Part One: the skills AI can’t replace

As Artificial Intelligence (AI), and technology in general, continues to advance at an unprecedented pace, designers are becoming increasingly concerned about their future professional life. The reason for their concern is that AI has the potential to take over many tasks that designers currently perform, threatening their livelihoods.

Why is it scary?

One of the most significant concerns for designers is that AI can automate tasks such as generating layouts, color schemes, and typography, which were previously done by human designers. This automation could lead to a decrease in demand for designers, leaving them with fewer job opportunities.

Another concern is that AI can create design solutions quickly and cheaply, making it harder for designers to compete in the market. Clients may prefer to use AI-generated design solutions that are faster and cheaper, rather than pay for the expertise of human designers.

Furthermore, designers fear that AI could make them obsolete in the long run. As AI continues to evolve, it may be able to perform tasks that are currently seen as exclusively human, such as creating art or composing music. If this were to happen, it would be challenging for designers to compete with machines that can produce similar or even better results.

Today’s skills to bring into tomorrow

Despite these concerns, designers can take comfort in the fact that AI cannot replace the human touch and creativity that they bring to their work. While AI can perform certain tasks, it can’t top the personal perspective and insight that human designers possess. Designers can adapt to the changing landscape by focusing on developing skills that are difficult for AI to replicate.

Creativity

While AI can produce some impressive results, it is still unable to match the creative power of the human mind. AI can replicate existing patterns, but it struggles to create something entirely new. While AI can generate designs that are aesthetically pleasing and technically proficient, it cannot replicate the unique style and personal touch that designers bring to their work. As of today, it can be a valid support in creating robust scenarios and a storytelling skeleton, but remember:

AI is as good as the data it’s trained on. If we’re exploring radical possibilities that haven’t happened yet or happened but there aren’t many examples, AI might not pick it. —

Yaron Cohen

Emotional Maturity

AI cannot empathize with human emotions, nor can it understand the subtleties of human communication. Emotional intelligence is crucial for fields such as stakeholder management, counseling, influencing, coaching and leadership, where personal interaction and connection are essential. AI lacks the ability to feel emotions, which means it cannot recognize or respond to emotions in others.

Emotions are an essential aspect of human communication and interaction, and our ability to understand and respond to them is a crucial part of our emotional intelligence.

Also, AI cannot experience empathy or understand the perspective of others. Empathy is an essential component of emotional intelligence, and it allows us to connect with others, build relationships, and respond appropriately to their emotional needs. Emotions and cultural practices can vary widely across different regions and communities, making it difficult for AI to adapt to different contexts and respond appropriately. And obviously, AI does not have life experience, which is a critical component of emotional maturity. Our emotions and reactions are shaped by our life experiences, and our ability to respond to new situations is often informed by what we have learned from past experiences.

Critical thinking

AI can perform some types of critical thinking tasks, such as analyzing large data sets, identifying patterns and correlations, and making predictions based on statistical models. On the other hand, it struggles to think critically and make judgments based on incomplete or ambiguous information. Human beings are much better at analyzing complex situations and making decisions based on multiple factors, combining quantitative insights with qualitative observations. And sure, we can use AI to improve our design thinking session (thanks to

But what about Ethics and Morals for example? AI can only make decisions based on the data it has been programmed with, and it cannot make ethical or moral judgments based on the broader context of a situation.

Even when it comes to the ability of making key decision, it’s important to remember that CEOs are rarely the ones giving you an answer in a few seconds:

Great leaders don’t have the answers all the time, but rather set the circumstances in the company so that the answers are explored.

Adaptability

AI is designed to perform specific tasks and is limited by the parameters set by its programming. Humans, on the other hand, can quickly adapt to changing circumstances and learn new skills.

• AI models rely on large amounts of data to learn and make predictions. However, this data is often biased and incomplete, which can limit their ability to adapt to new situations or environments.
• AI models are trained on historical data and are therefore limited to making predictions based on what they have seen before. They struggle to adapt to situations that are different from what they have encountered in the past.
• AI models lack the contextual understanding that humans possess. They struggle to understand the nuances of language, culture, and social interactions, which can limit their ability to adapt to complex and changing situations.

Followership

While AI can analyze vast amounts of data and make predictions based on patterns, it cannot connect with people on an emotional level in the same way that humans can. Followership is not just about providing information or making decisions based on data, it’s about building relationships, earning trust, and inspiring people to take action.

Humans can read between the lines, understand nonverbal cues, and empathize with the needs and concerns of others. These are all essential components of effective leadership and followership. Moreover, followership is not only about being able to communicate with people, but also to build a relationship of trust and influence. People tend to follow those whom they respect and trust, and AI lacks the ability to build such relationships with people.

Part Two: new skills to start developing today

As technology continues to advance at a rapid pace, it’s becoming increasingly important for individuals to develop the skills necessary to stay ahead of the curve. Two areas of expertise that are particularly crucial for the future are generative AI prompt design and AR & VR design.

Generative AI prompt design

Generative AI prompt design is a field of study that involves creating prompts or instructions that are used to generate new content (such as text, images, or even music) using artificial intelligence (AI) algorithms. It is a rapidly growing field that has the potential to revolutionize the way we create and consume content, from generating new music, art, and literature to creating personalized marketing content.

To learn generative AI prompt design, also called prompt engineering, one must first understand the basics of machine learning and AI algorithms. Machine learning involves training an algorithm to recognize patterns in data and make predictions based on those patterns. Generative AI algorithms take this a step further, using these patterns to generate new content that resembles the original data.

The role of the prompt is to guide the generative AI algorithm in the direction of the desired outcome. For example, a prompt for a generative music algorithm might specify the genre of music, the length of the piece, and the instruments to be used. A prompt for a generative art algorithm might specify the color palette, the style, and the subject matter.

The design of the prompt is critical to the success of the generative AI algorithm. A well-designed prompt can result in content that is creative, engaging, and meaningful, while a poorly designed prompt can result in content that is irrelevant, unappealing, or even offensive.

To learn generative AI prompt design, one must have a solid understanding of AI and machine learning algorithms, as well as a deep knowledge of the domain in which they wish to create generative content. This may involve studying music theory, art history, or literature, depending on the type of content one wishes to generate.

As the field of AI and machine learning continues to evolve, the potential applications of generative AI prompt design are likely to expand, creating new opportunities for creative expression and innovation. By learning generative AI prompt design, individuals can become pioneers in this exciting and rapidly growing field, pushing the boundaries of what is possible in the realm of generative content creation.

Here are some of the most popular and widely used generative AI tools to explore and get started:

1. GPT-3: Developed by OpenAI, GPT-3 is one of the most powerful and versatile generative AI tools available. It can generate human-like text, answer questions, and even write code.
2. Uberduck: This is a generative AI tool specifically designed for creating unique and interesting sounds. It uses machine learning algorithms to analyze existing sounds and create new sounds that are similar in style or mood.
3. Midjourney: This is a generative AI tool that creates unique, abstract images by using a combination of deep learning and neural networks. Users can input their own images, and Midjourney will use them as a starting point to generate new, surreal images that are similar in style or color.
4. BERT: Short for Bidirectional Encoder Representations from Transformers, BERT is a powerful tool for natural language processing. It can be used for tasks such as text classification, named entity recognition, and question answering.
5. DeepDream: Developed by Google, DeepDream is a tool that uses neural networks to generate surreal and abstract images from existing images.
6. Pix2Pix: This tool uses a technique called conditional adversarial networks (GANs) to generate images based on input images. It has been used to create everything from realistic portraits to hand-drawn sketches.
7. Runway: This is a powerful and user-friendly platform that allows users to experiment with a variety of generative AI models and tools, without needing to have any coding experience.

AR / VR Design

Augmented reality (AR) and virtual reality (VR) are rapidly growing fields that are revolutionizing the way we interact with digital content. As such, learning design for AR and VR is becoming increasingly important for designers who want to stay ahead of the curve and create innovative, engaging user experiences.

Designing for AR and VR requires a unique set of skills and considerations. In AR, designers must take into account the physical environment in which their design will be experienced, and how the digital content they create will interact with and enhance that environment. This requires an understanding of how to use visual and audio cues to guide users through the AR experience and create a seamless integration between the digital and physical worlds.

In VR, designers must create immersive, three-dimensional environments that allow users to fully engage with digital content in a way that feels natural and intuitive. This involves an understanding of how to create realistic lighting, textures, and movements within the virtual space, as well as how to create a sense of depth and scale that accurately reflects the user’s position and movements within the VR environment.

Learning design for AR and VR requires a combination of technical and creative skills. Designers must have a strong foundation in traditional design principles such as composition, color theory, and typography, as well as an understanding of 3D modeling and animation software. Additionally, designers must be able to think creatively and develop innovative solutions to the unique design challenges presented by AR and VR.

Emerging platforms like AR and VR require designers to utilize that skill in new ways and, ultimately, learn by doing. After all, prototyping is learning. — Design by Meta

Curious on where to start?

1. Unity: Unity is a popular game engine that can be used to create immersive experiences in VR and AR. It has a wide range of features and tools that can be used for creating interactive environments, 3D models, and animations.
2. Unreal Engine: Unreal Engine is another popular game engine that can be used to create high-quality experiences in VR and AR. It has a robust set of tools for creating photorealistic environments and advanced physics simulations.
3. Spark AR: Spark AR is a platform developed by Facebook that allows users to create augmented reality effects for use in Facebook, Instagram, and Messenger. It provides a range of tools for creating 3D models, animations, and interactive experiences that can be used to enhance the user experience in these platforms.
4. SketchUp: SketchUp is a 3D modeling software that can be used for creating 3D models of buildings and other structures in AR and VR environments. It is known for its intuitive interface and ease of use.
5. Adobe Creative Cloud: Adobe Creative Cloud offers a range of tools that can be used for creating visual assets for AR and VR experiences, such as Photoshop for image editing, Illustrator for creating vector graphics, and After Effects for creating motion graphics and animations.
6. Tilt Brush: Tilt Brush is a VR painting and drawing application that allows users to create 3D art in a virtual space. It can be used to create immersive environments or visual assets for use in other AR and VR applications.

These tools can be used individually or in combination to create immersive, engaging, and innovative experiences in AR and VR. By using these platforms, designers can stay up-to-date with the latest trends and developments in the field and create cutting-edge experiences that push the boundaries of what is possible in this exciting and rapidly growing field.

Overall, learning design for AR and VR is an exciting and challenging opportunity for designers to explore new possibilities in user experience design. By developing the skills and expertise necessary to create engaging, immersive experiences in AR and VR, designers can position themselves as leaders in this rapidly evolving field and contribute to the development of new and innovative applications for these technologies.

In conclusion, designers are right to be worried about the potential impact of AI on their profession. However, by focusing on their strengths and continuing to innovate, designers can remain relevant in the age of AI and continue to provide value to their clients and society as a whole.