Generative AI is set to revolutionize various industries by boosting productivity and introducing new methods of working and creating. Like how smartphones transformed business productivity and communication, generative AI unleashes a new wave of innovation.
At Stermedia, we’ve been implementing AI since 2009, but it gained widespread attention only recently with the release of ChatGPT in 2023. Since then, the industry’s growth has accelerated dramatically:
- According to McKinsey studies, 65% of companies in 2024 will use generative AI in some capacity, double the rate from 2023. (source)
- Additionally, 89% of executives rank AI and GenAI as a top-three tech priority for 2024 (source)
By mid-2024, clear trends in the use of AI in business are already emerging. We have identified 8 key use cases and will discuss them in this article.
What Is Generative AI?
Generative AI refers to algorithms capable of producing realistic content—such as text, images, or audio—based on the patterns and structures found in training data. These algorithms are often built on foundation models trained on vast amounts of unlabeled data to identify underlying patterns across various tasks.
Simply put, generative AI can create new content by learning from and mimicking existing data.
For example, GPT, a foundation model trained on extensive text data, can be adapted for tasks like answering questions, summarizing text, or sentiment analysis.
Similarly, DALL-E, a text-to-image model, can generate images, extend existing images, or create variations of artworks.
However, their applications extend far beyond just content generation.
How Do Businesses Utilize Generative AI?
Generative AI is being used in business to automate customer service, personalize client experiences, enhance analytics capabilities, model complex scenarios, and more.
This versatility is where its actual value lies.
1. Improved Customer Service
Generative AI is revolutionizing customer service by offering instant, automated support that still feels personal.
Imagine having chatbots and virtual assistants that can handle most customer interactions via live chat, phone calls, and emails 24/7. This would enhance the customer experience and save your business significant time and money.
But Generative AI doesn’t just automate—it also works alongside human specialists.
The AI assistants can handle tasks like searching for information, summarizing calls, and analyzing transcripts. This helps your support managers spot common issues, improve service quality, and use customer feedback automatically.
Another big advantage is hyper-personalization. Generative AI can detect subtle details in customer interactions, like word choices and tone of voice, to tailor responses and offers.
2. Real-Time Business Insights
Generative AI can analyze data collected from various processes, offering real-time insights without needing any coding or technical skills. Your team can just ask questions, and GenAI’s natural language processing capabilities will deliver the answers.
Here are a few business use cases:
- Supply Chain Optimization: GenAI can dive into your supply chain data to spot inefficiencies, predict potential disruptions, and suggest the best routes and inventory levels.
- Customer Behavior Analysis: Retailers can tap into GenAI to analyze customer data from different touchpoints, revealing shopping patterns, preferences, and trends.
- Financial Data Analysis: Financial institutions can use GenAI to sift through large amounts of transactional data to detect fraud, assess risk, and identify investment opportunities.
- Manufacturing Process Improvement: GenAI can monitor real-time production line data to identify bottlenecks, predict maintenance needs, and suggest process improvements.
- Healthcare Diagnostics: GenAI can assist healthcare providers by analyzing patient data to pinpoint potential health issues, recommend treatment plans, and predict patient outcomes.
- Energy Management: Utility companies can use GenAI to analyze energy consumption data, predict demand, and optimize energy distribution.
- Human Resources: GenAI can help HR departments by analyzing employee performance data, predicting turnover, and recommending personalized training programs.
- Marketing Campaigns: Marketing teams can use GenAI to analyze past campaign data, predict the success of future campaigns, and optimize ad placements.
3. Fraud Detection and Risk Management
AI-powered tools can sift through large amounts of transaction or claims data, quickly identifying patterns or anomalies. This makes generative AI a fantastic support tool for detecting fraud, underwriting, and managing risks in finance.
For insurers, generative AI can streamline the process of optimizing coverage and claim outcomes. By analyzing past policies and claims data from both internal and external sources, AI can help create more tailored plans for customers.
Example Solutions:
- Simplifai InsuranceGPT: Helps insurers analyze and optimize policies and claims, making the process quicker and more accurate.
- Docugami: Uses generative AI to extract critical information from business documents, highlighting risk areas.
4. Better Data Quality
Business professionals usually have the most profound understanding of their data, but dealing with data rules and metadata can be overwhelming and time-consuming. GenAI can simplify this process.
Picture a healthcare provider managing patient records. Typically, staff would spend hours manually reviewing and updating these records to ensure accuracy—a time-consuming task and prone to errors.
By integrating GenAI, the healthcare provider can manage data through simple natural language interactions.
For instance, a staff member could ask the AI to find any inconsistencies in patient records. GenAI would then analyze the data, flagging issues like duplicate entries, missing information, or incorrect details.
GenAI can also suggest improvements to data rules. For example, it might recommend standardizing the format for entering patient information to reduce errors and improve consistency.
5. Optimizing Product Development
By leveraging AI, companies can streamline product development at all cycle levels.
Surfacing Hidden Insights
Generative AI can dig into customer support databases, product reviews, and user surveys to uncover recurring issues and frustrations. This helps ensure that products are designed to address real user needs.
Facilitating Data-Driven Decisions
With easier access to actionable insights, businesses can make data-driven decisions that speed up product iterations and shorten development cycles. Generative AI tools can analyze vast amounts of feedback and usage data, providing clear, actionable recommendations for product improvements.
Enhancing Product-Market Fit
Understanding customer needs deeply allows businesses to design products that resonate better with their target market. This increases the chances of success by ensuring that new products meet users’ actual demands and preferences.
Supporting Developers, Designers, and Product Managers
Generative AI is also being used to code apps, write product documentation, and manage user experiences throughout the product lifecycle.
For example, AI can generate multiple design concepts at scale, helping product designers identify the best options more efficiently.
A product developer might use AI to brainstorm new code and designs, while a product manager could use it to summarize user feedback from multiple sources, ensuring improvements are based on relevant data.
6. Increased Productivity
Generative AI is transforming business process automation (BPA), offering advanced solutions that go beyond traditional robotic process automation (RPA) and intelligent process automation (IPA).
Given that language-based tasks make up about 25% of all work activities, generative AI can enhance various processes and workflows, such as:
- Performing managerial activities, like prioritizing tasks in project management apps, scheduling meetings, and organizing emails.
- Searching for accurate information across your IT infrastructure and summarizing content through a conversational interface.
- Automatically creating standard or custom documents and reports.
- Entering information into technology systems.
According to McKinsey, strategically using generative AI can automate up to 70% of tasks that consume your employees’ time. (source)
Doing so frees up human resources for more strategic initiatives and allows businesses to achieve more with less.
7. Efficient Content Marketing
Marketing departments have been quick to adopt generative AI, and for good reason.
From enhancing recommendation engines to optimizing ad placements, Gen AI can improve nearly every aspect of digital marketing. However, the most common use of Gen AI in marketing is content creation.
Generative AI can churn out contextually relevant and coherent content on virtually any topic in seconds. Compare that to the hours it takes for a skilled writer to produce a 1,000-word blog post. It’s no wonder that Gen AI is already responsible for creating 25% of all digital content.
A Few Examples:
- Initial Research: Gen AI tools help writers quickly understand complex topics and guide further research.
- Content Drafting: AI-generated drafts serve as a starting point. The team then adds statistical data, expert input, and case studies to enrich the content.
- Content Editing: AI algorithms catch grammatical errors, fix style inconsistencies, and tailor the tone to match popular online publications.
Generative AI also streamlines inbound and outbound marketing. AI tools can create and send personalized emails and messages to customers, reducing the workload on your marketing team. They can even automate customer lifecycle management on CRM platforms.
However, in terms of content creation, GenAI is still far from perfect. It can “hallucinate” or produce false information because it is trained on vast amounts of data, which can include inaccuracies.
SEO is another area where generative AI has limitations. While there are specialized plugins for SEO, most Gen AI tools provide basic keyword suggestions rather than comprehensive keyword and competitor research like traditional SEO tools.
8. Employee Onboarding and Education
Generative AI can play a key role in employee education.
From creating personalized learning paths and developing training materials to generating quizzes and other educational content, Gen AI can significantly speed up the work of your learning and development (L&D) team while enhancing learning outcomes.
Consider a large retail chain that needs to train thousands of employees on new sales techniques and product knowledge. Using Gen AI, the company can create personalized learning paths for each employee based on their role, experience, and learning style.
Gen AI can also streamline the hiring process. For instance, it can assist HR teams with CV screening and prepare job interview questions tailored to the applicant’s profile, making the recruitment process more efficient.
Ready to Implement Generative AI in Your Business?
As you can see, generative AI has applications in every business sphere, from product development to marketing, customer interaction, and employee productivity.
Have you considered how you can use it in your business?
See how we help other companies in this area:
- Revolutionizing Public Health with AI and ML: Sanitary Inspection Case Study
- From Vision to Validation: Wakeb’s AI & Image Recognition Journey
- Revolutionizing the Recruitment Process Using Machine Learning
And if you’re ready to discuss your specific challenges – contact us!
