It’s no secret that technologies like machine learning and artificial intelligence (AI) are changing the future of work in every industry. From banking and retail to healthcare and insurance, the technology is transforming business functions from HR to marketing, sales operations to manufacturing.
AI, in particular, is a fast-growing technology that will shake up the jobs market and make workers more productive. According to Gartner, Inc., “In 2021, AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity”. As more businesses adopt AI to maximise productivity and improve business results, they’ll also need to hire workers with a new skill-set, or invest in training their existing employees.
As technology fundamentally reshapes business and society, it will have a massive impact on careers. New jobs are being created at record speed, whilst other jobs are being optimised by automating mundane tasks. This evolution raises questions on how colleges and universities should prepare graduates. One such answer may be to incorporate emerging technology into coursework, teaching critical skills such as predictive analytics and machine learning to all business school students.
For the students currently in school, this also presents a huge opportunity. So, how should business-focused colleges and universities approach teaching AI-related skills?
I’d like to make three recommendations:
1. Start with the basics
In the past, machine learning and data science required deep technical knowledge that few possessed. With the influx of new technologies like automated machine learning, students can now leverage machine learning without the need to learn complex coding techniques that can take years to master.
That said, machine learning does require some upfront education, including learning about setting up models, avoiding common modeling mistakes, and working with others in the organisation. Colleges and universities should build machine learning 101 theory into courses so that students understand the basics of what machine learning can accomplish, as well as – critically – how it’s being used by organisations today.
“I am a firm believer that every business school graduate should have the opportunity to learn both the theory and practice of skills that will drive real value for businesses.”
2. Look beyond the theory
Colleges and universities are well-known for addressing topics theoretically, but not always in practice. To set graduates up for success, higher education institutions should bring real-world tools and applications directly into the classrooms for students to learn and practice on. They should also assign course projects that force students to use the tools in ways that require creative thinking and evaluate students by deploying detailed feedback. This real-life practice will come in handy, whether students are going to be using tools like machine learning to drive decision-making every day or just augmenting it in small parts of their jobs.
3. Get students involved outside the classroom
Beyond classroom work, higher education institutions should support outside activities – such as internships, office visits, or in-class guests – for fostering further hands-on experience of machine learning and AI technology in action. This will not only help prepare students for a career in a changing world, but also help them identify the areas they’re most passionate about, generating better adjusted and more successful graduates.
I am a firm believer that every business school graduate should have the opportunity to learn both the theory and practice of skills that will drive real value for businesses. To support this, DataRobot now provides more than 30 colleges and universities with access to our product to use in their curriculum.
The future is AI; we should embrace it now.