Understanding generative artificial intelligence (AI)

Generative AI language models have gained popularity across the globe. According to a report, the generative AI market is expected to have around 30% of outbound marketing messages from large organisations in 2025 as compared to a 2% in 2022. The increasing demand for generative AI models for applications such as image and video generation, chatbots and content creation will help the market to achieve a significant growth in the coming years.

What is generative AI?

Generative AI refers to a type of AI which is capable of creating new data or content based on a given set of input parameters. This involves training a machine learning (ML) model on a large dataset of existing examples and using that model to generate new data which resembles the original examples. This can include generating text, images, music, speech, or other forms of data. Generative AI models learn the patterns and structures within the training data and then generate unique content that resembles the input data.

Generative AI models can also be used to develop audio and images. For example, generative adversarial networks (GANs) can generate realistic images, where two neural networks – a generator and a discriminator – work together to create images that resemble the training data. Though generative AI is usually used in creative fields such as art, music and literature, it can also be applied in scientific research, legal services, medical applications and product design. Generative AI often uses techniques such as neural networks, autoencoders and adversarial networks. These techniques can be combined in various ways to create sophisticated generative models which can produce more realistic and diverse outputs.

Key features of generative AI

Given below are some of the key features of generative AI

1. Creativity

Generative AI models have the ability to produce new data which is similar to the training data making them a valuable tool for creative fields like art and music.

2. Contextual understanding

These models are capable of understanding and generating data in the context of the input data or environment. For example, a language model can generate responses which are contextually relevant to a given conversation or prompt.

3. Deep learning

The models usually use deep learning algorithms, such as neural networks, to learn patterns and structures in the input data and generate new content. The algorithms enable the models to establish complex relationships within the training data and generate high-quality content.

4. Training data

Large amounts of high-quality training data is required by generative AI models to create accurate output. The quality of the training data also has a direct impact on the quality of the output.

Industry-specific uses of generative AI

Generative AI can produce novel content and data which can benefit many industries in different ways. The various formats of content which can be generated with generative AI models can help different sectors for instance, generative AI can be implemented to optimise product design and to enhance processes in the manufacturing industry. Similarly, it can also help risk management by generating synthetic data for risk assessment. Let’s look at how generative AI can help different sectors.

Legal services

FinTech

Marketing

Media and entertainment

Education

Generative AI has emerged as a ground-breaking technology with immense potential to transform the healthcare industry. By leveraging advanced algorithms and machine learning techniques, generative AI is revolutionising various aspects of healthcare, including diagnostics, drug discovery, patient care, and medical imaging. Given below are some areas where generative AI can be used in the healthcare industry.

Medical image analysis: Generative AI models can be used to analyse medical images such as X-rays, MRIs, and CT scans, to help healthcare professionals make more accurate diagnoses and treatment decisions.
Drug discovery: Adopting generative AI can accelerate the drug discovery process by analysing large datasets of chemical compounds and their properties to generate potential new drug candidates. It can identify novel molecular structures with desired characteristics, reducing the time and cost associated with traditional drug discovery methods. AI-generated molecules can be further optimised through iterative processes, improving their effectiveness and safety. By rapidly generating innovative drug candidates, generative AI has the potential to revolutionise pharmaceutical research and development. This can lead to increased revenue and market share for a healthcare company.
Personalised treatment: Generative AI can be used to develop personalised treatment plans based on an individual’s genetic profile, medical history and other factors. This can lead to improved patient outcomes and satisfaction.
Medical chatbots: Generative AI-enabled medical chatbots can interact with patients to provide information, answer their questions and triage symptoms It can also provide personalised and accurate health information, guidance, and support for users. By understanding natural language patterns, these chatbots can engage in human-like conversations, answering health-related questions and offering tailored advice. Adopting this technology can improve patient education, triage support, adherence to treatments and can also enhance accessibility.
Predictive analytics: Patient data such as medical history, lifestyle and genetic information can be analysed by generative AI models to predict possibility of future occurrence of certain diseases. This can help healthcare professionals in early diagnosis and intervention.

Healthcare

To conclude, one can say that the many benefits of generative AI can help various industries to improve their processes and enhance their efficiency. However, organisations need to be careful about the risks involved with using generative AI such as cybersecurity, biases of training data, and unskilled workforce. Therefore, implementing generative AI will require careful planning to ensure that the use of generative AI is done with quality training data and considering the cybersecurity measures. Organisations also need to invest in upskilling their employees and equip them to work with this new technological tool to harness the optimal benefits of generative AI.

How PwC can help

At PwC, we can help integrate generative AI models that aligns with your organisational priorities and maximise the value generated from the technology. A comprehensive assessment of your organisation's current capabilities, data infrastructure, and business objectives can be conducted to assess how AI can help your firm. We can also design and implement generative AI solutions tailored to your specific requirements. Our team of experts can help in identifying use cases, defining project scope, and developing a roadmap for implementation.

We seek ways to empower organisations to unlock the full potential of generative AI and drive innovation, improve efficiency, and foster competitive advantage. We combine technical expertise with industry insights to deliver tailored solutions that address your specific needs and drive measurable business outcomes.

Contributors Bodhayan Mukhopadhyay, Ishita Agrawal and Rubina Malhotra.