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.&nbsp; 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.

Focus on hiring and supporting diverse talent.

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.&nbsp;

Match your workforce needs to your development needs

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.&nbsp;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.&nbsp;

Help your talent learn how to learn

Organizations today are often faced with a challenge: How do we move forward even if we don’t have all of the answers yet? A data engineer can have an impact across the application, from application performance to the semantics and meaning of the data flowing across the system.
AI team members must be curious and humble enough to acknowledge that they don’t have all the answers and identify who can reach across different boundaries within a system to track down an answer.

Accept that no one has all the answers

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.

New Ways To Think

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

Enabling Learning

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.&nbsp;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.&nbsp;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.&nbsp;&nbsp;

The Starting Point for an AI-Ready Workforce

A common refrain for many organizations, and government organizations, in particular, is that building an AI-ready workforce is particularly challenging because it is impossible to match <a href="https://www.nytimes.com/2018/04/19/technology/artificial-intelligence-salaries-openai.html" target="_self">the salaries offered by large, private-sector</a> companies such as Amazon, Google, and Microsoft. Salary discrepancies between industry and government are unlikely to change any time soon, however.&nbsp; Many individuals <a href="https://hbr.org/2018/11/9-out-of-10-people-are-willing-to-earn-less-money-to-do-more-meaningful-work" target="_self">choose meaningful work over a larger salary</a>.