For many organizations, the workforce needs to depend on where they are in adopting, deploying, and maintaining AI. Organizations just starting out on their AI journey may have a large set of data that they have been collecting over the years, and they are now trying to identify what predictions they can make from it.

Leaders need to move away from checklist-driven hiring practices and focus more on skills that showcase a candidate’s ability to work on a team, feel comfortable with ambiguity, and move forward in a rapidly changing environment. 

Too often when organizations seek to hire talent in the AI space, they assume they should focus on a couple of schools. In our experience, robust, secure, scalable, and human-centred AI systems are ones that incorporate varied perspectives and data. AI systems learn from examples, so it helps to have a diverse team that can bring different lenses to a problem and identify appropriate datasets to train the AI system on. 

It is therefore critical to consider who can help translate across these different roles or how teams invest time in developing shared language over time.

AI technologies are evolving so quickly that any specific requirements might soon be overcome by advancement. For that reason, organizations looking to adopt AI need to grow a culture of learning. On the hiring side, that also means looking for people with a sense of curiosity. 

In the early days of adopting any new technology, and AI, in particular, it is often more helpful to have individuals with the curiosity and willingness to try things that are outside their traditional bounds to figure out solutions to problems. 

Organizations also need to focus on helping existing employees learn how to learn. The expectation cannot be that everyone will be able to easily add learning on top of days filled with back-to-back meetings and never-ending lists for deliverables. Organization leaders have to think about ways they can create the structure to enable learning behaviours for individuals and teams.

To help individuals learn how to learn, managers can ask themselves the following questions:

• What is our shared vision for leveraging AI? What outcomes are we hoping to achieve?
• How am I creating opportunities for people to learn and grow? How am I establishing psychological safety to encourage risk-taking?
• Am I there and present when my team members have questions about where to go next? Who else can provide guidance?
• How do I help my team see things they haven’t previously seen, ask new questions, or curate a set of resources?

Organizations today are working to assemble teams that can make bespoke pieces of AI, leverage them towards specific outcomes, and endlessly tune system components to arrive at assured AI systems, able to be deployed in a variety of different environments. 

To develop such systems, organizations and leaders will have to take action to develop a workforce that has the necessary skillsets, mindsets, and an array of experiences. 

Unfortunately, there is no perfected recipe and we at the SEI are trying to navigate the growth of our workforce to support our AI engineering portfolio.