Generative AI has the potential to create new ways of working and innovating, enhance other AI systems and technologies, and transform enterprises in all industries. It can solve challenges in new ways, provide improved capability to perform business functions, and offer benefits in efficiency, capacity, speed and scale. Statista projects the market size of this technology to reach $44.89 billion in 2023 and grow to $66.62 billion in 2024. DigitalMara has prepared an overview of Generative AI and the best ways to use it.
The Generative AI algorithm is inspired by the human brain, with its billions of neurons. It goes further than the deep-learning models of traditional AI, allowing systems to process extremely large and diverse sets of unstructured data, reorganize and classify data, perform more than one task and create novel output.
All modalities are generated from natural language descriptions aligned with the user’s needs. More than one modality can be used for each use case. For example, text output by a chatbot can be represented as a simulated sound, and the generated image can be expanded to a video.
A lot of research has been published about Generative AI by Mckinsey, Bloomberg and Gartner. We’ve selected some key findings.
- Mckinsey reports that GenAI’s impact on productivity is expected to rise in global annual value from $2.6 trillion to $4.4 trillion..
- Mckinsey research also shows that GenAI can boost value by 75% percent in the fields of customer operations, marketing and sales, software engineering, and R&D.
- GenAI and other technologies have the potential to automate work activities that absorb 60% to 70% of employees’ time. (Mckinsey)
- Bloomberg reports rising demand for GenAI products that can add about $280 billion of new software revenue, driven by specialized assistants, new infrastructure products, and copilots that accelerate coding.
- The largest drivers of this rise in revenue are Generative AI Infrastructure as a Service, used for training LLMs (large language models), digital ads driven by technology, and specialized GenAI assistant software. On the hardware side, the drivers are AI servers, AI storage, computer vision AI products, and conversational AI devices. (Bloomberg)
- According to Gartner, by 2025, more than 30% of new drugs and materials will be systematically discovered using Generative AI techniques.
- By 2025, the use of synthetic data will reduce the volume of real data needed for machine learning by 70%. (Gartner)
How Generative AI can be embedded into your software and processes
With APIs and SDKs provided by AI companies or open-source communities, developers can add Generative AI models to various kinds of software applications. For example, OpenAI provides a GPT-3 API that developers can use to generate natural language text for various applications such as chatbots, content creation, and language translation. NVIDIA offers an SDK called StyleGAN that allows developers to generate high-quality images and videos using generative AI.
Developers can build their own custom Generative AI models using Deep Learning frameworks such as TensorFlow and PyTorch. These frameworks provide tools and libraries for building and training complex neural networks, including generative models.
Also, there are pre-trained Generative AI models that developers can use without needing to train their own models. These pre-trained models can be used as a starting point and fine-tuned for specific tasks or applications.
Ways to use Generative AI
Generative AI applications like ChatGPT, Stable Diffusion, Midjourney and others have captured people’s attention worldwide. With their ability to write text, compose music, and create digital art, anyone can create delightful content. However, the potential of GenAI is much broader.
Technology can fulfill a number of tasks in various industries and for all industry players — startups, SMBs and enterprises. The main value of GenAI is about saving on time and cost, uncovering new ideas and insights, and accelerating new-product development. Task automation allows released resources to be used to solve other tasks.
- Research
Having access to a variety of types of data, such as purchasing patterns, customer service, website and browsing data, and response to marketing campaigns, GenAI can conduct data analysis and provide you with important insights. You can learn, for example, which products are lacking in the market, what users want to see in an application, what limitations you may face in implementing an idea, and much more. The report may contain some degree of bias, but it can offer a good basis for new product development
- Product design (rapid prototyping)
GenAI can help to design workflows for developing a new product from your description and collected data about customer preferences and trends, and to write a basic technical specification. You won’t get a full-fledged working prototype from this process, but it will give you an understanding of how processes should work inside the application and some technical requirements. Such a prototype can be passed to the development team for creating an MVP and detailed technical specifications.
Also, it’s worth mentioning that there are GenAI-based app builders. They also can be used for prototyping. However, they have limited capabilities and lack customization. You may visualize your idea and export your app as a file in HTML, CSS, or JavaScript formats. This will help the development team work further on your application and deploy it into production.
- Content creation
GenAI can help to create personalized and contextually relevant content in multiple languages, such as product and app descriptions, advertising text, text for a website or landing page, and other materials. It’s possible to generate various text versions, of different lengths. However, it’s always important to check out all text written by AI to be sure it avoids bias and incorrect information and properly corresponds to what you want to communicate.
- Visuals creation
GenAI can be used to create and edit images and videos for applications, websites, landing pages, advertising banners and other uses. This is an easier way to create large volumes of content and test it in production. But you will need to monitor it for quality and copyright protection. The designer’s control won’t be superfluous.
- Development
GenAI can supplement the work of developers. It is capable of writing code in different programming languages, converting functional code to different environments, and assisting in repetitive tasks such as code maintenance and deployment across different platforms. In addition, it can create documentation describing the components and how the application works. Remember, you can’t run this process without human control. Developers have to validate the code to mitigate the risk of bugs or vulnerabilities and do security checks.
Industry-specific tasks
E-commerce, retail, banking, insurance
- Customer support and virtual assistant
Virtual assistants empowered with GenAI can improve the customer experience by providing real-time, personalized support and creating new ways of interacting with customers. They are capable of giving recommendations, responding to queries, handling complaints, making appointments and other tasks. They can also implement these functions in various languages with automatic translation. Customers can use natural language, voice and image inputs for communication.
E-commerce, retail
- Virtual try-on
GenAI allows consumers to see a digital rendering of clothes, cosmetics, furniture and other products on their bodies or in their homes. Customers are increasingly choosing to shop for these items online, but at the same time they’d like the opportunity to try things on, to better imagine how a product will look before making a purchase. Digital renderings reduce the likelihood of mismatched expectations, product dissatisfaction, and returns.
Logistics, manufacturing
- Supply chain optimization
GenAI can help identify and simulate potential disruptions and risks in the supply chain and give recommendations for mitigating them, by assessing factors such as port congestion, shipment routes, supplier mapping and reliability, demand patterns, and production capacity. Also, technology allows better decision-making by running what-if scenarios in a digital twin environment that reflects the real-world supply chain.
Insurance
- Automated claims reporting
Virtual damage rendering based on GenAI can be used for damage visualization, replicating it virtually. The basis for this can be formed from customer conversations, damage documents, photos, official reports, and other relevant media. Using such a system, agents are empowered to make better assessments and decisions regarding the amount and cost of the damage.
Banking, insurance
- Real-time risk management
With GenAI, financial institutions can better assess and manage risks related to credit, investment, fraud, and cybersecurity. Real-time monitoring, identifying and predicting fraudulent patterns makes it possible to reduce the overall fraud rate and enhance security. Moreover, the technology offers the ability to create synthetic data that reflects fraudulent transactions and train models to better identify risky scenarios.
Healthcare
- New-drug discovery
GenAI can be used to model the structure and function of proteins and biomolecules, accelerating the identification and validation of molecules and the creation of new pharmaceuticals. It can also help verify the validity of new drugs through simulations, selecting the best potential candidates for further testing and decreasing the number of real-time iterations.
Government
- Urban planning
GenAI can create 3D images and help in creating master city plans. Simulating factors such as natural disasters, population growth and demographic trends, it can help to evaluate the vulnerability of city infrastructure, develop scenarios for urban expansion, and create plans for resilient urban infrastructure, housing, transportation, and public services.
Education
- Hyper-personalized education
Generative AI makes it possible to adjust the traditional approach to education and to provide students with personalized learning support. A digital, adaptive teacher can use resources and lesson plans, check the student’s work and understanding, and create a hyper-personalized learning experience based on the student’s individual weaknesses, strengths, and preferences.
Generative AI and ethics
Obviously, Generative AI raises some ethical concerns. The main issues relate to bias, privacy, security and safety:
- Being trained on biased data sets, GenAI models can produce incorrect and discriminative outputs.
- GenAI models may use personal data to generate content or make predictions, which violate data privacy and security standards.
- GenAI models can be used to create harmful and fake content for cyberbullying, harassment, and misinformation about events.
To overcome these concerns, experts recommend using diverse and representative datasets for models training, conducting risk assessments, regularly monitoring and auditing models for bias, errors, and harmful content, and implementing strong data privacy and security measures, such as encryption and access controls, to protect personal data.
Final words
Generative AI is capable of improving processes and accelerating digital transformation. You can add AI into your software or choose one of the AI tools available in the market. It is necessary to understand not only the benefits but the risks as well. GenAI providers stand for a certain independence in actions. However, at DigitalMara, we believe that everything related to development is better done together with a team of engineers and developers.
