Generative AI is software that produces new content, which can be text, code, images, audio, or video, from a prompt fed to a trained model. This guide is a collection of generative AI examples that show how companies can apply this technology to marketing, customer service, sales, software development, enterprise data and business operations. More importantly, these generative AI use cases illustrate how model capabilities translate into working business processes.
What is generative AI used for in business?
Generative AI is a type of artificial intelligence that produces new content or responses based on patterns learned during training and the context supplied at runtime. A large language model (LLM), for example, can draft a document, explain a technical issue or turn a natural-language question into a database query.
For a business, the interesting part, more than the generated content itself, is what the generated output allows an employee, customer or software system to do next.
Some generative AI use cases need only a model and a carefully designed prompt. Others need retrieval-augmented generation (RAG), which is a capability that allows the model to retrieve relevant information from company documents before it generates a response.
The following 20 generative AI use cases illustrate how businesses are using generative AI.
Also read: AI in E-Commerce: Transforming Online Shopping
20 generative AI examples for business
Examples of generative AI for marketing and content
1. WPP — UK-based global advertising and communications group
- The job: Produce ad copy, visuals, and campaign variations at scale.
- Output type: Text / image.
- How it works: WPP's Production Studio combines generative AI models with creative production workflows, including technology developed with NVIDIA and Hogarth.
- Result: Turnaround from days to minutes and over 50% more content per hour for clients like Ford and L'Oréal (vendor-reported).
The value in this generative AI use case is the pipeline around the model. That surrounding system of brand rules, approvals, and delivery is where most real advantage of generative AI for marketing is centered.
2. Monks — US-based digital marketing and technology agency
- The job: Generate personalised campaign creative for each audience segment.
- Output type: Text / image.
- How it works: Monks uses Google Gemini to synthesise dynamic messaging and creative variations, then connects that output directly to the ad-delivery systems that serve and measure it.
- Result: 80% higher click-through, 31% lower cost-per-purchase, and a 97% cut in production cost for client Hatch.
Personalisation at this scale only pays off because the creative is tied back to delivery and measurement. Generation is the easy half. The feedback loop that learns what works is the valuable half.
3. Warner Bros. Discovery — US-based media and entertainment conglomerate
- The job: Caption and subtitle a large media library.
- Output type: Text.
- How it works: A custom captioning tool on Google Cloud's Vertex AI runs inside the existing media and storage pipelines, so files are captioned where they are already stored rather than in a separate tool.
- Result: 50% lower captioning cost and 80% less time per file (vendor-reported).
This generative AI use case shows that a narrow, high-volume task with an obvious accuracy check is ideal as a first generative AI project for any enterprise. The integration of the captioning tool into the storage pipeline is what turned a capability into actual throughput for Warner Bros. Discovery.
Examples of generative AI in customer service and support
4. Fortune 500 software firm — US-based business-software provider (Brynjolfsson study)
- The job: Give support agents real-time answer suggestions on live chats.
- Output type: Text / summary.
- How it works: GPT-3 was fine-tuned on the company's own past chat transcripts, deliberately up-weighted toward how top performers resolve issues. The tool generated suggestions for agents, right inside the live chat tool they used.
- Result: 15% more resolved chats per hour overall, rising to 35% for newer agents (peer-reviewed study).
The newest agents were able to benefit the most from the support agent. That happened because the model learned from how the company’s top performers already resolved issues, so it passed that experience on to everyone else. A standard model on its own would not have carried that advantage.
5. RIS d.o.o. — Croatia-based enterprise and ERP software provider
- The job: Classify incoming support emails, retrieve relevant information and draft responses.
- Output type: Text / summary.
- How it works: LLLMs work with RAG over internal FAQs and support information, with a Streamlit interface providing access to the workflow.
- Result: 50–60% reduction in manual support workload and a 70% improvement in response times
This is a useful example of a generative AI use case based on bounded automation. The model can prepare a response using company knowledge, while an employee can review exceptions or uncertain answers before sending them.
6. Telecom Italia (TIM) — Italy-based national telecommunications provider
- The job: Respond to routine inbound customer enquiries through conversational voice assistance.
- Output type: Audio / text / summary.
- How it works: Google-powered voice AI is integrated into the call-centre telephony and routing stack, so the agent can understand speech, respond in voice, and hand off to a human when needed.
- Result: 20% increase in call-centre operational efficiency.
7. Discover Financial — US-based digital banking and payments company
- The job: Help contact-centre representatives find relevant internal policies and procedures while speaking with customers.
- Output type: Text / summary.
- How it works: A conversational LLM search layer is connected directly to internal policy repositories, so representatives get a synthesized answer from approved sources instead of having to dig through documents while being on a call.
- Result: 10,000 agents answering complex queries accurately in real time).
In regulated customer service, finding the right policy matters as much as producing fluent language. The implementation should make it possible to check the source of an answer and ensure that outdated or unauthorised material is not treated as current policy.
Also read: 15 Agentic AI Examples Across Industries
Examples of generative AI in sales
8. Enpal — Germany-based residential solar installation company
- The job: Generate sales quotes, roof assessments, and panel-layout plans.
- Output type: Text / image.
- How it works: Built with dida on Google Cloud, the system pairs generative models with image-based evaluation of the customer's actual roof, then produces a tailored quote from that assessment.
- Result: Quote preparation cut from 120 minutes to 15 minutes.
The value in this use case came from combining vision and generation against a real physical constraint, that is, the roof. .
9. Best Buy — US-based consumer electronics retailer
- The job: Summarise support calls during and after the conversation.
- Output type: Summary.
- How it works: LLM-based Contact Center AI runs inside the agent desktop and writes the call notes automatically, leaving the agent to confirm rather than type.
- Result: 30–90 seconds saved per call on handling and after-call notes.
Small per-call savings compound quickly at retail volume. Summarisation is low-risk precisely because a human can see and edit the note before it's filed.
Also read: AI in Banking: Where It Pays Off
Examples of generative AI in software development and IT
10. Suzano — Brazil-based global pulp and paper manufacturer
- The job: Turn plain-language questions into SAP data queries.
- Output type: Code.
- How it works: A Gemini-based agent, built with Sauter, converts an employee's natural-language question into BigQuery SQL and runs it against SAP materials data (no SQL knowledge required of the user).
- Result: 95% reduction in query time across 50,000 employees.
11. UK-based financial services firm — software engineering division
- The job: Analyse requirements, write test cases, and convert code.
- Output type: Code / text.
- How it works: A multi-agent framework runs distinct LLM personas (designer, developer, tester) that critique and refine each other's output inside a private cloud environment.
- Result: Requirements and testing phases over 50% faster, with accuracy above 95%.
Because one agent reviews another’s work, the system catches mistakes a single pass would let through. This is also where generative AI and IT outsourcing meet. The real question is whether you build this kind of framework in-house or bring in a partner who has already run one.
12. FinQuery — US-based financial technology and accounting company
- The job: Draft emails, plan projects, and debug code across engineering work.
- Output type: Code / text / summary.
- How it works: Gemini is embedded across Google Workspace in the team's daily engineering and operational workflows. Employees use generative AI to draft or refine communications, organise project information and assist with debugging. For code-related tasks, the model can explain errors or suggest changes, but a developer still needs to run tests and check the proposed fix in context.
- Result: Around 20% faster drafting, smoother cross-team planning, and quicker debugging.
General-purpose AI tools can create value across many small tasks, not just one major automation project. However, organisations should define which data employees may enter, and they should assess quality as well as time saved.
Examples of generative AI in operations, risk, and safety
13. AES Corporation — US-based global energy and utility provider
- The job: Run energy safety audits across facilities.
- Output type: Text / summary.
- How it works: Generative agents on Vertex AI, using Anthropic's Claude models, are built into the facility safety workflows so an audit can be run as a guided, consistent process rather than a manual one.
- Result: Audit time from 14 days to one hour, costs down 99%, accuracy up 10–20%.
14. Nolinor Aviation — Canada-based specialised charter airline
- The job: Process and classify employee safety investigations.
- Output type: Text / summary.
- How it works: Built with Mila, the system does its work in two stages — an LLM first structures each report against Nolinor's risk matrix, then an agent pulls supporting context from flight data and manuals, with a human reviewing throughout.
- Result: Effort per investigation cut from 40 hours to 5, later spun out as its own product.
The order matters here. The system first organises the report against the risk matrix, and only then pulls in supporting context. A person checks the work throughout, and in a safety-critical setting, where a wrong call has real consequences, that human review is what makes the result defensible.
15. Drax Group — UK-based renewable energy generator
- The job: Catch equipment faults before they cause failures.
- Output type: Text / summary.
- How it works: Predictive and generative diagnostics, developed with Aveva, run over live sensor feeds across Drax's power plants, and flags anomalies as the data comes in.
- Result: Identified a critical anomaly in time to avoid a four-week unplanned shutdown.
A single shutdown avoided can pay for the whole programme. What makes early warning possible is that the model reads live sensor data as it arrives, rather than old records after the fact.
16. Five Sigma — insurance-technology and claims-automation provider
- The job: Extract claims data, classify documents, and assist adjusters.
- Output type: Text / summary.
- How it works: A generative engine inside the claims workflow reads varied claim documents, pulls out the structured data, and drafts the next handling steps for the adjuster to confirm.
- Result: 80% fewer processing errors and 25% higher adjuster productivity.
Normally, cutting errors and working faster pull in opposite directions. Pulling clean, structured data out of each document is how the firm improved both at once. The hard part is that claim documents arrive in so many different formats.
Examples of generative AI in finance, legal, and compliance
17. Eventus Advisory Group — US-based fractional CFO and advisory firm
- The job: Draft technical accounting memos.
- Output type: Text.
- How it works: An LLM runs with RAG over the firm's own library of past memos held in a vector database, is fine-tuned on hundreds of domain examples to curb hallucination, and routes every draft through a human reviewer.
- Result: Drafting time from 4 hours to 30 minutes, review included (vendor-reported).
This is the standard setup for text that has to be accurate and compliant. Retrieval keeps each draft tied to the firm’s real memos, fine-tuning shapes the style, and a person signs off at the end. The model itself is the smallest part of that chain.
18. Fluna — Brazil-based digital financial and legal services firm
- The job: Extract data, check compliance, and draft contracts.
- Output type: Text / summary.
- How it works: Vertex AI, Document AI, and Gemini 1.5 Pro work together over private APIs to read unstructured contract files and generate the corresponding legal documents.
- Result: 92% data-extraction accuracy with faster contract processing (vendor-reported).
With legal documents, getting the extracted data right is almost the entire point. Running the system over private APIs keeps sensitive contracts inside the company’s own boundary, which is essential when the material is this confidential.
19. UK-based financial services firm — customer due diligence (case study)
- The job: Automate document extraction and identity checks for KYC.
- Output type: Text / summary.
- How it works: A mix of small, medium, and large LLMs runs with RAG over unstructured customer documents, backed by human-in-the-loop verification at the end.
- Result: 98% field accuracy, which retired the redundant dual-human "four-eye" check (case study).
Rather than reaching for the biggest model by default, they used small, medium, and large models to fit each task. It looks like a minor choice, but across millions of checks, those savings in cost and speed add up.
20. Thomson Reuters — Canada-based global legal and information services firm
- The job: Analyse long legal and regulatory documents.
- Output type: Text / summary.
- How it works: Gemini Pro's two-million-token context window processes long documents in-context, within Thomson Reuters' approved internal suite of LLMs.
- Result: Complex document work up to 10× faster (vendor-reported).
A large context window lets the model read a long document in one pass, which can replace some retrieval setup. It does not suit every case, though, and it is not cheap. Choosing between a wide context window and RAG is a genuine design decision, not something to settle by default.
Summary of all 20 examples
AI Use Cases Across Different Industries
The table highlights 20 examples of how companies are using AI for marketing, customer support, sales, software development, operations, and finance/legal functions.
1. WPP
- Reported result: Days reduced to minutes; 50%+ content/hour.
2. Monks
- Reported result: 80%+ CTR and 97% production cost reduction.
3. Warner Bros. Discovery
- Reported result: 50% cost reduction and 80% time savings.
4. Fortune 500 Software Firm
- Reported result: 15% chat/hour improvement (with 35% for new agents).
5. RIS d.o.o.
- Reported result: 50–60% workload and 24/7 coverage.
6. Telecom Italia
- Reported result: 20% improvement in efficiency.
7. Discover Financial
- Reported result: 10,000 agents and an AI-first approach.
8. Enpal
- Reported result: Quotes reduced from 120 seconds to 15 seconds.
9. Best Buy
- Reported result: 30–90 sec saved/call
10. Suzano
- Reported result: −95% query time, 50k staff
11. UK FS firm (SDLC)
- Reported result: +50% faster, >95% accuracy
12. FinQuery
- Reported result: 20% faster drafting.
13. AES Corporation
- Reported result: 14 days reduced to 1 hour and 99% cost reduction.
14. Norfolk Aviation
- Reported result: 40 hours to 5 hours saved per investigation.
15. Drax Group
- Reported result: Avoided a 4-week shutdown.
16. Five Sigma
- Reported result: 80% error reduction and 25% productivity improvement.
17. Eventus Advisory
- Reported result: 4–5 hours reduced to 20 minutes.
18. Fluna
- Reported result: 92% extraction accuracy.
19. UK FS firm (KYC)
- Reported result: 98% field accuracy.
20. Thomson Reuters
- Reported result: Up to 10x faster processing.
How do you choose the right first generative AI project?
The mistake I see most often is this: a company buys a tool first, then goes looking for a problem to use it on. Do it the other way around. Start with the work. Three questions will take you most of the way.
1. Which workflow actually causes pain?
Pick one specific task that comes up again and again. Avoid a broad goal like “transform the department.” The best first tasks are narrow and clearly defined, such as summarising a document, pulling data out of a file, writing a first draft. If you can’t describe the task in a single sentence, it’s too big to start with.
2. Is it high-value but low-risk?
You want three things to be true at the same time:
- a person can quickly check whether the output is right,
- there’s a clear way to measure the value in terms of time saved, faster turnaround, or better quality,
- and someone owns the task and is accountable for making it work.
If a mistake is easy to spot and cheap to fix, you can move quickly. If a wrong answer could reach a customer without anyone noticing, slow down.
3. Can you actually build it on your own data?
Is your internal data easy to reach and clean enough to use? Can the system connect to the tools and records you already run on? The same care that goes into best practices for integrating generative AI with a dealership DMS (mapping to a live system carefully and respecting its limits) applies to any ERP, CRM, or claims platform you connect to.
Then there’s the build-or-partner decision. The choice between generative AI and IT outsourcing and building in-house usually comes down to one thing: have you built a system before that retrieves from your own data and keeps a human in the loop? If you haven’t, a partner who has will save you a lot of time.
Either way, run it as a contained three-to-six-month pilot, with a person checking the outputs before any full rollout. Prove the number first, then scale.
Choosing the project carefully only helps if you also plan for what can go wrong, so let’s be clear about the risks.
What are the risks of generative AI at work?
These risks are manageable, but only if you plan for them from the start.
Key AI Risks and the Controls That Help Manage Them
The table highlights four major risks associated with AI adoption and the practical controls businesses can implement to reduce them.
1. Accuracy (Hallucination)
- What it looks like: An AI model states something false with full confidence, potentially leading to poor decisions or business liability.
- Control that works:
- Ground AI outputs in your own data using Retrieval-Augmented Generation (RAG).
- Ensure human reviewers check outputs before anything is published or deployed.
2. IP and Copyright
- What it looks like: AI-generated output trained on scraped content may reproduce material that the business does not have the rights to use.
- Control that works:
- Review AI-generated assets before publication.
- Prefer AI models with clear training and indemnity terms.
3. Privacy and "Shadow AI"
- What it looks like: Employees paste contracts, customer Personally Identifiable Information (PII), or source code into public AI tools without proper oversight.
- Control that works:
- Provide employees with an approved, private AI environment.
- Restrict the use of public AI products for sensitive business data.
4. Security
- What it looks like: Prompt injection manipulates AI models, while attackers may use AI for phishing or deepfakes.
- Control that works:
- Treat all inputs as untrusted.
- Restrict what AI models can access.
- Maintain a private security boundary around sensitive systems.
Two more are worth watching.
- The first is bias, where old patterns in the training data skew decisions in areas like hiring or credit.
- The second is overreliance, where people stop checking the output and trust it blindly. You handle both the same way; keep a person in the loop who is expected to question the model, not just rubber-stamp it.
If there’s one thing to take from these twenty generative AI use cases, it’s that the companies that succeeded picked a single workflow, grounded the model in their own data, kept a human in the loop, and measured the result. Then they scaled what worked.
That’s the whole method. Choose one task where you can prove a number within a quarter, and let that result pay for the next step. If you’d like a partner to scope that first project with you, that’s exactly the work we do at our industry AI solutions and custom AI development practices.
