Here and elsewhere, you’ve likely read many articles and studies on the potentially transformative effect of artificial intelligence and machine learning on the workplace. We’ve seen some of that transformation unfold more quickly during the ongoing COVID-19 pandemic, as workplaces across sectors explore automation to ensure essential processes, from security checks to invoice payments, keep happening.
There are concerns that AI and ML will cause significant job losses, and it seems inevitable that they will change or even eliminate some kinds of positions. But to unlock the potential of AI and ML for the enterprise, existing job roles must be filled — and new ones must be created. Below are 10 workplace roles that could emerge as AI/ML continues to advance and organizations continue to integrate the technology into their operations.
1. Knowledge manager: As part of its Project Cortex rollout, Microsoft wants companies to hire knowledge managers. These employees would be responsible for the quality of knowledge shared across an organization and aggregating a companywide taxonomy.
2. AI scientist: Some organizations, of course, have just such a role in place already — but as artificial intelligence becomes increasingly powerful and adopted by more and more organizations, these specialists will become essential to a growing number of companies.
3. AI manager: And of course, if you are adding AI scientists and other AI/ML experts to your team, someone with knowledge and experience in that field needs to manage them and help them work together. Management staffers with specific experience in artificial intelligence and machine learning could become increasingly important in integrating these technologies across an organization.
4. Subject matter expert: Also recommended by Microsoft in relation to Project Cortex, an organization’s subject matter experts would have a deep understanding of how information is organized in the areas under their purview. As Microsoft imagines it, the one in this role would work closely with the knowledge manager.
5. Personality designer: Behind every AI architecture is a personality that someone had to design. Think of Siri, for example. Someone decided what the responses would be like, how the voice would sound, etc. As virtual assistants powered by machine learning become an increasingly common part of our work (and home) lives, the work of these designers — and related workers, like writers and UI/UX professionals — will be in even more demand.
6. AI trainer: The underlying structures of AI and ML products and services must be trained — and trained well — to be effective. That can be done with machines, but it’s likely to be far more effective if a human is selecting the information with an eye to effectiveness and bias.
7. Content services administrator: In some cases, a content services or knowledge administrator would represent an expansion of an existing role, like a SharePoint or Teams administrator. But this IT professional would set up and run knowledge product suites, like Cortex, Microsoft hopes.
8. Intelligence ethicist: The world is increasingly grappling with ethical issues brought forward by AI and ML, from built-in bias to spurious — or even dangerous or illegal — applications of technology. Large firms, in particular, will need intelligence ethicists to guide the decisions made by the products and services they are developing.
9. Data detective: Have you been impressed by the work done by COVID-19 contact tracers or intrigued by the possibilities (and pitfalls) of location tracing apps? Work as a data detective could be in your future. These employees could use data points — for example, the locations someone has visited — to solve problems, create datasets for AI/ML training, and develop new products and services.
10. Data broker: AI- and ML-driven technologies require reams of data to learn from, and that data has to come from somewhere. A data broker would be in charge of accessing, managing and deploying that data for an organization. It’s a role likely to become increasingly complex as more and more jurisdictions add data-centric regulations like the California Consumer Privacy Act.