First to market

Generative AI has changed, and is changing design ideation. Today, it is nearly impossible to avoid AI-powered search engines, such as ChatGPT when generating ideas. Indeed, AI is quickly changing how people search and use the internet more generally. Instead of getting a set of links to follow from conventional search engines, such as Google, AI-powered chatbots give short answers directly, which satisfy many queries and help generate ideas. That is, you can ask the chatbot any question and get a response in real time. As a result there is an abundance of AI generated ideas, and few are truly original, as any general search engine will testify. That is, most ideas are tiny adjustments or small alterations to existing ideas or combinations of ideas from many sources. So, is AI coming for your ideas? Well, individual ideas are not protected by copyright unless the copying is a direct copy of a specific work. This fact highlights how designers, to protect their ideas, better focus on realising their ideas, that is, to turn the idea into a specific work. And so, to execute the idea is what matters. In other words, "first to market". (However, having turned the idea into a product or service and made it available to the market before anyone else doesn't guarantee commercial success).

AI tools, AI agents and heuristics

When engaged in ideation and problem-solving it can be difficult to differentiate between AI Tools, AI agents or heuristics. Simply, AI tools, such as ChatGPT work in a reactive manner in response to prompts whereas AI agents operates independently without human intervention. In contrast, heuristics are adaptive tools involving experimentation and trial and error. Or, to use an aviation metaphor, AI tools are like co-piloting, AI agents similar to auto-piloting, and heuristic tools comparable to flying manually. Key considerations when deciding between these tools include: task predictability; complexity of decision-making; and need for adaptability. The choice, however, depends on user needs. AI agents, for example, in automating decision-making processes, don't need step-by-step instructions but fall short of true autonomy. That is, in real-world applications, AI agents are faced with technical challenges and issues of trust and security. For everyday design, this suggests a pragmatic approach to problem-solving where heuristics, grounded in simplicity and domain expertise remain a viable option, particularly to ensure decisions are made with ethical considerations and human oversight.  Sources:  https://relevanceai.com/relevance-academy/when-to-use-agents-v-tools  https://www.ibm.com/think/topics/ai-agents  https://mikecarruego.medium.com/choosing-the-right-algorithm-machine-learning-vs-heuristics-dc0b65e97d98