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How generative AI can augment human creativity in business
From personalized marketing and prototyping to business workflows and planning, generative AI can augment creativity as a new collaboration tool for humans in enterprise settings.
Generative AI has become a popular option for creative projects. This use case isn't unique to artistic work; businesses can also use generative AI to help with the creative aspects of tasks.
Generative AI models analyze existing data -- often very large data sets -- and use the patterns and relationships they find to create new content, such as text, images, audio and code. This ability to produce new content means that generative AI opens up a wide range of possibilities for creativity, letting users brainstorm and prototype with unprecedented speed and variety.
Generative AI models often yield unexpected outputs. Sometimes, these novelties are errors, called hallucinations. For example, generative AI is famously bad at drawing human hands and might create otherwise interesting, realistic pictures with extra fingers. Or, more seriously, generative AI can produce text containing factual errors or made-up information.
Other times, however, generative AI's outputs are useful and can inspire new ideas. This is true not only for creatives such as artists, designers, musicians and writers, but also for business strategists and product managers. Using generative AI as a creative companion has fascinating possibilities for businesses -- as long as it's paired with a careful approach to governance and a healthy dose of human curiosity.
The role of creativity in business
Creativity helps businesses thrive because it leads to greater adaptability and more successful answers to emerging challenges. Together, creativity and innovation are essential for growth and profitability.
Organizations can encourage creativity through incubators, design sprints, hackathons, focused collaboration and incentives. These initiatives help create a culture of experimentation and curiosity. But sometimes teams need help with developing new ideas. This is where generative AI can help.
Businesses can use generative AI tools to enhance the creative process of ideation. For example, human brainstorming sessions often reach limitations when people can't break out of conventional thinking, or run out of ideas and energy. AI tools can help by generating numerous ideas quickly and without the creative blocks that hold humans back, providing raw material for humans to develop further.
Real-world examples of augmenting human creativity with AI
Creative tasks can be found in every corner of the enterprise. Opportunities for generative AI augmentation include both client-facing applications, such as marketing and product visualization, and internal use cases, such as strategic planning and analysis.
Create personalized marketing content
Generative AI is already commonly used to create personalized marketing content. For example, Advaiya Solutions in Bellevue, Wash., developed an AI application for a landscape design and maintenance company. Using a public large language model, the application writes magazine-style stories for clients based on specific design proposals, enabling clients to better imagine proposed design plans.
Refine prototyping
The Board of Innovation consultancy uses generative AI to visualize sustainable packaging options for consumer goods. These AI models are able to generate both new concepts and high-quality visualizations of those concepts more rapidly than a human designer could.
Similarly, generative AI tools like Vizcom can render 3D images from 2D product sketches, accelerating the prototyping process for designers. This also enables designers to bring more realistic visual concepts to customers and clients to gauge their interest. And because these tools reduce the amount of human work required in the early design stage, designers are likely to find that they are more open to feedback about concepts that have been generated in this way.
Kick-start creative workflows
Loft Design, a product design agency in Boston, has found that image generation tools such as Midjourney can be valuable early in the design process when creating concepts, but are less useful later on when refining designs. The agency also discovered that individual designers use generative AI in different ways, reflecting that generative AI plays a personal role for designers rather than just standardizing the workflow.
Assist strategic planning
Generative AI can also augment business planning, which requires a great deal of imagination and expression. For instance, in budgeting, generative AI can run numerous simulations of business outcomes without using specialized predictive analytics platforms. Similarly, generative AI can assess business strategies to find areas for improvement -- for example, in stakeholder analysis and competitive analysis.
Some strategic planning tasks require both systematic analysis and creative wordsmithing, especially when creating goals and imperatives for teams. Generative AI can greatly assist in defining the specific initiatives needed for a strategic plan as well as crafting a compelling vision statement for the strategy. Some teams even train an AI chatbot with entire books by relevant business consultants, and then use the trained model to answer questions about business strategy and practices.
Given a strategic goal, such as "deliver a voice of the customer platform," a business analyst might come up with four or five useful initiatives. Generative AI, given the same task, might quickly generate 10 or 20 possibilities that the analyst can choose from and tweak, saving time and energy.
Limitations and ethical considerations
While generative AI can augment innovation, there are real concerns about its impact on creativity. If vast amounts of new content can be created with little effort, will this devalue human-made content or even replace human roles entirely? Others are concerned with intellectual property (IP) issues, ethical use, misinformation and manipulated content.
Generative AI, like other forms of machine learning, works by discovering patterns in existing data and applying this knowledge to new content. So, one immediate concern is the authenticity and originality of AI-generated content, as these tools might inadvertently reproduce copyrighted material. To mitigate this risk and ensure originality, it's essential to only use AI-generated content as a starting point, not a finished creative project. Some tools are also trained with copyright in mind and offer IP indemnification.
Another concern is the potential for AI to perpetuate biases that might exist in training data. For example, if a model has been trained primarily on the work of male European designers, then that aesthetic will be reflected in its output.
Addressing bias in any model requires careful data curation, human review processes and specialized debiasing techniques. Large-scale public models -- trained on vast and diverse data sets -- might dilute certain biases, but can introduce others due to less oversight of their training data. Smaller, industry-specific models, while prone to amplifying bias if trained on nondiverse data, can also reduce the risk of bias and hallucinations in other contexts. This is because their training data is usually more carefully curated, offering developers more control.
Future creative collaboration between humans and AI
Generative AI has both great potential to augment human workflows and limitations that require human oversight. Therefore, AI will not replace human creativity; instead, it will serve as a tool to enhance and extend human capabilities.
One human quality that is missing from AI tools is curiosity: the restless need to explore new ideas. This comes from human agency and motivation, and is something AI cannot replicate. Humans also fulfill the critical needs of providing context, strategy and empathy, ensuring that AI outputs are aligned with business goals, quality standards and organizational ethos.
However, if the future of creativity lies in embracing a collaborative approach where humans' work regularly incorporates AI output, then companies need to prioritize education about ethical AI use and a responsible approach to AI governance. They should also deliberately encourage uniquely human capabilities, like curiosity and compassion, alongside AI development.
Donald Farmer is the principal of TreeHive Strategy, which advises software vendors, enterprises and investors on data and advanced analytics strategy. He has worked on some of the leading data technologies in the market and in award-winning startups. He previously led design and innovation teams at Microsoft and Qlik.
