MIT SMR Connections
MIT SMR Connections is the custom content creation unit within MIT Sloan Management Review.
Organizations in the vanguard are using machine learning systems to generate real-time insights for better decision-making, and to automate rote processes so employees can spend more time creating value for customers. But leaders seeking to implement machine learning in the enterprise, and glean real business value from this area of artificial intelligence, face a barrier: a shortage of qualified talent.
Many businesses bridge this skills gap through new hires, partnerships, or acquisitions. But they overlook a more obvious solution: upskilling their current workforce.
This guide provides a framework for action on closing the machine learning skills gap by developing the existing capabilities and aptitudes within your organization. Here’s where to begin.
1. Assess current workforce skills and identify gaps. Performance reviews and employee screening are a good place to start. But many businesses are deploying innovative tools, such as self-assessment apps, to gain clarity around capability gaps and determine where training may be needed to provide current workers with new machine learning skills.
2. Identify individuals with promise for data science roles. Businesses that only consider hard skills like statistics, math, and coding when seeking candidates risk overlooking some important attributes that may recommend an employee for additional training and development.
3. Build data literacy across the organization. Systematically boosting data literacy across an entire workforce helps those with functional expertise and institutional knowledge to better collaborate with machine learning experts — a critical success factor for moving beyond pilots and experiments to successful enterprise implementation.
4. Explore diverse options for training in machine learning skills. A robust training and development plan for upskilling an enterprise workforce will need to address the wide range of learning modalities and skill levels, with in-depth technical training for those being groomed for machine learning development roles, but more general instruction in data literacy for others.
Download the guide, along with a machine learning upskilling checklist, for more details on how to prepare your workforce for the growth opportunities machine learning can offer your employees and your business.