Understanding Generative AI
Generative AI models are any type of artificial intelligence that can create new content (for example, text, images or audio) from data provided to train the AI. These models can be trained to understand natural language and applied to many industries. Most recently, OpenAI’s ChatGPT (Generative Pre-trained Transformer) has become well-known, as it can be used to hold dialogues or generate versatile text forms such as stories or poems.
Adoption and Implementation
A ‘generative AI onboarding curve’ features various stages, from exploration to embedding the technology in the organisation with appropriate security structures, buy-in across stakeholders, and optimisation of use cases. To encourage innovative use of GPT-4, IgniteTech incentivised its staff to experiment with the model by awarding cash rewards for the most effective prompts.
Applications of Generative AI
Content Creation
Generative AI will write articles, blog posts, and even entire books. Contents created by generative AI would be similar to what already exists but tailored to the parameters of the prompt. Marketing teams would be able to produce social media posts, product descriptions, and email newsletters at the click of a button, which perfectly captures the voice of the organisation. Writers and journalists will be able to use these systems to generate ideas, research, and draft articles.
Design and Creative Work
Generative AI is also being used in the creative industries, creating images, art, animations and music. Text prompts or other images can be used to generate designs in programmes such as DALL-E, Midjourney and Stable Diffusion. Over in music, programmes such as Riffusion and Mubert can create new melodies and full songs based on user inputs.
Data Analysis and Visualization
Generative AI works very well when it comes to analysing and visualising large amounts of data and generating insights, summaries or other visualisations that would be difficult for humans to create on their own, and that can aid business analysts and scientists in better understanding certain data patterns and trends.
Ethical Considerations and Limitations
And while there are many valuable uses for generative AI, there are many ethical issues to consider too – not least in terms of bias that the models might embed, misinformation that it might disseminate, and ways in which it could be used for nefarious purposes. And there are the thorny issues of intellectual property rights and copyright. These models are trained on existing data, after all. Policies and frameworks need to be developed for the responsible and ethical use of generative AI.
The Future of Work
If generative AI becomes like ChatGPT, expanding to cover most business functions that human beings do, the nature of work is at stake across many industries. Survey analysis of the number of jobs at risk due to automation has long been complemented by the analysis of which jobs remain safe. As long as certain tasks bore a ‘human touch’, they were not at risk. Ethan Mollick of the Wharton School in Pennsylvania argues that we should avoid focusing on jobs that don’t, such as writing recommendation letters or grading assignments.
Embracing Change
Like all disruptive technology, only some will flourish and others will fail. Leaders will need to develop strategies to embed generative AI in their organisations, give employees the freedom to experiment, and make failure acceptable so that real innovation can happen. ‘Make managers model how to use it and share what these experiences look like to help create a culture of innovation,’ says Ethan Mollick.
Conclusion
Generative AI is potentially an incredibly productive technology, but it will likely come with ethical challenges that need to be thought through before it is put into action. In the field, everyone in organisations like UniHouse that are at the forefront of experimenting with new technologies will need to keep experimenting, and craft strategic visions for using generative AI, in order to remain competitive in what looks to be a turbulent time ahead.
Author
Aows Dargazali, Regional Winner for Europe at Meta, is an entrepreneurial Chartered Manager and an alumnus of Oxford University Executive MBA and the associated programme there. He leads an organisation that was recognised by The Daily Telegraph for its use of innovative technologies in education and healthcare. With ten years of experience developing technologies for gamified learning, Aows writes about the impact of AI in education and health at UniHouse.
