Generative AI technology is a form of artificial intelligence that takes input from a user and applies AI models to generate an output it predicts the user expects. AI works by using machine learning algorithms, neural networks, and deep learning to learn from large data sets and identify patterns in the data. Once an AI model learns from its training data, it can predict what the user wants to see.
The past several years have seen many advancements in generative AI—allowing users to generate marketing content, streamline health care analysis, write programming code, and many other tasks. McKinsey predicts that generative AI could add at least $2.6 trillion to $4.4 trillion annually to the global economy.
This guide discusses the applications of generative AI in business and explores several AI use cases.
Digital media and entertainment
Generative AI is showing promise in the media and entertainment space by helping creators generate images, music, and web content.
Generative AI models, like DALL-E 2 from OpenAI, craft realistic images by training on vast data sets of various existing images. This extensive training enables them to discern and replicate intricate patterns, artistic styles, and art techniques.
When users provide AI models with text prompts, such as “a two-headed flamingo wearing sunglasses in the style of Picasso,” the system can generate a high-quality image that matches this description.
Tools like DALL-E 2 and Midjourney allow users to bring their ideas to life by providing a textual description of the image they envision. The integration of generative AI with graphic software like Adobe Firefly has streamlined routine design tasks, enhancing efficiency in creating fills, applying text effects, and extending images.
Generated images have profound implications for digital marketing, offering a tool to produce unique visual content quickly, tailored to specific campaigns or themes.
Generative AI models have ventured beyond visual content and moved into the realm of audio. By learning from vast libraries of music, these models can discern patterns, rhythms, and unique signatures of various music styles. They can transform a simple text prompt from the user into sounds, composing unique pieces from scratch.
A notable player in this field is Google’s Magenta Studio. It’s open source—welcoming a community of artists and creatives to generate music for free—and its capabilities span generations of melodies, rhythms, and dynamic musical elements.
With the availability of Magenta’s Python library, developers can embed AI-driven music creation directly into new applications, potentially automating intricate audio manipulation tasks.
The capabilities of AI are transforming digital media content. Businesses are leveraging AI to craft everything from images and videos to text and audio, establishing strong online brand presences.
Generative AI solutions can automate many aspects of content creation, such as drafting social media captions, designing brand website graphics, or generating blog articles. This automation isn’t just about speed—it also ensures consistency across platforms, helping brands maintain a unified digital voice and look.
A notable development is in video content. Tools like Runway are making AI-generated videos a practical option for many businesses.
Business and commerce
AI in business is helpful in several ways. We explain how it can help generate product descriptions, serve customers through chatbots, and allow companies to gain valuable insights into their data.
Product description generation
E-commerce platforms are tapping into AI to make listing products easier and more effective. If a business has a lot of products, listing and describing them can be really time-consuming.
AI tools help by automatically creating product descriptions. You can create a single template that lists the different features your customers want to know about. You can then add the details for individual products to the template, and ask the AI to write complete descriptions in your company’s style. Some AI content generators, like ChatGPT, can even adjust the length of the description based on what you want.
But it’s not just about descriptions. AI can also help improve how products show up in searches, suggest the best price based on what others are selling, and even decide which products to show customers based on what they might like.
Customer support chatbots
Earlier chatbot versions relied heavily on scripted responses, answering FAQs based on a predetermined set of rules. While these still serve their purpose, they’re more limited in their ability to handle natural language, or complex or unanticipated queries.
Companies now have the option to tap into powerful AI models, integrating them into their chat systems. This allows for a more natural conversation flow and can also tailor responses based on the unique context of each customer interaction.
For instance, OpenAI offers application programming interface (API) access to its large language models (LLMs), like GPT-3, GPT-3.5, and GPT-4. Businesses can connect their chatbots to these models, empowering them to engage customers in real time, provide more accurate information, and enhance the customer service experience.
Market trend predictive analysis
AI can sift through vast data sets, extracting invaluable insights about market trends. By recognizing intricate patterns and correlations in data, AI tools provide businesses with predictive analytics that can forecast emerging trends, potential market shifts, and areas of growth.
These insights, derived in real time, inform business decision-making, allowing companies to adjust their strategies proactively and stay ahead of the competition.
Advanced AI tools like AnswerRocket can also streamline the data interpretation process. Instead of manually parsing extensive reports, businesses can deploy chatbots trained on data analytics. With a simple query, these chatbots can provide clarifications, highlight critical data points, and offer summaries, ensuring businesses remain agile in rapidly evolving markets.
Generative AI has helped transform health care. We cover how it can help with drug discovery, enhance data synthesis, and empower medical professionals with advanced virtual assistants.
Generative AI is changing the pharmaceutical industry, particularly in expediting drug research. One of its major contributions is enabling researchers to generate and analyze molecular structures, accelerating the testing of potential new drugs for efficacy.
A notable player in this industry is Google’s Isomorphic Labs. Leveraging DeepMind and the AlphaFold system, Isomorphic offers a comprehensive suite of tools designed to streamline the drug discovery process.
These advancements quicken the time it takes to discover potent drugs and can also enhance patient care and treatment options.
Medical data synthesis
The health care sector has tapped into the potential of generative AI to craft synthetic patient data, enhancing research capabilities.
Instead of relying solely on real-world patient data—which often poses privacy concerns—researchers can use AI to generate realistic but artificial patient profiles. These synthetic data sets provide valuable insights into potential drug interactions and medical scenarios without risking any actual patient information.
Tools like Syntegra eliminate the need for patient trials early in the research phase and guarantee patient privacy. The data is completely synthetic, ensuring no real patient information is ever at risk.
Virtual health assistants
Virtual health assistants are advancing beyond typical chatbots, serving as a reliable resource for health-related queries. Many patients are short on time or hesitant to rely on inconsistent online sources, so they may lean toward these AI-driven tools for guidance.
One notable development is Google’s Med-PaLM-2. Trained extensively on medical research papers, the tool offers more refined and trustworthy insights than conventional online searches.
Virtual assistants can provide immediate, accurate responses to patients’ queries, and when they need a more in-depth consultation, these systems can facilitate scheduling with health care specialists.
Software development has seen several new tools pop up due to generative AI. Developers can use it to generate code, automate bug detection, and optimize development workflows.
AI development tools can generate code for developers to use. Programming models get their training from large codebases and multiple programming languages. For example, Copilot uses the public GitHub repositories for training.
This training allows developers to ask AI models coding questions and get programming code as an answer. They can then work with AI tools to refine their code and resolve bugs to create more programming code.
Tools like Cursor are expanding those capabilities, diving deeper into analyzing existing code files to enhance the AI’s code suggestion capabilities.
Automated bug detection
Bugs are a common occurrence for developers. Many business days are spent tracking bugs down and troubleshooting problems. That leads to a lot of lost time.
AI can help automate the bug detection process by analyzing code patterns, referencing extensive databases of known errors, and understanding intricate programming structures. AI tools can also suggest solutions to fix the errors.
For example, GitHub Copilot allows developers to submit segments of their code and receive real-time corrections.
Software workflow optimization
The use of generative AI in software development can help automate tasks like code deployment and provisioning, reducing manual intervention. Advanced AI tools like Tabnine can also help predict potential issues and suggest optimizations, streamlining the code review process.
By taking care of these tasks, AI allows developers to focus on innovative solutions, tackle more intricate challenges, and ensure a higher quality of code in shorter cycles.
Given how much data AI uses to train, education is a primary focus for AI applications. It can help generate data-based tests, summarize text for learning and planning, and tutor students online.
Automated test generation
One of the roles of educators is creating exams and assessment tools that test knowledge and align with the curriculum. This manual process can be time-intensive.
Generative AI offers a solution. By being fed the specific content of a curriculum, generative AI tools like Conker can generate questions that accurately reflect the material taught. This ensures tests and quizzes are comprehensive and relevant.
AI-generated exams also help eliminate any potential biases. Unlike manual test creation, which can sometimes inadvertently introduce subjectivity, AI ensures evaluations are more objective, better focusing on the content and desired learning outcomes.
One of the most challenging parts of lesson planning is looking through large amounts of information. Teachers need to understand the text’s main points to create effective lessons.
AI can help with its summarization abilities. Teachers can provide an AI text generator blocks of text and ask it to summarize the information. For instance, the AI generator Claude has one of the largest windows for text input at around 75,000 words. This allows users to enter chapters worth of text and get summaries.
These summaries are useful for creating quicker lesson plans and personalized learning for students with specific needs. It can also help a teacher discuss in real-time subjects that students bring to the classroom, helping to better personalize lessons and engage students.
One of the primary advantages AI-driven virtual educators offer is uninterrupted access. Unlike office hours or scheduled classes, AI educators are available round the clock, ensuring students can seek help whenever they need it.
Platforms like Khan Academy have introduced AI-powered tutors that engage students interactively. These virtual tutors, trained on extensive educational data sets, cater to individual learning paces and styles.
While AI educators shouldn’t replace human educators, they complement the learning experience by offering personalized, on-demand academic assistance.
Supply chain and logistics
AI systems offer several advantages to the logistics industry—allowing companies to take advantage of better inventory management, vehicle route optimization, and risk analysis.
Predictive inventory management
While maintaining adequate stock ensures businesses can meet consumer demands, excessive inventory ties up capital and may result in waste or losses.
Generative AI dives deep into historical sales data, seasonal trends, and market fluctuations to provide accurate predictions for future demand. These insights allow businesses to precisely gauge optimal order points, safety stock levels, and reorder schedules.
Coupa is one example of a platform that aids in inventory optimization, using generative AI techniques to offer granular insights, ensuring businesses remain agile and responsive to changing market dynamics.
Optimizing delivery routes isn’t merely about connecting points A and B. For businesses running vehicle fleets, efficiency means conserving fuel, ensuring timely deliveries, maximizing driver productivity, and adjusting to real-time challenges like traffic congestion, roadblocks, or missed delivery windows.
By analyzing past route data, current traffic conditions, and even weather predictions, generative AI can dynamically adjust delivery paths. Tools like Geotab and Routific use advanced AI algorithms to provide fleet managers with real-time recommendations.
Supply chain risk analysis
Understanding potential risks is pivotal for timely and efficient deliveries. Generative AI can help forecast these risks and provide actionable insights.
By analyzing company data—ranging from transaction records to shipment logs—these algorithms can anticipate disruptions from challenges including weather, geopolitical events, supplier delays.
Beyond just risk anticipation, tools like Sphera spot patterns that might suggest fraud or other anomalies. By identifying deviations from normative behaviors, these tools help businesses navigate the supply chain environment.
What to consider about generative AI applications
Generative AI offers a lot of opportunities for businesses, but using these tools comes with its own set of challenges and ethical considerations.
- Accuracy and authenticity. Generative AI can sometimes produce inaccurate or misleading information. You’ll need to design robust verification mechanisms to ensure quality and accuracy. For instance, if AI-generated articles are disseminated as genuine news without being rigorously fact checked, readers may be misled and the brand’s reputation tarnished.
- Copyright and originality. There’s a risk of AI producing content similar to copyrighted material, especially when trained on vast public data sets. An example of this is when AI-generated music unintentionally resembles copyrighted tracks, which could spark challenges to intellectual property rights.
- Privacy concerns. Generative AI processes vast amounts of data, potentially compromising user privacy. Businesses should be transparent about data collection and usage.
- Integration and ROI. Integration can be cumbersome, especially in established systems. Businesses should consider both the initial investment and potential long-term gains or losses, and employ a robust change-management program before wide-spread adoption is enforced.
- Ethical responsibilities. Both developers and users have a shared responsibility to ensure AI tools are used ethically. Continuous dialogue, regularly updated guidelines, and best practices are important.
While generative AI promises benefits, businesses need to approach its adoption with a good understanding of its implications.
Embrace generative AI with the right help
Generative AI has proven to be useful across various industries, helping marketers, health care professionals, educators, supply chain managers, and more. The ability to input data using natural language and get user-friendly answers allows professionals to get more done and gain insights they may not have gotten otherwise.
If you’re inspired by the tangible impacts of AI highlighted in this article and are eager to leverage its potential, consider browsing Upwork to find a generative AI professional with the skills you need.
Disclosure: Upwork is an OpenAI partner, giving OpenAI customers and other businesses direct access to trusted expert independent professionals experienced in working with OpenAI technologies.
Upwork does not control, operate, or sponsor the other tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.
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