The AI Education Gap and How to Close It
AI education is broken, how do we solve it? Individuals end up learning a specific tool or tactic in a vacuum. They are missing the real-world applicability and collaboration that is critical to building impactful AI solutions in line with the organization’s strategy.
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It’s an exciting time to be involved with analytics and AI. Much like the software boom of the early 90s, many organizations are approaching a new generation of technical capabilities, empowering teams and driving digital transformation in ways only previously imagined.
While there’s a handful of mature AI-centric companies in the market, most organizations seem to be lagging behind. The good news is that with this gap comes an influx of online educational opportunities for learning the necessary technical skills to build AI solutions. Options range from free educational content on video platforms like YouTube to massive open online courses (MOOCs) like Udemy or Coursera, which require a monthly fee. There are even long-term online bootcamps designed to help individuals change jobs and advance their data & analytics careers.
Although these options may refine the technical skills of individuals in data-centric roles, most miss the mark in teaching the skills necessary to successfully enable and scale AI across a broader organization.
The Case Against Today’s AI Education and MOOCs
AI education is broken. You may be thinking this claim is pretty bold, especially since there are hundreds of free options available for data, analytics and AI education online. But when you take a closer look, it becomes obvious that the training being offered has a few concerning limitations.
- Many AI educational tools struggle with learner engagement. Studies show the average completion rate of online analytics and AI education from MOOCs was a mere 3 percent. What makes these courses so dry? They have a one-dimensional style. One person, taking one course, to achieve one certification. Some individuals learn fine by staring at a screen all day and going through sections, but others can find this tedious or difficult to apply.
On top of that, without a motivating factor to complete a course and make a change in a certain timeframe, the priority to sit down and learn is just not there. When leaders expect their students to find the time outside of work, training eventually gets deprioritized and teams don’t end up seeing an impact from their investment in training.
- AI education is too narrowly focused on tactical skills. Most training is offered by a tool or language vendor, so it centers around specific software tools or technical methods. Technical skills, like writing lines of code or setting up automations, should not be overlooked. But they’re a single piece of the puzzle when it comes to successful AI deployment.
- Most AI education is completely off the shelf. In many cases, thousands of students enroll in a single course with a single instructor. Sometimes the instructor is only a facilitator, rather than the course creator, and other times they are absent altogether. While great for scaling, this teacher-learner relationship gap puts the onus on the student to tie the skills they learn back to their job. Feedback from students highlights this pain. They report that applying learned concepts on the job is one of the most difficult aspects when trying to develop a new skill on their own.
How do these limitations impact a team looking to implement and scale AI? Individuals end up learning a specific tool or tactic in a vacuum. They are missing the real-world applicability and collaboration that is critical to building impactful AI solutions in line with the organization’s strategy.
- Find training inclusive of both technical and business teams. Analytics and AI are inherently cross-functional, so business and tech individuals being in lockstep is critical for consistency and scale in any organization.
- Understand that students have demanding workloads and complex responsibilities. Find courses that are built as a series of short modules for quick accessibility throughout the workday. Focus on the right amount of information to help learners achieve a specific, actionable outcome in their work.
- Leverage a blend of on-demand and live experiential learning exercises. One size doesn’t fit all when it comes to learning styles. Pre-recorded videos are a common approach today, but it doesn’t work for everybody. Employee students should learn a concept and why it’s important, apply it practically in a safe environment with team members, then reflect on what works for their specific team and environment. This variety in learning modes helps to keep the content fresh, engaging, collaborative, and most importantly, applicable.
With all the options available for analytics and AI training today, don’t forget the necessary skills outside of specific tools and lines of code. Technical skills are important, yes, but so much more is needed to successfully enable and scale AI across your organization.
Rehgan Avon is the co-founder & CEO of AlignAI, an AI adoption platform that helps organizations mature their AI capabilities faster through process-oriented education. With a background in integrated systems engineering and a strong focus on building technology to support analytics and ML, Rehgan has worked on architecting solutions and products around operationalizing machine learning models at scale within the large enterprise.