The Role of AI-Generated Images in Storytelling
Storytelling is used extensively, from conventional applications in books, literature and education materials to brand marketing, online content and promotions. You'll even find storytelling in instructional manuals and airline safety videos!
An increasing proportion of companies use an AI generator to develop the images and graphics they need to communicate visual messaging as an accessible way to deliver an idea or work through a concept, which viewers can grasp more quickly than reading long-form text.
If you are interested in fostering innovation with AI-generated content to bring your stories to life, we'll explain how algorithms can help and the benefits of using graphics to get your message across.
What Is Storytelling and Why Is AI Important?
Stories are a fundamental concept in all forms of marketing–consumers want to engage with organisations, businesses and people who they relate to and who have a compelling backstory that incentivises them or makes them feel part of a community.
Although we often think of stories as a book or entertainment for children, storytelling is used across the digital landscape in targeted promotions, public service announcements and how-to literature.
How Are AI-Generated Images Used in Storytelling?
AI-generated content is an increasingly popular way to generate text and images quickly, cheaply, and sustainably, drawing on the capacity of machine learning algorithms to produce the assets necessary to complete a storytelling process. For example, if you log into Netflix and review your personalised recommendations, those are produced by an algorithm that analyses your data and preferences to refine specific suggestions.
Tailored content with curated graphics, short intros and summaries are designed to capture your attention, meet your expectations and ultimately improve your viewer experience–and it’s all generated by AI!
Other examples include sports reports, event coverage and interactive visualisations (check out the New York Times for a real-world illustration), where animated graphics and charts are AI-generated to explain a story in an approachable manner.
What Are the Advantages of Storytelling Through AI-Generated Images?
There are several benefits to incorporating AI image generation into storytelling, whether to personalise the interaction, as in the examples above, or to create visuals that are unique to your brand or organisation. AI-generated content can also be developed with data analytics to improve the relevance and impact your graphics have on your intended audience.
- AI is highly efficient and produces content in a fraction of the time and cost required to manually generate sufficient imagery to visualise your concepts.
- Powerful algorithms are adept at customisation, using data analysis to ensure the visuals and content shown to each consumer are adapted to their location, needs and requirements.
- Once trained, an AI algorithm is accurate and operates to a high level of specificity by understanding user patterns, preferences and trends, responding to changes, reducing the potential for irrelevance and enhancing the impact and credibility of the messaging.
- Organisations can scale easily, using AI-generated content based on their objectives and lifecycle and automating tasks to help them optimise efficiencies and make the best use of their resources.
To be effective, storytelling needs to be authentic and credible. By ensuring each communication, graphic or step in the process is tailored and relevant, brands improve their reputation and engage with their audience on a one-to-one level, even if their target demographic is broad.
Are There Challenges Associated With AI-Generated Imagery for Storytelling?
As with any AI-enabled or generated approach, graphics and communications should be overseen and tweaked by humans where necessary, particularly if a first iteration of an image lacks the personal touch that makes stories compelling. Businesses and organisations utilising AI content generation should implement sufficient quality controls and standards to ensure their content is appropriate and consistent to avoid any obvious missteps that can compromise the credibility of the overall messaging.
Likewise, algorithms can sometimes rely on bias, depending on the datasets they have been trained in. It remains essential to use AI transparently and with the right management level to ensure the content produced is accurate, inclusive and representative. Transparency is key, and where consumers are conscious that a vast proportion of the content they consume is AI generated, this ensures that the audience trusts the communications they receive and retains full control over how they choose to interact.