Founder and CEO of Workmetrics, a leader in workforce software. Doctor of Information Technology specializing in data integration and AI.
Whether you believe that generative AI is a game changer, overhyped or somewhere in between, there’s no denying its immense creative potential. As the advances in computing continue to push the boundaries of what machines can create, new opportunities emerge to leverage this technology in the workplace.
When it comes to training purposes, they can create a lot.
On the surface, there’s the obvious benefit of writing relevant material and enhancing existing content. Based on employee and workplace data it has been trained on, multimodal generative AI like GPT-4 can produce contextually relevant programs tailored to the needs of each individual. Such an approach can help training departments create different dynamic materials more efficiently, thus raising the level of learning experiences for the workforce along with information retention.
When you dig a little deeper, things get far more interesting, as there are all sorts of case uses where generative AI can provide a significant boost.
Take analyzing data as an example. Generative AI can dissect employee data such as company positions, skills and performance reviews to identify skills gaps and areas where employees need training.
From there on, it can design training programs that help workers close the skills gaps and develop the aptitudes they need to be successful (not to mention it can do all of it far quicker than any HR professional). This method offers an easier way to track staff progress and pinpoint areas where training needs to be improved.
An important part of the process can be suggesting courses employees may be interested in or would help them improve. The same goes for the discovery of emerging courses that may be relevant to the company’s business. For example, if a company is expanding into a new market, generative AI could be used to identify courses that would help personnel learn the necessary intricacies of the new market.
The beauty of generative AI is in its ability to be as granular as needed. Let’s say a company wants to improve its diversity practices. On a company-wide level, generative AI can go over hiring and promotion practices and single out patterns that potentially point to bias. The results can be used to construct training specifically targeting these actions.
It can also analyze how a worker communicates and recommend specific training to improve their interactions with coworkers from diverse backgrounds. There’s an option to come up with custom scenarios that simulate real-life situations in the office, offering a safe and controlled environment to rehearse responses.
In the same fashion, generative AI can be utilized to create adaptive learning pathways based on individual progress and training needs. Using the performance data as a foundation, these pathways can adapt in real time, recommending specific resources based on the employee’s strengths, weaknesses and learning style, ultimately helping organizations optimize training outcomes.
Going a step further, generative AI via tools like Auto-GPT can act as a virtual mentor, providing personalized guidance and support to employees by answering questions, giving feedback and offering recommendations. The good thing about this virtual assistance is that it can be available 24/7, making sure staff have access to counsel whenever they need it. It can also adapt its teaching style to individual learning preferences, making the training experience even more tailored and effective.
This seems like a particularly beneficial use case as there’s already evidence of significant productivity growth. A recent study by MIT and Stanford University showed that those using AI assistants became 13.8% more productive than those without. This more greatly affected less experienced workers, who saw a 35% efficiency increase.
Then, there’s one of the most well-known applications of generative AI: image generation. Breakthroughs in image synthesis have led to the creation of realistic faces, landscapes and all kinds of invented, unconventional images. Not only can a text-to-image model produce images that resemble the training examples, but it can also generate very niche photos for equally niche circumstances—photos that aren’t necessarily available online. These can describe a concept or a situation more accurately, especially those difficult to explain in words.
Of course, the same can be applied for video generation and virtual reality as there is no shortage of tools. One such is Nvidia’s GET3D, a generative AI model that makes 3D shapes from data like 2D images, text and numbers. These shapes can take the form of almost anything: animals, humans, furniture, cars, buildings and so on.
With machine learning algorithms that turn 2D inputs into 3D models, corporate trainers in various industries can quickly scale the quantity and diversity of content. They gain more flexibility when setting up the parameters and creating scenarios tailored to distinct use cases.
For instance, generative AI can be employed to simulate realistic training scenarios. By training models on real-world data such as customer interactions, sales cases or crisis management scenarios, training departments can create mock environments where employees can practice and refine their skills. Furthermore, these simulations have an interactive and adaptive element to them, offering real-time feedback and guidance to employees.
Thus, workers get practical, hands-on training without the need for physical resources or risking actual consequences.
All in all, it should be clear by now that the future of workplace training is going to be heavily driven by AI.
These use cases (some of which are already applied) illustrate how generative AI can enhance training departments through content generation and personalized learning. Compared to conventional methods like presentations and tests, employees can experience various situations in a controlled environment and retain more information.
While there are certain ethical concerns attached to its use, the promise of generative AI is too good to pass up, even more so in a changing work environment where the skills required to succeed are in dire need. As technology continues an upward development trajectory, it’s safe to say we’ll see more innovative and effective ways to support the workforce.
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