Insights for Organisations

Generative AI for Boosting Creativity in Business

Paul Brown
09.08.23

Purva Raut is an expert on Artificial Intelligence, Machine Learning and Natural Language Processing and the author of five books on AI adoption. She is also our Java Team Lead at the FDM Canada Academy and is currently exploring novel avenues of Generative AI and Prompt Engineering.

As debate around safe and ethical AI use continues to grow, Purva shares her insight on using AI to boost creativity in business.

TL;DR: AI is not a replacement to human creativity!

Generative AI techniques are continually advancing, and researchers are exploring new applications and pushing the boundaries of what can be generated. These techniques offer creative possibilities for content creation, artistic expression, and data augmentation across various domains.

Can it also help us getting creative in business? It can, indeed!

Generative AI can help us right from brainstorming and generating novel business ideas, designing adverts, analysing and visualizing the data to personalising user experience for the service or product provided by your business. It has the potential to significantly boost creativity across various aspects of a business. Style transfer is another powerful technique used in generative AI which can be employed in to enhance visual aesthetics, branding, marketing, and content creation.

By inputting appropriate prompts or parameters, AI can generate novel concepts, designs, or solutions, sparking creativity in the process. Generative AI can assist in creating visually appealing designs, artwork, and multimedia content. It can generate unique and customized graphics, logos, or even entire marketing campaigns. This technology can save time and inspire creative professionals by offering fresh perspectives and alternative options.

“By automatically generating data visualizations, businesses can uncover patterns, trends, and insights that may have been difficult to identify otherwise. This fosters creative thinking and facilitates better decision-making.”

By analyzing user preferences, behaviour, and historical data, AI models can generate tailored recommendations, personalized advertisements, or customized user interfaces. This enables businesses to engage customers in unique and creative ways.

Let’s consider a specific industry application, let’s say Banking and Insurance sectors, and see the different ways generative AI can be used to enhance efficiency, customer experience, and risk management. Some of the prominent applications include:

  1. Fraud Detection: Generative AI can be employed to analyze patterns and detect anomalies in financial transactions, helping to identify potential fraud cases. The AI model learns from historical data and can recognize unusual behaviour or suspicious patterns and alert the relevant authorities or systems.
  2. Risk Assessment: Risk assessment is another area that can benefit from automating high-volume data analysis of customer profiles, credit histories, and market trends. It can generate risk profiles and predict the likelihood of default or insurance claims. This helps financial institutions and insurance companies make informed decisions when approving loans or underwriting policies.
  3. Chatbots and Virtual Assistants: AI-powered Intelligent chatbots or virtual assistants can handle customer inquiries, provide support, or guide customers through various banking or insurance processes. These AI-powered assistants can improve response times, provide personalized recommendations, and enhance the overall customer experience.
  4. Personalized Financial Planning: By analyzing an individual’s financial goals, risk tolerance, and market conditions, AI models can generate customized investment portfolios and provide real-time recommendations. This helps individuals with personalized financial planning and investment advice.
  5. Customer Service and Sentiment Analysis: Generative AI can analyze customer feedback, reviews, and social media posts to gain insights into customer sentiment. This information can be used to improve customer service, identify areas for improvement, and develop tailored marketing campaigns to enhance customer satisfaction.
  6. Claims Processing: In the insurance sector, automating document analysis and verification can streamline claims processing. AI models can extract relevant information from various documents, such as medical records or accident reports, accelerating the claims adjudication process and reducing manual effort.
  7. Anti-Money Laundering (AML): Generative AI can assist in detecting and preventing money laundering activities by analyzing large volumes of financial transaction data. AI models can identify suspicious patterns, flag potentially illicit activities, and help banks comply with regulatory requirements.

Healthcare is another industry that Generative AI has the potential to revolutionize by improving patient care, accelerating medical research, and enhancing diagnostic capabilities..

  1. Medical Imaging Analysis: Generative AI can assist in the analysis of medical images such as X-rays, CT scans, or MRIs. By training on large datasets, AI models can generate automated and accurate diagnoses, identify abnormalities, and assist radiologists in detecting diseases or conditions at an early stage.
  2. Drug Discovery and Development: AI models can generate and analyze molecular structures, simulate drug interactions, and suggest potential drug candidates. This can significantly expedite the drug discovery process and increase the chances of finding effective treatments for various diseases.
  3. Personalized Medicine: Automated analysis of patients’ medical history, genetic data, and other relevant information can enable personalized treatment plans. AI models can generate tailored recommendations for medications, dosages, or treatment options based on an individual’s unique characteristics, improving treatment outcomes and reducing adverse effects.
  4. Disease Prediction and Early Diagnosis: Generative AI can help predict the likelihood of diseases or conditions based on a patient’s medical records, lifestyle factors, and genetic information. By generating risk profiles, AI models can assist in early diagnosis, enabling timely interventions and preventive measures.
  5. Virtual Patient Assistants: AI-powered virtual assistants or chatbots can provide personalized healthcare guidance, answer medical queries, and assist in self-care management. These assistants can offer real-time recommendations, remind patients of medication schedules, or provide support for chronic disease management.
  6. Medical Research and Literature Review: Researchers can use AI to in review and analyze vast amounts of scientific literature. AI models can generate summaries, extract key information, identify trends, or even propose new research directions. This can save time, facilitate knowledge discovery, and enhance the efficiency of medical research.
  7. Mental Health Support: AI-backed virtual agents can engage with mental health patients, provide counselling, or offer coping strategies for anxiety or depression. They can also help in monitoring and early detection of mental health issues.

“Generative AI can boost creativity but surely it is not a replacement for human creativity. Instead, it serves as a powerful tool to augment and inspire human creativity, providing new possibilities and expanding the creative process.”

While generative AI holds significant potential in banking, insurance and healthcare sectors, it should be integrated carefully and ethically. Regulatory compliance, data privacy, security and patient confidentiality should be at the forefront when implementing AI solutions in these sectors to ensure the responsible and secure use of AI.


We at FDM have partnered with Microsoft to organise an event ‘Artificial Intelligence for Real-life Business Challenges’ that brings together industry experts who will share their thoughts on how AI can shape your tech workforce.