

AI is changing the role of the scheduler, but it is not replacing them. Instead of manually allocating shifts, the scheduler will in future define the rules, evaluate the results and communicate these to staff. 

Or to put it another way: AI generates the proposal; humans take responsibility. 

Workforce Scheduling as a Competitive Advantage

In the age of  Staff shortages , the quality of shift planning is increasingly becoming a strategic success factor. Many employees leave their employer not because of pay, but because of a lack of predictability, unfair shift allocation or an inability to balance work and private life. 

Modern and transparent shift planning can therefore achieve far more than just greater efficiency. It improves staff satisfaction, strengthens commitment to the company and enhances its appeal as an employer. 

Conclusion: The Future Is Hybrid

The question is no longer whether artificial intelligence will be incorporated into shift planning. The question is how it will be used. As a control mechanism, it will meet with resistance. As a tool for greater fairness, transparency and predictability, however, it can become a genuine competitive advantage. The most successful companies will not be those with the most powerful technology. Success will go to those who manage to combine technology and people in a meaningful way. 

The future of workforce scheduling is hybrid: AI provides recommendations, while people make the final decisions.

Sources

• University of St. Gallen (Institute for Leadership and Human Resource Management) & PwC: Human Resource Management between AI and Cultural Transformation. Study on the cultural prerequisites for the successful deployment of AI (2025). 
• SBB & social partners: Collective Labour Agreements & Deployment Concepts. Information on flexible working time models and new route planning to improve efficiency (2024/2025). 
• Bitkom e.V.: Artificial Intelligence in Human Resources. Guide to the legal framework (AI Act) and potential applications (updated version 2025). 
• Aspect / Infoniqa: AI in workforce planning. Expert articles on compliance automation and skill-based scheduling (2025).