Explore Ciklum’s Guide to Practical Generative AI Examples

June 28th 2024
Lucian Gruia
Principal AI Tech Lead
Explore Ciklum’s Guide to Practical Generative AI Examples

Over the last couple of years, artificial intelligence (AI) has been transforming just about every sector you can think of, and especially in its Generative AI form. It’s now widely recognized as a major driver f growth: according to Google research, 82% of organizations who currently use GenAI or are considering doing so believe that it will significantly change their industry.

However, there has been somewhat of a digital gold rush around AI, and many organizations have dived headlong into expensive AI investments without connecting the technology to practical and relevant use cases. With AI in business likely to become an even bigger driver of growth and competitive advantage in the months and years ahead, matching AI to the right areas and applications is critical.

This blog will take a look at applications of Generative AI for a range of business sectors, and give you an understanding of where you can direct your AI investment for the best possible benefit.

Understanding Generative AI

Generative AI is the form of artificial intelligence that can create all types of media and content, including text, images, videos and even 3D models. Generative AI models work by identifying patterns in a vast array of different data sets, whether that’s books, image libraries, web pages or other sources, and using those learnings to support AI content creation based on user prompts. 

Because Generative AI applications learn and train themselves through the data that they process, their capabilities are constantly getting better and better all the time. That sets GenAI apart from other emerging technologies in terms of the scale of its potential: it has the power to transform workplace productivity, support better innovation and experimentation, and break down barriers to information and skills.

Gen AI understanding

 Practical Examples of Generative AI

It seems like the potential use cases of Generative AI in business are virtually limitless: from communication tools like summarisers and grammar reviewers, through customer service aids like chatbots for common queries, to personalized recommendations and offers in marketing and advertising. However, now more than ever, it’s vital to understand the best Generative AI applications for individual industries:

Icon1_Education Generative AI examples in Education

Generative AI has the potential to upend traditional teaching norms by redefining how knowledge gets delivered and absorbed; bringing fresh perspectives, better vocabulary, and simplified high-level concepts to clarify complex topics in the classroom. This helps students gain deeper subject understanding and more meaningful learning experiences.

Automating tedious tasks, generative AI also liberates time for teachers to craft personalized learning paths tailored to each student's needs. This inturn facilitates differentiated lesson plans and assessments, catered to individual learning styles and abilities.

Icon2_Research Generative AI examples in Research

In the field of research, Generative AI can rapidly process vast datasets, uncovering errors and biases that humans may overlook. These generative models also serve as powerful brainstorming partners, churning out numerous creative concept ideas during the initial exploratory stages. 

As a supporting tool, GenAI empowers researchers to ask bigger questions and explore a wider range of potential solutions.

Icon3_tourism Generative AI examples in tourism and hospitality

Generative AI is proving instrumental in enabling the frictionless travel experiences that many consumers - especially from younger generations - are increasingly coming to expect. The ability for GenAI to personalize content means that contactless journeys can be tailored to individual preferences, with minimal human intervention needed.

It’s also possible to use GenAI to automatically translate travel information and eliminate language barriers, including in the use of AI-powered customer support and automated chatbots. Travel itineraries and virtual reality tools can also be generated to give travelers a taste of their trips to come.

Icon4_healthcare Generative AI in Healthcare

According to CapGemini, AI could support as many as half of healthcare-related activities by 2045, with a substantial portion of that covered by Generative AI. 

For example, Generative AI can be used to create tailored medicine and treatment plans for patients, based on the specifics in their medical record and previous results of similar treatments for other patients. This can be delivered as part of an AI-driven patient interaction platform, where they can also converse with chatbots to get help with common queries. More widely, AI can also help with early detection of diseases and similar diagnostic work. 

Icon5_Finance Generative AI in Finance

With so many financial services and operations based around data, the banking sector is a prime candidate for transformation through Generative AI.

This works in two ways. The first is enhancing customer-facing services and functionalities, such as providing personalized financial advice, and assisting with fast and effective customer service when problems arise. The second is to improve many of the processes behind the scenes, such as fraud detection and prevention by analyzing suspicious activity and generating reports. Forecasting and risk management can also be covered in much the same way. 

Icon6_Entertainment Generative AI in Entertainment

Entertainment is based around content, which makes Generative AI a really good fit for supporting the media and gaming industries. The ability to automate through AI powered content creation, whether for the written word, still images, moving images or audio can enable new creative avenues to be explored. 

On a more practical level, GenAI can also drastically speed up the production of content compared to current manual processes -  as long as the GenAI tool is used ethically and responsibly along the way.

Icon7_SW development Generative AI in Software Development

At a time when skilled coders and software developers are in short supply - and therefore very expensive to recruit - Generative AI is ideal for bridging the gap. Being able to generate sections of code based on user prompts can make development processes easier, quicker, more cost-effective and less vulnerable to human errors that cause bugs and user frustration.

Tools like Google’s Gemini 1.5 Pro are already allowing automated code generation to enter the mainstream, and ideally, can be dovetailed with intelligent project management tools and AI-driven code reviewing and debugging software to check for inconsistencies and drive further efficiencies. More widely, GenAI is also supporting low-code and no-code development processes and platforms.

The Future of Generative AI

AI is still in its relative infancy, and as the recent rise of Retrieval-Augmented Generation has proved, it can evolve very quickly. This means the jury is still very much out on how it might evolve over the next few years. 

For example, on one hand, Elon Musk has said that Artificial General Intelligence (AGI) - where AI matches or even surpasses human capabilities - may be with us within two years. On the other hand, OpenAI’s Sam Altman says AGI will not be the revolution that many believe it could be.


What we can be more confident in predicting is that business use of Generative AI will continue to expand in the short term. According to Gartner, 75% of businesses will use GenAI to create synthetic customer data by 2026, and by the following year, more than half of all GenAI models will be specific to an industry or business function. 

At the same time, as more of Generation Z enter the job market, more and more people will become used to using Generative AI on a day-to-day basis. For example, ChatGPT is already being used by 62% of working professionals, and more than a third of its current users are between 25 and 34.

With every technology leap also comes a set of challenges, and GenAI is no different. As we see it become more mainstream and commonplace, many of its potential issues and limitations will have to be addressed: data protection, ownership, ethics, the risk of bias, copyright, and even the carbon footprint of running these powerful tools. And that’s before considering the risk of AI replacing human endeavor and rendering people’s jobs obsolete.

Gen AI future stats

In summary: action now will pay off later

It’s clear that Generative AI already has a vital role to play across many industries, and will only become more important in the future. So now is the time to invest in the future and stay ahead of the curve, and start yielding the rewards of improved productivity, efficiency, cost and competitive advantage.

But as this blog demonstrates, it’s vital to understand the right practical use cases for GenAI, and that’s where the experience engineering of Ciklum can be invaluable. We’ve built a strong level of expertise in AI, developing our own framework to share cross-domain experience, run a continuous upskilling programme that prompts knowledge sharing and keep connected through industry events and scientific conferences. This helps us develop suitable Generative AI models that are perfectly matched to our customers, and help them translate the excitement of AI into tangible business benefits. Find out more about Ciklum Generative AI, or for further reading, explore this blog on how to get started.


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