Can AI-driven innovation outperform human creativity?

Editor’s Note: This is part of a series examining the impact of generative AI on business operations, including creativity, innovation, management, and hybrid and remote working.

Picture this: You’re in a conference room, surrounded by a mix of designers, engineers and strategists, all eager to brainstorm your company’s next big innovation. Could a machine be more effective at guiding this brainstorming session than your human team? It may sound counterintuitive, but AI is not only catching up to human creativity — it’s excelling in ways that could redefine how we approach innovation.

More in this series

How managers can leverage the productivity promise of generative AI

How businesses can fully harness the power of generative AI

Will generative AI liberate workers from the office?

Leveraging Gen AI to transform your learning and development programs

In the age of AI, idea curation will eclipse idea creation

Leading the generative AI transition through cognitive biases

The case for AI-driven innovation in creative problem-solving

A working paper from the Harvard Business School Technology & Operations Management Unit recently thrust this question into the limelight. The researchers initiated a crowdsourcing challenge focused on sustainable, circular economy business opportunities. While humans exhibited semantic diversity, AI solutions delivered better environmental and financial value. This means the very idea of creative innovation is undergoing a tectonic shift. The question we ought to be asking ourselves isn’t whether AI can be creative but how quickly we can adapt to this emerging reality.

Dismantling the conventional wisdom that machines can’t be creative is essential. A study from the Humboldt University of Berlin went straight for the jugular, comparing the quality and quantity of ideas generated by humans and AI. The result? No qualitative difference in creativity. Moreover, while 9.4% of humans were more creative than the most creative generative AI, GPT-4, it’s a negligible margin given the rapid pace of AI evolution.

A study conducted by the University of Montana demonstrated something extraordinary: Artificial intelligence can rival the top 1% of human thinkers in creativity. Using the Torrance Tests of Creative Thinking, the results placed ChatGPT, powered by GPT-4, in elite company for fluency and originality. Although it fell slightly to the 97th percentile for flexibility, this demonstrates that AI isn’t merely mimicking patterns — it’s generating them. This seismic shift has serious implications for how we approach AI-driven innovation, particularly in a corporate setting.

Recent research from Wharton Business School adds a new layer to this complex picture. The study compared the ideation capabilities of ChatGPT-4 with those of students at an elite university. Not only did ChatGPT-4 generate ideas faster and cheaper, but those ideas also ranked higher in quality when evaluated using purchase-intent surveys.

Even more compellingly, the lion’s share of the best ideas in the study was generated by ChatGPT, not by the students. And get this: ChatGPT’s performance soared further when it was provided with a few examples of highly-rated ideas. This study has profound implications for the management of innovation and points to a future where AI could be the backbone of a company’s innovation strategy.

How AI and humans can collaborate

If AI-driven innovation performs on par with or even outstrips human creativity in specific contexts, the logical next step is an integrative approach. Imagine a future where a generative AI takes the first stab at problem-solving, followed by human evaluation to add that touch of semantic diversity and nuance. What we’re talking about isn’t competition but collaboration.

It’s the blend of AI’s computational prowess with the human capacity for intuitive understanding that will pioneer uncharted territories in innovation. And that’s what I’ve seen when helping my clients adopt AI as a creative tool of innovation.

In the fashion industry, I worked with a globally recognized apparel brand with thousands of employees to leverage AI-driven innovation in their design process. Facing market saturation and diminishing returns from conventional design methods, the brand sought a novel approach. They trained an AI model that analyzed a confluence of variables — global fashion trends, social media sentiments and even the prevailing political climate. This wasn’t rudimentary data analysis; it was a comprehensive, multi-dimensional evaluation aimed at predicting what would resonate with consumers at a deeper level.

The AI-generated initial design sketches serve as data-driven inspiration for the human designers. These were not mere templates but creative catalysts, reflecting emerging trends and consumer inclinations. Human designers took these drafts, tweaking and enhancing them to add the intuitive touch that only human creativity can provide. The resulting clothing line was both a commercial and critical success, hailed for its innovation and ingenuity.

In terms of quantifiable metrics, the collaboration led to an 18% uptick in sales compared to the previous year’s collection. Moreover, the AI-enabled process cut design costs by over 20%, speeding up the initial design phase and enabling quicker market entry. This wasn’t a marginal improvement but a paradigm shift in fashion design at scale. The brand successfully broke through market saturation, achieving both financial growth and artistic acclaim through the judicious use of AI.

In another example, in a competitive landscape, a gourmet food chain sought to invigorate its brand and attract new customers. Facing stagnant sales and an increasingly undifferentiated market position, the leadership decided to innovate their menu but was concerned about the risks tied to cost and brand perception. To navigate this, they asked my help to employ an AI-driven approach in collaboration with data scientists. The machine learning algorithm analyzed customer reviews, social media sentiment, regional taste preferences and ingredient seasonality to predict future consumer preferences.

The AI generated a wide array of potential recipes, even suggesting innovative combinations of ingredients that human chefs had not considered. However, the chefs’ nuanced understanding of taste and texture complemented the AI’s computational ability. They fine-tuned the AI’s suggestions, leading to a new menu line that balanced the brand’s heritage with culinary innovation.

The pilot launch of this new menu in select locations led to an 18% sales increase, a 10% boost in new customer footfall, and a 5-point uplift in Net Promoter Score.

Based on this success, the chain planned a full-scale roll-out and used the real-time feedback loop enabled by AI for quick iterations. This case study underscores the power of human-AI collaboration for risk-mitigated, creative problem-solving in a competitive sector.

It demonstrates that AI can significantly accelerate the creative process, providing pioneering and profitable outcomes. Companies in various industries can learn from this example, recognizing that the synergy between AI and human expertise is a potent formula for innovation.

The risk landscape

While the transformative potential of AI in driving innovation is compelling, a sobering lens must also be cast on the risk landscape that businesses must grapple with as they increasingly rely on artificial intelligence. Failing to anticipate and mitigate these could lead not only to business disruptions but also to ethical, legal and societal calamities.

One core risk is that of automated decision-making running amok, particularly when based on biased or flawed data sets. This isn’t merely a question of producing sub-optimal creative solutions; the implications could be grave, leading to loss of consumer trust or lawsuits. For instance, if an AI-driven fashion design model were trained on data that implicitly favors a particular ethnic group, the resulting designs could be perceived as discriminatory, causing reputational damage that is hard to quantify but impactful nonetheless.

Then there is the issue of security, which is often underestimated in the AI conversation. Machine learning models can be targeted and manipulated by malicious actors to produce erroneous outcomes — or worse, to act in ways that are explicitly harmful. Imagine a competitor managing to compromise the AI system you rely on for innovation. They could feed it data that leads to unviable or even damaging business strategies, all without immediate detection. As AI systems become more complex, their susceptibility to “adversarial attacks” that exploit the model’s weaknesses to produce incorrect outputs also rises.

There’s also the question of AI interpretability, often known as the “black box” issue. When an AI system makes a decision, understanding how it arrived at that conclusion is often elusive. This lack of transparency can be a significant concern when justifying creative or strategic choices to stakeholders. If an undesirable outcome were to occur, it would be challenging to dissect the AI’s decision-making process for accountability.

Lastly, there’s a speculative yet essential issue — AI alignment and control. As AI systems grow more powerful, ensuring that their goals align with human values becomes exponentially difficult. A misaligned superintelligent AI could pursue objectives detrimental to human interests or ethical norms, including seizing control of the future and making humanity obsolete, even nonexistent. While this is often viewed as a futuristic concern, it warrants consideration, given the rapid advancements in AI capabilities.

To mitigate these risks, organizations should adopt a layered strategy. First, diversify the data sets that train AI models to minimize biases. Second, integrate robust cybersecurity measures specifically tailored for AI systems, and third, develop a transparent audit trail for AI decision-making, making it easier to ascertain responsibility. Additionally, a dual oversight mechanism, where both humans and independent AI systems review the outputs for anomalies, can offer an added layer of safety. Investment in AI ethics and governance will not be a choice but a necessity, requiring ongoing monitoring and adjustments to adapt to the evolving AI risk landscape.

Preparing for the AI-driven innovation landscape

As we hurdle into this counterintuitive future, those poised to lead will be the organizations that adopt an AI-centric approach to innovation. It’s not merely about utilizing AI as a tool but integrating it into the very fabric of the decision-making process. Those clinging to the traditional, human-centric model of innovation will find themselves increasingly isolated, unable to compete with the agility and efficiency that AI brings to the table.

This first appeared in Entrepreneur.
The information and opinions presented are the author’s own, and not those of Vistage Worldwide, Inc.

Related Resources

AI Resource Center

3 ways leaders can start unlocking the benefits of AI

0 views