Digital technology, data, and machine learning have already changed the world. Whereas employers previously employed people in positions with regular hours, pensions, benefits, and specialized skills, more people are performing work with greater flexibility, without paid sick time or insurance, and with less predictability. Intelligent algorithms can perform work, constantly and consistently, with greater efficiency and control.

But the rise of machines is not the end of employees. It requires the ideal employee to have adaptable skills. The demand for workers is becoming less about the ability to perform repetitive tasks or applying specialized knowledge. It requires workers to navigate the complexities of supporting or being supported by machines, managing interactions with cyber physical systems, and critically examining the network of information and structures that impact decisions.

How will employment and labour law keep up? What will your advice be if, for example, an employee enables a predictive algorithm to learn from discriminatory data and the employer relies on the algorithm for hiring? Does it matter if neither the employee nor the employer is aware of the bias in the algorithm? What if one is aware, but not the other, and how much – or little – does impact and intentionality matter in a relationship that has been changed by machine learning?

To get ahead and leverage the changes in the practice of employment and labour law, consider three areas that will have increasing importance:

1. Learning the law, then expanding it. Familiar concepts like working time, constructive dismissal, and training will look different as technology fundamentally changes the workplace and management of human capital. How can legal protections be interwoven into the development of workplace changes rather than be imposed as outdated requirements? For example, traditional union memberships will change but new member groups will emerge. How can established rules be applied to unfamiliar situations, in a way that is effective and relevant?
2. Learning the changes, then preparing for innovation. As trial lawyers know, you do not need to be an expert to cross-examine an expert. Similarly, you do not need to be a data scientist to know that the links between probabilities will result in business changes. Basic knowledge of data analytics, business intelligence, economics, marketing, and programming languages will amplify your advice. Understanding technological nuances means you will be more effective at delivering services tailored to your client’s needs.
3. Learning the business, then the challenges, risk profile, and strategic goals. When your client asks a question, your first question should be, “Why do you ask? What is your goal?” You can potentially find a more compliant, better aligned solution for their needs. How you deliver guidance can be just as important as providing the correct legal answer. It will become increasingly important to know when to provide the perfect-top-shelf-law-firm advice and when to triage. Your primary value might not be resolving the thorny issue, but distilling the learnings to further a strategy.

Fostering confidence in how the law will be applied is integral to gaining trust. Even where the law is clear, it will likely be considered differently as the landscape of work continues to shift. The best lawyers proactively offer options, flexibility, and added value in the delivery of legal services. It is a small leap for lawyers, as experts in managing issues, risk, and policies, to become partners in the leadership of people and technology.