Generative AI is useful for more things than generating funny images and creating helpful chatbots.
Teams are using generative AI to draft proposals, summarize meetings, create training content, follow up on leads, and automate repetitive tasks — and it can all be embedded in business workflows.
In this guide, we'll walk through:
- What generative AI means
- 4 types of generative AI
- 28 practical, industry-specific examples
- How companies are applying generative AI to improve their workflows
What Is Generative AI?
Generative AI is a type of artificial intelligence that creates new content — such as text, images, music, audio, or videos — based on patterns learned from existing data. It uses machine learning models, often large ones, that are pre-trained on vast datasets.
Instead of just identifying patterns or making predictions, generative AI actively produces new versions or variations of the data it has been trained on.
Most people first hear about generative AI through tools like ChatGPT or Midjourney. But the technology powering them goes deeper than generating clever text responses or trippy images.
Traditional AI systems can classify, sort, or predict outcomes based on input data. Generative AI is different. It creates something new: a blog post, code snippet, voice track, or sales proposal. This core difference makes it worthwhile across business functions, not just technical ones.
The main types of generative AI
Generative AI can create different things. The most common categories of generative AI fall into four broad buckets:
- Text generation: Tools like GPT-4, Claude, and open-source LLMs are used for writing articles, summarizing transcripts, crafting emails, and more. You'll find this in everything from marketing copy to CRM updates.
- Image generation: Platforms like Midjourney and DALL·E generate visuals based on prompts. Creative teams use these to brainstorm ad visuals, product concepts, and brand assets without a designer on standby.
- Audio generation: Tools like ElevenLabs or Descript can clone voices, clean up recordings, or generate lifelike audio from scratch. Some sales teams use them to pre-record responses for prospecting.
- Code generation: GitHub Copilot is the big name here. It helps developers draft or complete code inside their IDEs. It's also used for QA automation and scripting internal tools.
In 2025, generative AI is shifting from standalone tools to becoming embedded into everyday systems, helping teams automate both content creation and execution within the platforms they already use.
So, how are businesses using generative AI? Let’s check some real-world examples next.
28 real-world generative AI examples in 2025
There are tons of fantastical ideas or half-baked product demos using generative AI. But, we won’t be discussing those. We’ll focus on what industries use AI and what teams are doing with it –– working examples used daily across industries like healthcare, sales, education, ops, and media.
We've organized the list by industries to show how gen AI applications adapt to real-world problems in different contexts.
Let's start with healthcare, one of the most widely adopted sectors:
Healthcare & life sciences
For healthcare professionals, time is always short, and the paperwork never ends. Teams use generative AI to reduce administrative load, assist with diagnostics, and accelerate research without interfering with patient care.
This is one of the clearest examples of generative AI business applications that make a direct impact: less time on documentation, more time with patients. Here are some healthcare-specific use cases:
- AI-generated clinical notes: Tools like Suki and Nuance DAX generate SOAP (Subjective, Objective, Assessment, and Plan) notes automatically by listening to patient conversations and summarizing them into structured medical records. Some clinics report saving hours of charting daily, and the AI doesn't get tired or skip details. AI medical charting has moved past the pilot stage and is now a part of how providers work.
- Radiology report automation: Companies like RadAI and Aidoc use generative AI to analyze scans and generate structured insights that radiologists can review to finalize the reports faster. While not pure generative AI, they include intelligent automation to make radiology reporting faster and more accurate.
- Simulated clinical trials: Startups like Unlearn.AI build digital twins of patients and use generative models to simulate clinical trial outcomes. This reduces the need for control groups, shortens trial timelines, and brings new treatments to market faster.
- Mental health documentation support: Therapists use AI scribes like DeepScribe to record and summarize counseling sessions. The AI handles note-taking and transcription, so the clinician can stay fully engaged with the patient — no more scribbling between sessions.
- Drug discovery acceleration: Companies like Insilico Medicine use gen AI and advanced machine learning to design and evaluate new molecular compounds in silico. This accelerates the initial process of drug discovery and reduces the costs of lab testing.
Across the board, using generative AI in healthcare improves efficiency and directly impacts outcomes. Unlike other tech trends, this one's sticking because it solves real, repeatable problems.
Marketing & sales
Marketing teams were some of the first to adopt generative AI, and now sales teams are catching up fast. From cold outreach to content production, Gen AI is handling repetitive tasks, speeding up workflows, and helping teams scale what used to be manual, low-leverage work.
Let’s look at some real examples from sales and marketing:
- Personalized email sequences: AI tools like Lavender and Smartwriter help teams write hyper-relevant cold emails based on a prospect's website, job title, or LinkedIn activity. Some go a step further. Tools like Lindy can build complete sequences — including follow-ups — triggered by prospect behavior or CRM stage. It's not just writing emails, it's managing the follow-up engine.
- Ad creative generation at scale: Platforms like AdCreative.ai use generative models to generate dozens of ad variations based on a few inputs: headline, product, and target audience. Copy and design variants are A/B tested automatically, giving teams higher-performing creatives without more time in Figma.
- SEO content and blog writing: Marketers pair SurferSEO with tools like Jasper or Copy.ai to generate long-form content optimized from day one. These setups can handle briefs, outlines, and complete drafts, freeing writers to focus on editing and strategy.
- AI avatars for sales demos: Instead of waiting for the perfect spokesperson, companies use Synthesia to create demo videos with AI avatars that can speak multiple languages and update scripts on the fly. This process is faster, cheaper, and more scalable than traditional production.
- Lead prospecting with ChatGPT plugins: Sales reps use ChatGPT plugins for tools like Apollo, HubSpot, and even LinkedIn to research prospects and prep personalized outreach in minutes, without switching tabs a dozen times.
- Automated lead scoring + CRM routing: Some teams now use AI agents to evaluate lead activity, score them based on interest or fit, and assign the right rep automatically. No more spreadsheet juggling or manual triage.
In this space, generative AI is augmenting what sales and marketing teams already do. The playbooks are the same. What's changed is the speed, scale, and depth with which you can execute them.
Education & training
In education, generative AI is helping teachers, lecturers, and professors better utilize their time. Whether you're building lesson plans or running a corporate onboarding program, these tools make it easier to create, personalize, and deliver content without starting from scratch.
Here’s how generative AI is transforming education and training workflows:
- Lesson plan and quiz generation: Teachers use tools like Quizizz AI, Khanmigo, and custom GPTs to generate lesson plans, quizzes, and comprehension questions in seconds. It's not about outsourcing teaching — it's about getting the groundwork done faster, so teachers can focus on delivery and discussion.
- AI tutoring bots: Interactive agents trained on course materials can now walk students through problems step-by-step. Students can now get 24/7 assistance without always needing a live tutor.
- Course content creation with video: Platforms like Synthesia allow educators and L&D teams to create training videos using AI avatars — no cameras, no scriptwriters. This is especially useful for updating onboarding content or localizing training across languages without re-recording everything.
- Writing support and grammar coaching: Tools like Grammarly go beyond correcting typos. They now offer tone rewrites, clarity suggestions, and structural edits, acting like virtual writing coaches. This is especially helpful for non-native speakers or students in writing-heavy programs.
- Class feedback summaries: Using AI tools like Otter.ai or Lindy, instructors can now turn live classroom conversations or training sessions into actionable summaries. These insights help track participation, identify areas of confusion, and improve future sessions.
These next-generation AI applications help teams spend less time prepping materials and more time teaching. The path to personalized, on-demand support is now much shorter for learners.
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Business operations & productivity
Generative AI is a necessity for business operations and productivity teams. They use it to make internal processes more efficient, cut through backlogs, and handle the tedious, repetitive work that usually gets pushed to the bottom of the to-do list.
Here’s how operations and productivity teams are putting generative AI to work:
- Meeting note automation and follow-ups: Summarizing meetings is one of the most widely adopted gen AI applications in business. Tools like Fireflies, Otter, and Lindy transcribe the conversation, extract action items, identify next steps, and send summaries to Slack or email.
- Internal knowledge base generation: Teams are training AI agents to read through Notion pages, SOPs, and documentation to build searchable internal wikis. That means new team members can ask questions like "How do we submit an expense?" and get a clear answer, without pinging someone on Ops.
- Smart email triage and responses: Generative AI isn't just replying to emails. Tools like Superhuman AI and Lindy Mail prioritize messages, suggest replies, and even auto-respond based on rules, like sending a polite "no" to cold outreach or escalating urgent tickets to the right person.
- Proposal and deck creation: Sales and strategy teams use platforms like Beautiful.ai or Lindy's proposal drafter to generate branded decks and proposals from templates, auto-filling names, logos, pricing, and other inputs.
- Calendar and task management automation: Teams increasingly use AI assistants to schedule meetings, assign tasks, and send reminders — all based on context from emails or meetings. With workflow builders like Lindy, you can set up custom triggers and conditions. For example, if the prospect doesn’t reply in 3 days → send follow-up.
- Voice-to-CRM automation: After a call, sales reps record a 30-second voice note. An AI agent transcribes it, updates the CRM, creates tasks, and shares a summary with the team. It doesn’t need manual notes and avoids pipeline gaps.
These capabilities are becoming bare-minimum for fast-moving teams. Using generative AI for business operations means fewer delays, better documentation, and less time spent chasing small stuff.
Design, media, and entertainment
This is the domain where generative AI first turned heads, but now the hype has settled into actual workflows. Prompt-based art tools have become seriously capable for creative teams across design, video, gaming, and content ops.
Let’s dive into how creative teams are using generative AI today:
- Brand concept development with image generation: Designers use tools like Midjourney and DALL·E 3 to mock up logo directions, ad visuals, and brand motifs based on creative briefs. These tools do not replace designers — they help them skip the blank canvas phase.
- Ad creative and campaign visuals: Creative teams use Ideogram because it generates usable marketing graphics, including text. Unlike older models, it reliably produces visuals with clean call-to-action text, making it viable for campaign work.
- AI video editing for social teams: Tools like Runway, Pictory, and Descript let video editors use natural language to trim footage, apply effects, or reformat content for different platforms. This reduces multi-format editing from hours to minutes.
- 3D game environments and character design: Game development studios use generative tools like Scenario.gg to quickly create 3D assets, characters, and environment textures. Instead of starting from scratch, designers work from AI-assisted first drafts.
- Script writing and voice cloning for podcasts: With tools like Lindy's podcast-to-blog agent and ElevenLabs, teams can script shows, generate realistic narration, and repurpose audio content into newsletters or articles. It's now easy for a team to convert a podcast episode into five new pieces of content without a human editor touching a thing.
- Music generation for commercial use: AI music tools like Suno or Udio can now produce ad-ready soundtracks from text prompts. Creative teams use these to fill in background scores for internal videos, product demos, or even full-scale campaigns, without licensing hassles.
Generative AI in creative fields is an accelerator — helping small teams punch above their weight, and helping larger teams move faster without cutting corners.
Next, we'll see how big and small companies use generative AI across their teams today.
How businesses are using gen AI today
By now, it's clear that generative AI isn't just for content creators and startups. Businesses of all sizes are building it into their core workflows.
Small teams and startups are using AI agents and templates to get more things done with less resources. Instead of hiring a coordinator, they'll automate lead follow-ups, triage support tickets, or generate client proposals. What used to take three tools and five meetings now happens with a single agent and one Slack thread.
For example, a 10-person SaaS company might use a generative AI app to build AI agents that can:
- Follow up with every inbound lead within 10 minutes.
- Summarize every sales call and send the key points to Slack.
- Update the CRM with call notes and trigger the next email sequence.
That's not a theory — that's daily usage. These AI agents are actively used in sales and ops teams to handle tasks that would otherwise fall through the cracks or require headcount.
Enterprise teams, meanwhile, are integrating generative AI into their customer support systems, sales pipelines, and internal documentation.
Let’s look at some examples:
- A customer support team drafts ticket responses using generative AI, pulling from an internal knowledge base. Gen-AI tools allow teams to route, escalate, and respond all in one place.
- An operations team builds a workflow where an AI agent updates dashboards, flags blockers, and reminds the team about missing inputs.
- A marketing team auto-generates content briefs, emails, and repurposed social posts based on performance data.
Companies that lean into gen AI applications early are starting to see the compound benefits –– fewer handoffs, more consistency, and less overhead.
Whether through custom workflows or prebuilt agents, using generative AI at work now results in speed and easy scaling, not just efficiency. These tools are giving startups and enterprises a practical way to achieve that without needing technical expertise.
We saw examples of how generative AI aids business processes. But how did we pick these examples? Let’s decode that next.
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How we picked these generative AI examples
With hundreds of tools launching every month, the hardest part isn't finding examples — it's finding the most valuable ones that teams are using for actual applications or workflows. Here are the criteria we used to build this list:
- Real-life implementations, not tech demos: Every example here is based on a working product or a live use case, not just a vision deck. Whether it's an AI avatar presenting a sales deck or a support assistant triaging tickets, these aren't ideas. They're part of daily workflows.
- Designed for teams, not just individuals: It's easy to find personal productivity tools. But what matters in a business context is how well a tool fits into a team's workflow. Many examples here are used by revenue, support, or ops teams, not just individual power users.
- Cost and accessibility in 2025: Gen AI is moving fast, but it's also getting more affordable. The healthcare-specific gen AI tools are the most expensive, as expected, with their price going up to ~$1800/month for AI transcription. Other creativity and marketing-specific tools like Scenario.gg are around ~$150-300/month for their most premium plan.
Ultimately, the best generative AI tools make people faster, sharper, and more focused on their work.
Let Lindy be your AI-powered automation app
If you want affordable automation with generative AI capabilities built-in, Lindy is a great choice — it’s an intuitive platform where you can build custom AI agents for a wide range of tasks.
You’ll find plenty of pre-built templates, and there are loads of integrations to choose from.
Here’s why Lindy is an ideal option:
- AI Meeting Note Taker: Lindy can join meetings based on Google Calendar events, record and transcribe conversations, and generate structured meeting notes in Google Docs. After the meeting, Lindy can send Slack or email summaries with action items and can even trigger follow-up workflows across apps like HubSpot and Gmail.
- Sales Coach: Lindy can provide custom coaching feedback, breaking down conversations using the MEDDPICC framework to identify key deal factors like decision criteria, objections, and pain points.
- Automated CRM updates: Instead of just logging a transcript, Lindies can be set up to update CRM fields and fill in missing data in Salesforce and HubSpot — without manual input.
- AI-powered follow-ups: Lindies can send follow-up emails, schedule meetings, and keep everyone in the loop by triggering notifications in Slack by letting you build a Slackbot.
- Lead enrichment: Lindies can be configured to use a prospecting API (People Data Labs) to research prospects and to provide sales teams with richer insights before outreach.
- Automated sales outreach: Lindy can run multi-touch email campaigns, follow up on leads, and even draft responses based on engagement signals.
- Cost-effective: Automate up to 400 monthly tasks with Lindy’s free version. The paid version lets you automate up to 5,000 tasks per month, which is a more affordable price per automation compared to many other platforms.
Try automating up to 400 tasks with Lindy and see how it is an excellent automation tool with Gen AI built into it. Try Lindy for free.
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