Harvard Business Review featured a highly popular article by Brian Solis, “Train Your Brain to Work Creatively with Gen AI.”

Summary: When most people prompt generative AI, they do so within the paradigm of how they think about what could or should come next. For example, when searching Google, users may ask a question, search for the best Thai restaurant “near me,” or insert specific criteria based on filtered output, such as “best downhill mountain bike for intermediate riders.” That approach is often carried into prompting. The results build on a linear path of thinking, research, and decision-making based upon the world as we know it. However, where AI starts to “come alive” is when you create something unique — something that wouldn’t have been achieved without a human and machine collaboration. This requires challenging your own conventions around how you think GenAI should work, and also the outcomes you think are expected or possible. This article offers 12 exercises to help you expand the way you think about prompting GenAI. Leaders who foster these mindshifts within their teams will find themselves cultivating a culture where “the impossible” becomes a daily challenge — and achievement.

Excerpt:

There are countless articles on how to use generative AI (gen AI) to improve work, automate repetitive tasks, summarize meetings and customer engagements, and synthesize information. There are also scores of virtual libraries brimming with prompting guides to help us achieve more effective and even fantastical output using gen AI tools. Many common digital tools already feature integrated AI co-pilots to automagically enhance and complete writing, coding, designing, creating, and whatever it is you’re working on. But there is so much more to generative AI beyond enhancing or accelerating what we already do. With the right mindset shift, or mindshift, we can train our brains to creatively rethink how we use these tools to unlock entirely new value and achieve exponential outcomes in what’s becoming an AI-first world.

When most people prompt, they do so within the paradigm of how they think about what could or should come next. For example, when searching Google, users may ask a question, search for the best Thai restaurant “near me,” or insert specific criteria based on filtered output, such as “best downhill mountain bike for intermediate riders.” That approach is often carried into prompting. The results build on a linear path of thinking, research, and decision-making based upon the world as we know it. This is perfectly normal and effective. In fact, it’s how today’s generative AI models largely work.

Generative AI relies on natural language processing (NLP) to understand the request and generate relevant results. It’s basically pattern recognition and pattern assembly based on instructions to deliver output that completes the task at hand. This approach aligns with our brains’ default mode: pattern recognition and efficiency-seeking, which favors short, straightforward prompts to get immediate, predictable results.

If most people use gen AI in this way, then no matter how powerful the tools, we inadvertently create a new status quo in how we work and create. Training our brains to challenge our thinking, our assumptions of AI capabilities, and our expectations for predictable results starts with a mindshift, to recognize AI not as just a tool, but as a partner in innovation and exploring the unfamiliar.

Click here to read the full article at HBR.