The Top AI Tools for Innovators in 2024

As innovators, we can sense that the latest AI wave is different from past hype cycles. A decade ago, it was obvious that AI was an ideal technology to automate any skill that could be heuristically defined. But GenAI is different! It can generate ideas at a rapid clip and seemingly beats humans at creativity.

The bad news: GenAI has raised the floor of creativity. The good news: the opportunity (and the race) to augment your creativity with GenAI and lift the creative ceiling is on. This article looks at some of the top AI tools that can help you make the most of that opportunity.

“You aren’t going to lose your [creative] job to an AI, but you will lose it to someone using AI.”

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AI’s Creative Supremacy?

Over the last six months, Ethan Mollick has led and shared research that seems to indicate that GenAI is better than humans at creativity. I won’t unpack all of those research papers here, but based on popular, and woefully outdated, creativity research that relies on the Alternate Uses Task (AUT) to assess creativity, GenAI seems to smoke humans. But when you take into account the latest scientifically-validated approach to testing creativity, the Divergent Association Task (DAT), GenAI is way behind the top human performers (~70 percent vs 90+ percent for humans.) Also, one of Mollick’s main research papers highlights one of GenAI’s biggest creative weaknesses: even though the research found a 40 percent increase in idea quality using GenAi, it also showed a 41 percent drop in the diversity of ideas generated.

Creativity and innovation has permeated every aspect of business in the last two decades, giving rise to the popularity of design thinking. The rise of the creative class, the 55 million people who make their living by being creative, has enabled companies to drive growth and competitive advantage. As much as GenAI can generate ideas, any company that automates or outsources their creative functions to GenAI will face a reckoning when their competitive advantage is blunted by the lack of diverse ideas.

If you want to leverage GenAI tools to get a head start on the race, I have broken down the creative process and mapped some of the best, currently available tools that me and my team have been exploring. First, let’s explore Mollick’s latest foray into AI-led creativity: Innovator GPT.

Mollick’s Innovator GPT: A Case Study in AI-Driven Ideation

Ethan Mollick’s recent creation, Innovator GPT, epitomizes the integration of AI in the innovation process. Mollick asserts that GenAI, particularly GPT-4, tends to outperform humans in ideation. His process involves transforming problems into open-ended questions, crafting a mix of original and researched ideas, and producing constrained ideation to merge ideas for new solutions. This systematic approach is the epitome of raising the creative floor. However, this approach has a major downside: premature convergence and basic associations lead to a lack of idea diversity and depth. 

Our ‘Personal LLMs’

The first large language model (LLM) everyone needs to learn to use effectively in order to maximize creativity with GenAI is our own brain, aka our “personal LLM” (PLLM). By better understanding how creativity works in our “PLLMs,” we can begin to harness the true potential of AI-augmented creativity.

Divergent association or, as I call it, “dis-associative thinking,” is needed to get past the low hanging fruit, first-order associations that plague traditional approaches to creativity and out-of-the-box GenAI.

As Einstein shared in his thoughts on what made him creative, “The psychical entities which seem to serve as elements in thought are certain signs and more or less clear images which can be ‘voluntarily’ reproduced and combined… this combinatory play seems to be the essential feature in productive thought…“

More recently, Neuroscientist Elkhonon Goldberg offered a compelling analogy in his book Creativity: The Human Brain in the Age of Innovation to explain why combinatory play with diverse ideas is the key to creativity: a “Lego master builder.”

Combinatory Play: “The lateral prefrontal cortex, in particular, has the ability to manipulate the mental representations stored in the brain like Lego pieces: to assemble them into new configurations through a deliberative, goal-driven process.”

Tapping into the Unconscious: “The actual ‘Lego pieces’—the information manipulated by the prefrontal cortex — may be stored in other parts of the brain, to a large extent in the parietal, temporal, and occipital lobes — and the prefrontal cortex acts like the player accessing the pieces from various compartments according to the mental blueprint he or she has devised.” (Goldberg, Elkhonon PhD, ABPP. Creativity (p. 46). Oxford University Press. Kindle Edition.)

AI Tools for Design Thinking

Let’s breakdown the tools through the lens of a high-level, jobs-to-be-done (JTBD), design thinking approach:

• Problem Finding
• Problem Understanding
• Problem Solving.

1. Problem Finding

Key JTBD: Signal scanning to understand the problem space and drive broader exploration.

• Signal Scanning: Waldo and Wonder can scan for macro signals, helping to identify emerging trends.
• Not There Yet: We have explored the use of prompting to create synthetic users, but unfortunately the sophistication is limited, and only seems to scale caricatures of real people. We’re working to develop deeper persona modeling leveraging behavioral science and broad data sets from real users to create better synthetic users, but currently this is a weakness of GenAI. (Indi Young has a great piece on this topic.)

2. Problem Understanding

Key JTBD: Being able to analyze and synthesize both qualitative and quantitative data is a powerful use case for GenAI.

• Ethnographic Research: LLMs like GPT-4 can assist in analyzing qualitative data from interviews and ethnographic studies.
• Synthesis: NotebookLM is a powerful tool that enables deep synthesis of diverse data sources to create a comprehensive understanding of the problem space.
• Not There Yet: We haven’t yet seen great tools for creating visual frameworks like persona grids and detailed journey maps. We believe this will be coming soon.

3. Problem Solving

Key JTBD: Generating and rapidly testing solutions through rigorous experimentation.

• Experimentation: ChatGPT and Claude can be prompted to leverage an experimentation-first approach to develop and evaluate various scenarios. These scenarios and solutions need to be narrowed down and tested against real users, a task still better handled by experienced humans rather than automating with AI.
• Prototyping: Using DALL-E 3, Midjourney, or specialized tools like Diagram can help with accelerating prototyping design.
• Market Testing: CoLoop can help analyze the market feedback from testing with real humans.
• Not There Yet: Out-of-the-boxGenAI seems to develop solutions rapidly, but without a rigorous approach to experimentation and hypothesis generation, you’re left with a shallow idea space.

Key Takeaways

Based on the currently available GenAI tools, we’re a long way from ceding human creativity and innovation to technology. The best first step any innovator can take to future-proof themselves is to learn more about how creativity works in our brains, in order to better leverage the incredible power of our “PLLMs.”’” Then, I highly and humbly encourage you to get hands-on and experiment with any or all of the tools mentioned in this article. Here’s a quick breakdown of key use cases for the tools mentioned:

Five GenAI Tools for Creativity:

• Problem Finding:
  • Waldo – signal scanning and research
  • Wonder – signal scanning and research
• Problem Understanding:
  • NotebookLM –  synthesis
• Problem Solving:
  • Diagram – prototyping design
  • CoLoop – testing and experimentation analysis

Five Popular All-Purpose GenAI Tools:

• ChatGPT
  • problem understanding: ethnographic research analysis
  • problem solving: scenario development and evaluation
• Claude – problem solving: scenario development and evaluation
• DALL-E 3 – problem solving: prototyping design
• Midjourney – problem solving: prototyping design
• Perplexity – all-purpose: the new upgrade to search.