In addition to hiring new AI specialists, existing employee skillsets should be upgraded. An organization cannot assume its employees are keeping up to date on AI by themselves. Instead, formal training needs to happen for all workers who have interaction with AI-related processes. It is essential to assess employee training needs, deliver the required training, and then provide post-training support. 

A large corporation may have hidden AI talent among its various non-technical employees. One AI leader suggested conducting a survey asking employees about their specific AI skills to access this talent. 

Existing technical employees (including IT department personnel) may not be proficient in the latest AI technologies and may need retraining. This retraining needs to be personalized based on the specific jobs of employees and their learning styles. 

Organizations should democratize data science and AI, which will enable non-technical employees to get involved in AI projects. “Democratization of data science and AI development [is] the notion that anyone, with little to no expertise, can do data science if provided ample data and user-friendly analytics tools” (Benbya et al., 2020, p.11). The democratization efforts require AI and data science courses to be available to non-technical employees, along with providing access to data and AI technologies such as AutoML that are easier to use. Also, as more employees within an organization understand the benefits and risks that AI products or services bring, these employees can better assist management in enhancing the benefits of AI and mitigating related risks. 

As AI technologies are continually changing, training needs to be continuous. Training can be provided in-house, or financial support can be given to allow employees to obtain external certifications on their own. Big companies are investing heavily to strengthen their AI talent base. 

Employees can also learn from working on AI projects in multi-disciplinary teams. The inclusion of domain experts on such teams helps to enhance team members’ AI-related skills. Domain experts can assist in identifying lesser-known data sources, selecting features for AI models, and gaining user acceptance of new AI models. Working within multi-disciplinary teams allows team members who are technically oriented to become more knowledgeable about business domains and, in the future, become more independently capable. Cross-functional teams are also more innovative and better able to perceive potential risks from AI model deployment.

Further, it is crucial to not only look at the skillset of AI team members but also to consider their compatibility with each other. Compatible team members are much more productive. Also, it is important to be on the lookout for new technologies to increase the innovation speed of AI specialists and other key AI team members.

Although high reliance on external consultants is generally not a good practice, there seems to be an agreement in the market that hiring consultants/external vendors may help initially. External help may assist a corporation to move forward with AI projects when internal skills are lacking or when it is taking longer for an organization to complete its full-time hiring process. Issues that need to be managed with external vendors include, but are not limited to, privacy, security, and cost. 

Author: Dr. Jodie Lobana

References included:

Benbya, H., Davenport, T. H., & Pachidi, S. (2020). Artificial Intelligence in Organizations: Current State and Future Opportunities. MIS Quarterly Executive, 19(4).