By Gary Allemann
ARTIFICIAL Intelligence (AI) and robotics are steadily making their way into every industry. From retail companies to healthcare practices that use AI-driven robots for medical processes, the applications are proving to be endless. This is why 85% of companies plan to incorporate AI and machine learning into their business by 2026, and commercial robots are experiencing record adoption rates.
While AI and robotics concepts were once isolated into Science, Technology, Engineering and Math (STEM) courses at schools and universities, modern educational institutions are increasingly incorporating them into business courses too. Educators must prepare business graduates for a future career that’s impacted by automation each day.
As business education evolves, companies will have rising access to professionals who can make better decisions — from investments to sales strategies — for the modern business landscape. Learning AI and robotics can also help students improve their data analyses before they actually enter the workforce.
As much as 50% of jobs may be automated by 2030. While this largely includes manufacturing careers and menial work, automation is increasingly affecting careers that once required a tertiary education too.
Rather than simply teaching students how to manually predict supply and demand, for instance, students may learn how to use AI to predict supply and demand trends in their industries. They can then use this AI-supplied information to improve pricing strategies. Business students therefore become irreplaceable, knowledgeable workers when they expand upon the capabilities of AI and robotics.
Technology leaders can support the rise in interdisciplinary education by offering hands-on internships to business students, rather than limiting opportunities to STEM students.
Teaching AI and robotics isn’t just an opportunity to secure business students’ futures, it can also help them improve their decision-making processes so they’re fully prepared to put their skills into practice once they’re in the field. Visual teaching strategies improve learning by up to 400%, in part by making complex subjects more concrete and easy to remember — and few subjects are more visual and hands-on than robotics and AI.
Even using AI software in the classroom can be more immersive than purely learning business theories with an internship. For instance, professors can teach resource management by having students work as a team to choose whether to complete certain assignments or tasks themselves or “invest” in automation software to do the work for them, given a set budget and costs. Students may need to consider both HR and automation ethics by ensuring each member of their team always has a task (and therefore remains “employed”).
Interdisciplinary education doesn’t have to be complex. Business students don’t necessarily need to learn how to code or build machines, but they can benefit from learning how AI and robotics work and how they can incorporate these new technologies into their careers.
STEM professionals and data analysts can contribute to this extension of traditional business education by volunteering as guest speakers in classrooms. By providing insights about AI, robotics, and automation from the perspective of someone who’s actively watching businesses transform, you can make business courses more realistic and actionable for students.
The hard part
Collecting data is often the easy part of business analyses. Getting insights from all those numbers is the difficult part — and the most important. With actionable insights, you can predict sales volumes and even turn your company’s data into a monetisable asset. In the business field, experts expect the usage of AI and machine learning for cash flow forecasting to increase by 450% in just two years.
When students can understand basic AI and robotics concepts, they can understand how to decipher data and use it to their advantage. One business school in India teaches students how to build IoT devices and store data, which they can use to analyse and monitor the devices via the cloud.
Data mining for analysis is key to business solutions, and this can be done by AI-driven models. Students can then create their own data visualisations and use AI-driven software to predict potential business outcomes. Business students can practice using transactional data collected by AI software to identify customers’ interests, then practice their selling skills by using those interests within their pitches.
Modern business courses can no longer neglect the rise of AI and robotics across industries. No matter what company business students work with (or start) in the future, they must understand these new technologies to make efficient, ethical, and smarter business decisions in the future. Supporting these programs — for example, by volunteering as a guest speaker, offering internships, or even donating to universities — can support the growth of a more proficient workforce and more capable graduates.
Gary Allemann is MD at Master Data Management