Introduction
Generative AI is everywhere in the zeitgeist right now, being a prevalent topic of discussion throughout a number of industries for how it’s transforming the world of business. There’s no doubt that gen AI has ushered in a revolutionised way of driving innovation, efficiency, and creativity, within healthcare, retail, education, and many other industries, but there’s a lot to be concerned about too.
The applications, implications and use cases of gen AI are seemingly countless, but here we’ll look at some of the major industries that stand to benefit (or already are) by leveraging this tech in their day-to-day operations.
What is gen AI?
Before we look at its applications, it’s important to understand how generative AI works, especially in relation to traditional AI.
Simply put, traditional AI follow a specific set of rules based on a particular set of inputs it has been given and trained, taking that data and forming decisions or predictions within the confines of those rules. Critically, traditional AI can only ever work within those rules and can never creating anything new; it can only predict using the data it has. Generative AI, however, can.
Generative AI relies on expansive datasets and its models still refer to the data its been trained on but, unlike traditional AI, it can create brand new, high-quality content, from text, images, music, code, synthetic voices, and plenty more. This capability has been unseen until now and the tech boasts immense potential in having a game-changing effect on myriad industries.
Applications
Retail
In the retail industry, generative AI is transforming the customer experience and supporting retail owners in hugely impactful ways.
Product descriptions
Writing product descriptions that are accurate, effective, and unique can be a time-consuming process. Generative AI can not only automate the process but provide compelling and search engine optimized product descriptions in a fraction of the time. This helps engage customers with more accurate information that can help them make informed choices and drive sales for the business.
Product design
Ideating and producing innovative design concepts is a capability generative AI can handle in minutes, allowing the design workflow to be expedited and streamlined in ways before unseen. The technology can easily and efficiently explore brand new design concepts and produce visualizations that designers can then translate into products.
Marketing strategies
Data and insights from past campaigns to inform future ones is a core part of a marketer’s job, as that information, when leveraged in the right way, helps drive future performance. Gen AI can analyse previous performance and suggest an optimised strategy for a new marketing campaign, factoring in customer preferences and spending habits to create a truly personalised strategy.
Real life use case: eBay is enabling marketplace sellers to use gen AI to write their product descriptions
Healthcare
Generative AI is making significant strides in healthcare by offering valuable insights into diagnostics and treatment planning. In a field highly concerned with precision and accuracy, this tech has far-reaching benefits on safety and efficiency.
Automating admin tasks
Admin tasks in healthcare are a crucial component of managing processes effectively, but are encumbered by resource constraints, training needs, data security and a lot more. Gen AI can easily handle appointment scheduling, documentation and record-keeping, data entry, and general workflow optimisation, having profound effects on admin efficiency and effectiveness.
Medical imaging
Medical imaging supports in diagnosing, monitoring, and the treatment of medical conditions, but faces several challenges across a number of areas. Gen AI tackles these with its capability to improve image quality, which can result in more accurate diagnoses. It can also create hypothetical images that can then be added to existing datasets to enhance the training for machine learning algorithms, allowing healthcare professionals access to even greater insights and more effective learning.
Drug discovery and development
Drug discovery and development is a hugely time-consuming process that is fraught with failure. The extensive scientific research, experimentation, testing, and compliance needed has a significant financial investment and protracted timelines.
Gen AI addresses many of these challenges as it can analyse datasets that can find possible targets for new drugs and verify if they are relevant for treating diseases. Additionally, it can also predict how drugs might interact with one another, which can help healthcare professionals understand if a drug is safe and effective.
Real life use case: Google is partnering with medical device manufacturer, to integrate gen AI to detect breast cancer.
Education
Education is also experiencing a transformation with gen AI, offering teachers and students improved experiences and ways of doing things.
Personalised and optimised teaching
By understanding individual learning styles, preferences, and weaknesses, gen AI can generate customised educational content such as lesson plans, giving students the most tailored and effective resources and teaching, while freeing up teachers’ time for more important tasks. The AI can dial into past performance or student capabilities to create plans that are truly targeted at them as an individual and helps them fulfil their potential.
Course curriculum
Gen AI is effective at creating brand new teaching materials, such as classroom engagement content, reading materials that help explain key concepts, discussion points, and even study guides. With gen AI, it also means that content can be completely original and bespoke to the teacher’s requirements and student needs.
Real life use case: Duolingo, a language learning platform, is using gen AI to deliver highly-personalised language lessons, affordable and accessible English proficiency testing.
Challenges and drawbacks
While Generative AI is shaping up to revolutionise the way we carry out innumerable tasks across as many industries, there are challenges and drawbacks to the tech that need to be taken into consideration. Some of those include:
- Ethical concerns and bias: The algorithms powering these systems learn from datasets that could contain biases or discriminatory patterns, which AI has the potential to perpetuate and amplify if not governed appropriately. In industries like retail and healthcare, for instance, this could result in personalised recommendations that reinforce existing stereotypes or even contribute to health disparities.
- Data privacy and security: Gen AI relies heavily on massive datasets for training, and the nature of these datasets often includes sensitive information. With any handling of sensitive information, privacy and security must be taken into consideration and, with being a relatively new technology, stringent measures need to be put in place.
- Job displacement: A commonly discussed drawback to gen AI is the risk it poses to job displacement. With its power to automate any number of tasks, it raises the concern of replacing humans doing these jobs, particularly in the creative industries. That risk of job loss would well likely have a significantly adverse effect on the economy and is not an area we know much about at this stage.
Looking forward
The proliferation of Generative AI, not just in the industries mentioned but across all of business and society, is only the beginning. The technology will continue to advance, and its use cases will only continue to expand with it. While we will be able to considerably increase efficiency and innovative – and in many industries, like healthcare, the safety of operations – it’s crucial to mitigate the challenges and risks gen AI poses. Job displacement and data privacy are very justified concerns, but the more likely outcomes are a world where we learn to live with gen AI. What we need is to strive towards cultivating a symbiotic relationship between Gen AI and human capabilities, where the former enhances the latter, rather than outright replace it.
Contact Info:
Name: R.K.D.Manoj Piyatissa
E-mail: dmanojpiyatissa@gmail.com
Linkedin: https://www.linkedin.com/in/manoj-piyatissa-694019124
Country: Sri Lanka
