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Training Departments Using Generative AI


There is no doubting the enormous creative potential of generative AI, regardless of whether you think it is overhyped, a game changer, or somewhere in between. There are more and more opportunities to use this technology in the workplace as computer innovations continue to push the limits of what machines are capable of.

They have a lot of creative freedom when it comes to training needs.

Writing pertinent content and improving current content seem to have clear advantages. Multimodal generative AI, like GPT-4, may create contextually appropriate programs that are suited to the needs of each individual based on the employee and workplace data it has been trained on. By utilising this strategy, training departments may produce various dynamic materials more quickly, improving the quality of the workforce’s learning opportunities.

Things become much more intriguing as you delve deeper because there are a variety of use cases where generative AI may significantly improve things.

Consider data analysis as an illustration. The use of generative AI can help discover skill gaps and places where employees require training by analysing employee data such as job descriptions, skills, and performance evaluations.

From there, it can create training programs that assist employees in filling in the skills gaps and acquiring the abilities necessary for success (not to mention that it can complete all of this significantly more quickly than any HR specialist). This approach provides a simpler way to monitor staff development and identify areas where training needs to be enhanced.

Offering courses that staff members could be interested in or that would help them grow might be a crucial component of the procedure. The same is true for finding new courses that can be useful for the company’s operations. For instance, generative AI might be used to find courses that would assist staff in teaching the relevant nuances of a new market if a company was entering one.

Generative AI is beautiful because it can be as granular as necessary. Let’s imagine that a business wants to enhance its diversity policies. Generative AI can review hiring and promotion procedures throughout the entire organisation and identify patterns that may indicate bias. The findings can be utilised to provide instruction specifically aimed at these behaviours.

Additionally, it can evaluate a worker’s communication style and suggest particular training to enhance their interactions with peers from various backgrounds. There is an ability to create unique scenarios that replicate real-life circumstances in the workplace, providing a secure and monitored setting for practising replies.

Similar to how adaptable learning paths can be developed using generative AI based on individual progress and training requirements. These paths can adjust in real time, offering certain resources based on the employee’s strengths, limitations, and learning style, ultimately assisting organisations in maximising the results of training. They do this by using the performance data as a basis.

Further, generative AI through applications like Auto-GPT can serve as a virtual mentor, offering individualised advice and support to staff members by responding to inquiries, providing comments, and making suggestions. The benefit of this virtual support is that it is always accessible, ensuring that staff members have access to guidance whenever they need it. Additionally, it can modify its instructional approach in accordance with student learning preferences, further customising and improving the training process.

Given that there is already proof of a considerable increase in productivity, this use case looks especially advantageous. According to a recent MIT and Stanford University study, people who use AI assistants are 13.8% more productive than people who don’t. Less experienced workers were more severely impacted, with a 35% efficiency drop.

Then there is image generation, one of the most well-known uses of generative AI. Realistic people, landscapes, and other invented, unusual visuals may now be produced because to advances in image synthesis. A text-to-image model may create graphics that not only resemble the training samples, but it can also create specialised images for specialised situations that aren’t always online. These can more precisely convey ideas or circumstances, particularly those that are challenging to put into words.

Naturally, since there is no lack of instruments, the same can be said for virtual reality and video creation. Nvidia’s GET3D is one such example; it is a generative AI model that creates 3D shapes from data such as 2D images, text, and numbers. Almost everything can be represented by these shapes, including people, animals, furniture, vehicles, buildings, and more.

Corporate trainers in many industries may swiftly grow the quantity and diversity of information using machine learning algorithms that convert 2D inputs into 3D models. They have more freedom to create the conditions and design scenarios that are specific to particular use cases.

To replicate realistic training circumstances, for instance, generative AI can be used. Training departments can develop simulated environments where staff members can practise and hone their abilities by basing training models on real-world data like as customer encounters, sales cases, or crisis management situations. These simulations also feature an interactive and adaptive component that gives users real-time feedback and direction.

As a result, employees receive relevant, hands-on training without investing in costly equipment or running the risk of negative outcomes.

Overall, it should be obvious by this point that AI will play a significant role in shaping workplace training in the future.

These application cases—some of which have already been implemented—demonstrate how generative AI may improve training departments by generating customised content. Employees can experiment with various situations in a controlled setting and remember more information compared to standard approaches like presentations and testing.

Although there are certain ethical questions surrounding its use, the promise of generative AI is too great to ignore, especially in a changing workplace where the necessary skills are desperately needed. It’s safe to say that as technology advances, there will be more creative and efficient ways to support the workforce.

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