2. Demand education and AI training.
As organizations invest more in their AI and data capabilities, employees understand the growing influence of these technologies on their companies and careers. But despite their best efforts, many of these employees will not have the right training and qualifications to work effectively with AI.
It’s important for organizations to establish education and training requirements for their AI practitioners. Data scientists have varying qualifications, and not all have sufficient training in mathematics or computer science for AI projects. Even an employee with a Ph.D. might have studied a narrow field that isn’t relevant to a particular company’s needs. On the other hand, companies that can scale AI successfully make sure they have people with the right mix of skills and qualifications. Of Strategic Scalers, for example, 70% say their employees have formal training around data and AI5.
To establish an effective professionalized workforce, it’s up to companies to assess which skills they need, their workforce skills gaps and the qualifications of their talent and match them to the appropriate roles. While one way to address the skills gaps may be to acquire or work with a professionalized firm, another approach is to establish an academic partnership. Companies can partner with research and academic institutions to reskill employees or strengthen their future talent pipelines.
For example, Accenture is building a strategic relationship with UC Berkeley’s Institute for Data Science (BIDS) to advance the field of data science. The program is designed to create opportunities for researchers, students and Accenture’s Applied Intelligence practice to work together to explore complex problems facing society and to learn from each other while doing so6.
When recruiting new talent, companies often use technical screening to assess whether an applicant has the required level of knowledge to fulfill a role. To create greater accountability and confidence among AI practitioners, organizations should implement regular assessment points throughout an employee’s career to test their knowledge and to continue their technical education. Like construction workers or medical professionals who must renew their certifications as techniques change and theories evolve, companies should test and re-test the professional competency of AI practitioners to make sure they are upholding rigorous standards and trust, as well as providing them with the training they need to evolve those standards.
To enable a consistent approach to training, companies should create clear career paths for their AI practitioners. Each career level should have established pre-requistes such as coursework and training to help build necessary skills and proficiencies. These pre-requisites should be shaped by a combination of leaders across technology, data and human resources and even outside counsel from leading academic partners. This transparency and consistency will provide clear educational expectations for anyone working on AI projects – from data architects, to test developers to machine learning engineers. The added benefits of establishing career paths are better talent retention, employee development and a market-leading, professionalized practice.