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- 🤖 10 Practical Applications of Generative AI for Businesses in 2024
🤖 10 Practical Applications of Generative AI for Businesses in 2024
Exploring How AI is Making Business Smarter: Simple Ways Companies Use AI Today
10 Practical Applications of Generative AI for Businesses in 2024
Let's talk about how AI is changing the way companies work today. AI, or artificial intelligence, is super smart and can do lots of things that make jobs easier. It's like having a robot helper that's really quick and knows a lot. We're going to look at some cool ways businesses are using AI right now. This includes chatting with customers, writing stuff quickly, helping workers find information quickly, and even making decisions easier by summarizing long documents. Big companies are already using AI to speed things up and do better work. So, let's explore these neat ways AI is helping in the business world.
1. Automated Customer Support with Human Touch
Generative AI can be very useful for customer service. It can quickly respond to customer questions and requests through chat, phone calls, or emails. This AI can fully automate customer service or help human agents do their jobs better.
For example, generative AI can search for information, summarize calls, and analyze transcripts. This helps customer service managers see common problems and improve their products and services.
The AI can also personalize responses for each customer based on how they speak and write. This improves customer satisfaction and loyalty.
Expedia is using generative AI in its travel app. The AI assistant acts like a travel agent. It suggests destinations, hotels, and transportation based on what the user is looking for.
Expedia trained the AI on over a quadrillion travel options. So the assistant can find the best flights and hotels at certain prices and dates. This helps travelers save money and earn rewards.
In summary, generative AI can reduce wait times, improve satisfaction, and lower costs for customer service. It's very useful for banking, insurance, energy, and other industries. Adopting this AI can cut customer service expenses by up to 30%.
2. Content Marketing for Digital Outreach
Generative AI can be very useful for content marketing. It can create relevant, coherent content on any topic in seconds. This is much faster than human writing.
Some brands use generative AI to write social media posts, blog posts, product descriptions, articles, emails, and presentations. This reduces content costs.
But there are some issues. Generative AI can sometimes share false or made-up information. It also can't search the internet to find data and quotes. This limits its SEO value.
However, generative AI can still help content teams.
Do initial research on complex topics
Write early drafts for articles
Catch grammatical errors and improve writing style
At ITRex, generative AI makes our writers 30% more productive. They can focus more on research and experts.
AI helps with:
Researching new technology topics
Drafting articles and parts of articles
Editing and improving human-written content
Companies could train generative AI on their data to make highly customized, effective content. This content could rank well in searches and convert visitors.
In summary, generative AI has limitations but can make content creation much faster. It leaves human writers more time for high-value tasks.
3. Internal Chatbots for Employee Assistance
Companies can use generative AI to create chatbots for employees. These chatbots are connected to the company's knowledge base.
When employees have a question, they can ask the chatbot. The chatbot will instantly give them an accurate answer using the knowledge base.
This saves employees time since they don't have to search for information themselves. It also makes sure they can easily find the insights and resources they need.
For example, a government agency created an AI chatbot for employees. The chatbot searches the agency's documents to find answers to questions.
Now employees can ask the chatbot instead of bothering the agency director.
In summary, internal AI chatbots give employees quick access to company knowledge. This saves time and effort for everyone.
4. Automated Document Summarization
Automated document summarization uses AI to quickly summarize key information from large amounts of documents. This helps people make faster decisions by giving them the main facts and action items without having to read everything.
For example, a government agency used AI to process job applications faster. Before, employees had to manually match job descriptions to job codes, which took weeks. The new AI system can automatically recommend the right codes by analyzing the text.
Now the agency can process applications much faster. The AI does the time-consuming matching work. This frees up employees to focus on more important tasks. Overall, the AI-powered application has greatly reduced the application processing time. This allows the agency to handle more applications and make decisions quicker.
5. Email Response Generation
AI can be used to generate email responses automatically, which makes communication workflows more efficient.
For example, an investigation organization uses AI to process tips they receive. The AI reads the tips, pulls out key details, matches them to cases, and writes a draft email response. This is much faster than having employees do all this manually.
An employee reviews the AI-generated email and sends it back to the tipster. This provides a quick response while still having human oversight.
With this AI tool, the organization can act faster on important tips and handle more cases overall. The AI does the time-intensive work of analyzing tips and drafting responses. This frees up employees to focus on other critical tasks.
In summary, the AI generates email responses automatically based on predefined settings. This streamlines communication workflows, so organizations can respond faster and handle more volume. The AI drafts the emails, and humans review them before sending them.
6. Tailored Customer Service Assistants
AI chatbots can provide customized customer service to improve the user experience and efficiency.
For example, an insurance company uses AI bots to handle claims. First, AI reads and organizes incoming emails. Then a chatbot asks customers questions and addresses issues.
The chatbot is customized to match the company's brand look. Customers interact with the bot to get answers without contacting a human agent.
This frees up the company's staff to focus on more complex claims that need human attention. The AI bots handle the simple, repetitive questions.
Overall, the AI chatbots improve customer service by providing quick, customized responses. Customers get their questions answered faster. And company staff can focus on higher-level work.
The bots are an efficient "first layer" of service. They resolve easy issues so humans can spend time on the harder ones. This improves customer satisfaction and makes operations more efficient.
7. Optimizing Entity Extraction
AI can extract the most relevant data from large amounts of text. It can categorize and prepare the data for analysis. This improves data management and analysis.
For example, a home retailer used AI to speed up returns and refunds. Before, employees had to manually approve everything. Different systems didn't share data well. This caused delays, cancellations, and dissatisfied customers.
The company automated several steps. Software robots automate refund payments across payment systems. Then AI extracted key customer data from documents needed for the refunds.
This automation streamlined up to 90% of the previously manual work. The AI quickly pulls the most important data from text for the refund process. Employees don't have to read through everything manually anymore.
In summary, the AI helps efficiently extract the most useful data from large volumes of text. This data can then be categorized and prepared for analysis. It enhances data capabilities by removing tedious manual work. Employees can then focus on higher-level data tasks.
8. Efficient Workflow Creation for Knowledge Workers
AI can automate and streamline workflows for knowledge workers. This includes jobs like legal, finance, and sales.
Knowledge work involves unstructured data like documents, emails, reports, etc. AI is good at analyzing large amounts of unstructured data.
AI can summarize information, pull out insights, search for relevant information, and surface key points. This saves knowledge workers time compared to reading everything themselves.
For example, AI tools can analyze legal documents to highlight important passages. In finance, AI can process earnings reports and find key takeaways. In sales, AI can comb through customer data to identify sales opportunities.
By handling time-consuming data tasks, AI lets knowledge workers focus on higher-value work. Legal experts can provide analysis instead of just research. Financial analysts can give strategic advice rather than just reporting numbers. Salespeople can have more customer conversations than administrative work.
In summary, AI improves efficiency for knowledge jobs by managing unstructured data. This frees up workers to focus on tasks only humans can do. AI becomes the data processor, while knowledge workers apply their expertise.
9. Streamlining Engineering and Data Processes
AI can help automate repetitive coding, debugging, and data engineering tasks. This boosts efficiency for software and data engineers.
For example, AI tools can:
Generate code snippets and review code for bugs
Automatically fix minor bugs
Create synthetic test data to protect privacy
Generate documentation for code
Convert old code languages to modern ones
These AI capabilities let engineers focus on higher-value work instead of mundane tasks.
A media company uses AI to classify and prioritize data changes. The AI then triggers appropriate test builds. This streamlines their workflows.
OpenAI uses AI internally to aggregate system alerts and understand product issues. This is faster than manually investigating each one.
In summary, AI handles repetitive coding, data, and debugging work. This boosts engineering productivity, so teams can focus on creativity and strategy. AI becomes the assistant for rote tasks while engineers apply expertise. It's a more efficient division of labor.
10. Democratizing Data Access Across Companies
AI tools allow non-technical employees to access and analyze data themselves. They can enter plain English questions that the AI converts into SQL queries. This makes complex data more accessible across organizations.
For example, an employee could ask, "What were total sales last month in the Western region?" The AI would generate the SQL code to pull that data.
This self-serve model is faster than requesting reports from IT or data teams. It democratizes data access beyond just analysts.
A chatbot helps employees at a tech company query data themselves in plain language. This provides easy self-service.
A livestream company uses AI to teach all employees SQL. The AI helps non-technical staff learn by generating code based on their questions. This lets everyone access data.
Vendors offer natural-language AI interfaces to databases. These will allow plain English queries company-wide.
In summary, AI breaks down data silos by letting all employees query data. It translates plain language to SQL. This provides self-service access across an organization, not just analysts. It democratizes data to drive broader insights.
In conclusion, generative AI is really changing how businesses work. It's making customer service quicker and more personal, helping create content quickly, and giving employees easy access to important information. For example, AI can chat with customers, write articles, and even summarize long documents quickly. Big companies are already using this AI to do things faster and better. This technology helps save time and money, making it super useful for lots of different jobs, like customer service, marketing, and data management. Overall, AI is a big help in today's fast-moving business world.
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