Generative AI amplifies tech investments, including AI models. It is here to stay, and will aid businesses perform better as it continues to evolve. Almost every industry can benefit from it.
Today, every IT vendor is promoting generative AI. In current seminars and conferences on IT technology, leaders invariably talk of ‘generative AI’. McKinsey defines generative AI as algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations and videos. Generative AI is primed to make a significant impact on enterprises over the next five years.
Key characteristics of generative AI technology
Generative AI comprises models that describe our requirements, visualising and generating content to match prompts. It expedites ideation, brings vision to life and allows more time for creativity. It can create a wide variety of data such as images, videos, audio, text and 3D models, often employing large language models, like ChatGPT.
The following are the key characteristics of generative AI technology.
- Text management: Generative AI can complete a given text coherently, translate languages, summarise text concisely, and generate text that mimics human writing.
- Contextual understanding: Generative AI has a strong ability to understand the context.
- Natural language processing (NLP): It can perform various NLP tasks, processing human language and allowing users to ask questions conversationally.
- Answering questions: Generative AI can answer questions based on a knowledge base.
- Personalisation: It can be fine-tuned for specific use cases.
- Multi-language support: Generative AI can handle NLP tasks in multiple languages.
- Advanced semantic search: It enables semantic search in large volumes of structured and unstructured data, including databases, documents, etc. It also supports auto data indexing using custom AI/ML based data processing for efficient embedding.
- Content moderation: It has smart content filtering and moderation engines for query and response, trainable on enterprise-specific data.
- Integration: It can integrate with various applications and data sources within an organisation, including CRM, ERP, and other proprietary systems, to access and analyse data via APIs.
- Role-based access control: The system can be configured with role-based access control, ensuring that users only have authorised access to data.
- Software coding: Generative AI can generate, translate, explain and verify code.
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Real-world use cases of generative AI
Generative AI’s use cases are vast and continually evolving. Businesses across industries are experimenting with different ways to incorporate generative AI to enhance efficiency and decision-making capabilities. Generative AI applications improve experiences, reduce costs and increase revenues for enterprises.
Here is a summary of the generative AI use cases across various industries.
Healthcare and pharma
Generative AI-based applications help healthcare professionals be more productive by identifying potential issues upfront, providing insights to deliver interconnected healthcare and improve the health of
patients. It helps in:
Better customer experience: Automates administrative tasks such as processing claims, scheduling appointments, and managing medical records.
Patient health summary: Helps provide healthcare decision support by generating personalised patient health summaries, speeding up patient response times, and improving the patient experience.
Faster analysis of publications: Generative AI helps in reducing the time it takes to create research publications on specific drugs by analysing vast amounts of data from multiple sources faster than ever.
Personalised medicine: Generative AI-based individualised treatment plans are tailored to a patient’s genetic makeup, medical history, life style, etc.
Healthcare virtual assistant: It provides end users with conversational and engaging access to the most relevant and accurate healthcare services and information.
Manufacturing
Generative AI enables manufacturers to make better use of their data, leading to advancements in predictive maintenance and demand forecasting. It also helps in simulating manufacturing quality, improving production speed, and material efficiency.
Predictive maintenance: It helps in estimating the life of machines and their components. Generative AI can provide proactive information to technicians about repairs and replacement of parts and machines, thus reducing downtime.
Performance efficiency: Anticipating the problems proactively can cover production disruptions, bottlenecks, and safety risks in real time.
Other uses in the manufacturing industry include:
- Yield, energy and throughput optimisation
- Digital simulations
- Sales and demand forecasting
- Logistics network optimisation
Retail
Generative AI in retail helps in personalising offerings, brand management, as well as optimising marketing and sales activities.
Personalised offerings: It enables retailers to deliver customised experiences, offerings and pricing, and plan according to customer demand, modernising the online and physical buying experience.
Dynamic pricing and planning: It helps predict demand for different products, enabling more confident pricing and stocking decisions.
Other uses include:
- Campaign management
- Content management
- Augmented customer support
- Search engine optimisation
Banking
Generative AI applications help in delivering a personalised banking experience to customers. There are other benefits too.
Risk mitigation and portfolio optimisation: Generative AI helps banks to build data foundations for developing risk models, and identify events that are impacting the bank. It can help mitigate risks to optimise portfolios.
Customer pattern analysis: Generative AI can analyse patterns in banking data at scale, helping relationship managers and customer representatives to identify customer preferences, anticipate needs, and create personalised banking experiences.
Customer financial planning: Generative AI can be used to automate customer service, identify trends in customer behaviour, and predict customer needs and preferences. This helps to understand the customer better and provide personalised advice.
Generative AI also helps with:
- Anti-money laundering regulations
- Compliance
- Financial simulations
- Applicant simulations
- Risk analytics
- Fraud prevention
Insurance
Generative AI’s capability of analysing and processing large amounts of data helps in accurate risk assessments and effective claims processes.
Customer support: Generative AI can provide multilingual customer service by translating customer queries and responding to them in the preferred language.
Policy management: It analyses large amounts of unstructured data related to customer policies, various policy documents, customer feedback, and social media literature to implement better policy management.
Claims management: Generative AI helps in analysing various claims artefacts to enhance the overall efficiency and effectiveness of claims management.
It also helps with:
- Customer communications
- Coverage explanations
- Cross-selling and upselling of products
- Acceleration of the product development life cycle
- Innovation of products
Education
Generative AI helps to connect teachers and students. It enables collaboration between teachers, administrators and technology innovators to provide better education.
Student enablement: Generative AI helps students with real-time lesson translations in different languages. It helps visually impaired students with classroom accessibility.
Student success: Deep analytic insights into student success help teachers make informed decisions on how to improve performances.
Telecommunications
Generative AI adoption by the telecom industry improves operational efficiency and network performance. Here, generative AI can be used to:
- Analyse customer purchasing patterns
- Give personalised service recommendations
- Enhance sales
- Manage customer loyalty
- Get insights into customer preferences
- Provide better data and network security, enhancing fraud detection
Public sector
The goal of digital government initiatives is to establish a connected government and provide better citizen services.
Smart cities: Generative AI helps in toll management, traffic optimisation, and sustainability.
Better citizen services: It provides citizens with easier access to connected government services through tracking, search, and conversational bots.
It also enables:
- Service operations optimisation
- Contact centre automation
Benefits of generative AI
The many benefits of generative AI include:
- Increased productivity
- Better content creation
- Personalised customer experiences
- Identification of new customer journeys and audiences
- Improved customer interactions through enhanced chat and search experiences
- Enhanced creativity and data exploration
- Exploration of large amounts of unstructured data through conversational interfaces and summarisations
- Transformation of campaigns, audiences, experiences, and insights
There is a significant gap between what generative AI can do and what enterprises want it to do. From assessing business needs to building generative AI solutions, it is necessary to make the right choice for enterprise business use cases.
However, generative AI is here to stay and will continue to improve in future versions. Unlike previous technologies, AI can make increasingly complex decisions enabling new business opportunities. The use of generative AI across enterprises is becoming more and more widespread, possibly even trending towards industrialisation.
Generative AI is an opportunity and not the competition. It won’t replace humans but will assist in the business success of the next generation.
