Generative AI Examples: How Businesses Are Using AI Across Industries

Sources: National AI Centre and Deloitte Australia.

Deloitte’s latest enterprise AI research found that while 61% of Australian organisations report efficiency improvements from AI, only 30% are using AI to fundamentally transform the way they work. At the same time, 69% are already using autonomous AI agents.

That creates a significant opportunity.

Many businesses have started experimenting with AI. Far fewer have integrated it into the systems and workflows that drive revenue, customer experience, and operational performance. This is where AI implementation, AI integration services, and custom AI business solutions can become important.

The following examples show how businesses are applying generative AI to improve efficiency, automate repetitive tasks, and create better experiences for customers and employees.

1. AI Customer Service Assistants

How many customer questions does your team answer repeatedly?

“Where is my order?”

“What is your return policy?”

“Does this product come with a warranty?”

If your team handles hundreds of similar questions, a generative AI customer service assistant could manage a significant portion of those interactions.

You could connect it with your:

• Product catalogue
• FAQ database
• CRM
• Order management system
• Knowledge base
• Website or mobile application

The system can generate responses using approved business information, while complex or sensitive enquiries can be transferred to a human employee.

What this means for you: Your support team can spend less time answering routine questions and more time handling complex customer needs.

For businesses considering AI chatbot development, customer service is a practical use case because the existing workload is measurable.

2. AI Knowledge Assistants for Your Employees

How long does it take employees to find company policies, product information, technical documents, or internal procedures?

A custom AI knowledge assistant can give employees a conversational interface for accessing approved company information.

For example, an employee could ask: “What documents do I need to submit to onboard a new supplier?”

Instead of searching through folders and internal portals, the AI can retrieve relevant information and present the answer.

A Retrieval-Augmented Generation (RAG) architecture can connect the application with:

• Policies
• Product documentation
• Training material
• Technical documents
• Internal procedures
• FAQs
• Knowledge bases

At Vrinsoft, we develop RAG-based AI solutions that connect generative AI with business knowledge and enterprise systems.

What this means for you: Employees can find the information they need faster without searching across multiple internal sources.

3. Document Processing and Reporting

Contracts, reports, proposals, applications, invoices, meeting notes, and customer documents can all require significant manual review.

A generative AI solution can read incoming documents, extract relevant information, summarise the contents, and produce a structured output.

For example:

Document received → AI extracts information → AI summarises key points → Employee reviews → Information enters your workflow

Applications include:

• Contract summaries
• Business reports
• Proposal preparation
• Meeting summaries
• Customer communications
• Document classification
• Internal reporting

Human review can remain part of the workflow where required.

What this means for you: Your team can spend less time processing documents and more time acting on the information they contain.

4. Personalised Sales and Marketing

Your sales and marketing teams already have valuable customer and product information. The challenge is turning that data into relevant communication at scale.

An AI sales assistant could review CRM records, previous emails, meeting notes, and product information to prepare a customer meeting brief. Afterward, it could help draft a follow-up based on the discussion.

For ecommerce businesses, generative AI can also support:

• Product descriptions
• Product recommendations
• Customer questions
• Personalised emails
• Campaign variations
• Product comparisons

Connecting the AI with your existing systems gives sales and marketing teams assistance within the workflows they already use.

What this means for you: Your team can reduce preparation time while delivering more relevant customer communication.

5. Generative AI for Healthcare

Healthcare organisations face additional considerations around patient information, privacy, accuracy, documentation, and professional oversight.

Potential applications include:

• Documentation assistance
• Information summarisation
• Administrative support
• Report preparation
• Knowledge retrieval
• Patient communication

Australian healthcare organisations are already testing practical AI applications. The NSW Agency for Clinical Innovation, for example, documents the use of generative AI to assist with report and correspondence drafting while clinicians retain review responsibility.

For healthcare businesses, the key development question is: How can AI assist your team while maintaining the controls your organisation requires?

That needs to be addressed during architecture and development.

What this means for you: You can identify useful AI applications while keeping privacy, accuracy, and professional oversight central to the solution.

Also Read: AI Development in Healthcare: Solutions, Use Cases & Cost in Australia

6. Generative AI for Retail and Ecommerce

Managing hundreds or thousands of products can make product content and customer queries difficult to handle manually.

A custom AI application could help customers compare products, answer questions, find relevant items, or receive personalised recommendations.

It could connect with your:

• Ecommerce platform
• Product catalogue
• Inventory system
• Customer data
• CRM
• Website search

This allows the AI to work with your products and business rules rather than provide generic responses.

What this means for you: You can use AI to improve product discovery and customer interactions while supporting internal content and service teams.

7. AI Assistants for Logistics and Operations

Logistics information is often spread across delivery systems, warehouse software, supplier communications, customer records, and operational reports.

An AI operations assistant can bring relevant information together.

A manager could ask: “Which deliveries are delayed today, what is causing the delays, and which customers are affected?”

The AI could retrieve information from connected systems and provide a concise response.

Potential applications include:

• Shipment information
• Operational reporting
• Supplier communication
• Document processing
• Internal knowledge
• Workflow assistance

What this means for you: Managers can get relevant operational information faster and spend less time pulling data from multiple systems.

Also Read: AI in Logistics: From Inventory Forecasting to Delivery Optimization

1. Business Data

Your AI needs access to the right information to deliver useful results.

This may include:

• Product and customer data
• Internal documents and policies
• Transaction records
• Operational data
• Knowledge bases

You also need to determine where the data is stored and how it can be accessed securely.

2. The Right AI Model

Model selection should match your generative AI application’s requirements.

Response quality, speed, cost, context, data requirements, and functionality can all influence which AI model you use. A customer service assistant may have very different requirements from an enterprise AI solution.

3. RAG and Knowledge Architecture

If your AI needs to work with private business information, Retrieval-Augmented Generation (RAG) can connect the model with your approved data sources.

The application retrieves relevant information before generating a response, helping your custom AI solution provide answers based on business-specific knowledge.

4. APIs and System Integrations

Your generative AI application may need to connect with:

• CRM and ERP systems
• Ecommerce platforms
• Databases
• Cloud services
• Websites and mobile apps

AI integration services can connect these systems so your AI works within existing business workflows.

5. Security and Access Controls

AI applications may handle sensitive business information. Authentication, permissions, data handling, and monitoring should therefore be part of the AI development architecture.

Users should only be able to access information they are authorised to see.

6. Testing and Monitoring

Before launch, test your generative AI solution against real business scenarios.

Check response accuracy, handling of unknown questions, human handover, sensitive data handling, and ongoing performance.

These steps help turn an AI experiment into a reliable custom AI development project that can support your business at scale.

These are indicative market ranges, and actual costs depend on project scope and solution complexity.

• Factors that influence cost include:
• Data readiness
• System integrations
• AI architecture
• User volume
• Security requirements
• Cloud infrastructure
• Ongoing optimisation

A customer-facing AI chatbot will naturally require a different investment from a fully integrated enterprise AI platform connected to multiple business systems.

Also Read: AI Development Cost from Planning to Implementation: What to Expect in 2026

What are the most common generative AI examples in business?

The most common examples include AI customer service assistants, AI knowledge assistants, document processing systems, AI sales copilots, retail recommendation engines, healthcare documentation support, and operational AI assistants.

How are Australian businesses using generative AI?

Australian businesses are using AI for content generation, data analytics, customer service, reporting, operational support, and workflow automation. The National AI Centre reports that 43% of Australian SMEs have adopted AI, with content generation and analytics leading adoption.

What is the difference between AI tools and custom AI development?

AI tools provide general functionality, while custom AI development creates solutions designed around your business data, workflows, users, and systems.

Can generative AI integrate with CRM and ERP systems?

Yes. Custom AI solutions can integrate with CRM platforms, ERP systems, ecommerce software, databases, cloud environments, websites, and mobile applications.

How much does generative AI development cost in Australia?

A typical AI MVP can range from $30,000 to $60,000, while business applications often range from $60,000 to $120,000. Enterprise AI platforms can exceed $300,000 depending on complexity.