How To Build GenAI Applications & Drive Business Value in 2024

Building applications powered by generative AI can feel overwhelming. With so many options, it can be challenging to know where to begin, even for seasoned developers. Fortunately, GenAI applications are designed to help businesses like yours streamline processes to achieve specific objectives, such as increasing efficiency and driving innovation. This blog will provide valuable insights to help you build GenAI applications that streamline your processes and drive significant business value, leading to increased innovation, efficiency, and profitability in 2024.

To help you achieve your goals, Lamatic has created a managed generative AI tech stack that makes building GenAI applications simple.

What are Generative AI Apps?

Generative AI applications use AI models, like large language models, to create new content, ranging from text and images to:

Audio
Video
Code

Generative AI applications operate by learning from vast datasets to generate realistic outputs. Trained on a large dataset of human interactions, a generative AI chatbot can produce remarkably coherent responses to user queries.

Generative AI Tools

The most common generative AI tools include:

Chatbots
Image generators
Automated content creation platforms

These tools have impressive versatility across industries and differ from traditional AI, which is more focused on pattern recognition.

How Do Generative AI Applications Work?

Generative AI applications work by using machine learning models to learn the patterns and relationships in a dataset of human-created content. They then use the learned patterns to generate new content.

The most common way to train a generative AI model is to use supervised learning. The model is given a set of human-created content and corresponding labels. It then learns to generate content similar to the human-created content and labels with the same labels.

What Can Generative AI Applications Do?

Generative AI applications process vast amounts of content and create new insights and answers via:

Text
Images
Other user-friendly formats

They can also improve customer interactions through enhanced chat and search experiences. Businesses can use generative AI to explore vast amounts of unstructured data through:

Conversational interfaces
Summarizations

Assist with repetitive tasks like replying to:

Proposal requests
Localizing marketing content in five languages
Checking customer contracts for compliance

What’s Behind the Sudden Hype About Generative AI?

sudden hype about gen ai - GenAI Applications

Generative AI leverages advancements in deep learning, natural language processing, and computer vision to produce new content that mimics existing data. Also called machine learning, deep learning uses neural networks to find patterns in data and make predictions.

The deeper the networks, the more granular the patterns, enabling the generation of highly realistic text, images, audio, and video. Natural language processing (NLP) focuses on human language.

NLP and LLMs

NLP systems learn human language by analyzing massive amounts of written content, which helps them understand context and meaning to produce human-like text responses.

Recent deep learning and NLP innovations, including large language models (LLMs), have propelled generative AI to new heights.

Popular LLMs

LLMs, like OpenAI’s GPT-3 and Google’s PaLM, are trained on billions of parameters and can generate coherent and contextually relevant text.

Riding the Hype Cycle Wave

Since 2020, Gartner has tracked generative AI on its Hype Cycle for Artificial Intelligence. The technology has advanced from the Innovation Trigger phase to the Peak of Inflated Expectations.

Generative AI first hit mainstream headlines in late 2022 when OpenAI launched ChatGPT, a chatbot capable of very human-like interactions. The tool became wildly popular overnight and galvanized public attention.

DALL·E 2 and Future

OpenAI’s DALL·E 2 tool similarly generates images from text in a related generative AI innovation. In time, the hype will subside as the reality of implementation sets in.

The impact of generative AI will only grow as people and enterprises discover more innovative applications for the technology in daily work and life.

Generative AI Reshapes Industries by Enhancing Creativity and Personalization

In a recent Gartner webinar poll of more than 2,500 executives, 38% indicated customer experience and retention is the primary purpose of their generative AI investments.

This was followed by:

Revenue growth (26%)
Cost optimization (17%)
Business continuity (7%)

Impact on Industries

Generative AI is reshaping industries by enhancing creativity, personalization, and efficiency at scale. It holds the potential to revolutionize content creation, a fundamental business activity across industries.

Marketing and advertising firms can use generative AI to create personalized content at scale to:

Improve customer engagement
Boost campaign performance

Game Development

Game developers can leverage the technology to generate unique assets, including:

Characters
Environments
Narratives, to enhance player experiences

Major Players in Generative AI

Companies driving interest and investment in generative AI like include:

OpenAI
Google
Microsoft

OpenAI’s recent product launches, including ChatGPT, have sparked public interest in generative AI and inspired the development of similar tools by other tech firms.

Tech Giants' Race

In a bid to compete, Google launched its own chatbot, Bard, and has integrated generative AI capabilities into several products, including the Google Cloud platform.

Microsoft has also invested heavily in OpenAI and is rapidly integrating generative AI capabilities into its products. The company’s latest version of its ubiquitous software, Microsoft 365, includes a feature called Copilot that uses generative AI to help users create content.

Lamatic's Managed Generative AI Tech Stack

Lamatic offers a managed generative AI tech stack. Our solution provides:

Managed GenAI Middleware
Custom GenAI API (GraphQL)
Low Code Agent Builder
Automated GenAI Workflow (CI/CD)
GenOps (DevOps for GenAI)
Edge deployment via Cloudflare workers
Integrated Vector Database (Weaviate)

Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities. Start building GenAI apps for free today with our managed generative AI tech stack.

9 Benefits of Generative AI for Business

1. Creativity Amplification: Unleashing New Levels of Imagination

Generative AI opens the door to creativity and exploration for businesses across industries. This technology enables organizations to produce creative and engaging content at scale rapidly.

In advertising, AI-powered systems can automatically generate compelling ad copy, visuals, and even video content, serving as a valuable tool for innovative ideas and reducing the need for extensive manual creative work.

Future of Content

Latanya Sweeney, Professor of the Practice of Government and Technology at the Harvard Kennedy School, predicts, "When we think about the future of the internet, I would guess that humans will no longer generate 90% of content. It will be generated by bots.”

2. Time and Cost Savings: Automating Processes to Reduce Waste

Generative AI can save businesses significant time and money by automating tasks that previously required human intervention. AI algorithms can generate building designs based on given specifications in architecture and design, significantly speeding up the design process.

Product Innovation

It can also help generate new product concepts and designs by analyzing:

Stakeholder feedback
Market trends

Its power is derived from the speed with which it can analyze massive amounts of data and make design recommendations.

3. Hyper-Personalization: Crafting Unique Experiences That Delight

Generative AI can hyper-personalize the customer experience by analyzing customer data and generating customized product recommendations and offers based on individual preferences.

Voice automation can enable e-commerce brands to provide customer support by dynamically changing personalized voices. These voices will remove the frustration from the support experience and make it more human-like and natural than stale, similar-sounding robotic voices.

4. Enhanced Efficiency and Productivity: Streamlining Workflows for Better Output

With generative AI, businesses can automate complex and time-consuming processes, enabling them to:

Optimize workflows
Improve efficiency
Allocate resources effectively

Industry Applications

In the manufacturing industry, AI algorithms can generate optimized production schedules:

Minimizing waste
Maximizing efficiency

Not only that, in contact centers, generative AI can enable customer support agents with ticket summarizations, responses, and tone suggestions that best suit the context of the customer’s query, significantly increasing their productivity.

5. Data Synthesis: Unlocking Valuable Insights to Improve Outcomes

Generative AI presents a compelling use case in the domain of data synthesis. By leveraging its ability to analyze diverse datasets, AI models can:

Synthesize large amounts of data
Generate valuable insights

Financial Sector

In the financial sector, generative AI can analyze market trends, consumer behavior, and economic indicators to generate predictive models that enable businesses to make well-informed investment decisions.

By harnessing the power of data synthesis, generative AI empowers organizations across various sectors to unlock actionable intelligence and gain a competitive edge in today’s data-driven landscape.

6. Realistic Simulations: Creating Virtual Test Environments to Improve Safety

Generative AI enables the creation of realistic simulations that can be used in various fields, such as:

Training
Entertainment
Research

In the automotive industry, AI-powered simulators can accurately simulate driving scenarios, allowing for safer and more efficient testing of autonomous vehicles.

7. Adaptive Learning: Improving Outputs Over Time

Generative AI systems can continuously learn and adapt based on feedback and new data. This capability allows the models to improve their performance over time and generate outputs that align better with user preferences and objectives.

In personalized education, generative AI models can dynamically adjust learning materials and methods to match individual student needs, preferences, and learning styles, enhancing the effectiveness of educational experiences.

8. Enhanced Knowledge Organization and Discovery: Mining the Data for Intelligent Solutions

Generative AI can facilitate knowledge discovery by mining and extracting insights from diverse sources. It can automatically organize and categorize vast amounts of knowledge assets.

By leveraging machine learning techniques, these systems can identify and tag information based on topics, themes, and relationships, facilitating easier access and retrieval.

9. Improve Customer Experience: Elevating Interactions for Better Satisfaction

Enhancing the customer experience is a prominent and early business application for generative AI. Adopting dynamic AI agents that provide more human-like responses to customer inquiries allows businesses to improve customer interaction.

The underlying language models enable these chatbots to deliver more comprehensive and sophisticated responses, elevating the depth of customer interactions.

Dynamic AI Agents

Generative AI-powered dynamic AI agents can play a supportive role in customer service as agent-assistants. They leverage natural language processing (NLP) to listen to agent-customer conversations and access relevant resources to enhance the interaction.

6 Examples of GenAI Applications Across Industries

examples of gen ai applications - GenAI Applications

1. Transforming Healthcare: Generative AI Solutions

Generative AI has the potential to revolutionize the healthcare industry, from discovering new medicines to personalizing treatment plans and even improving the accuracy of medical imaging. Here are some of the most promising applications of generative AI in healthcare:

Enhancing Medical Imaging

Generative AI can:

Augment medical images like X-rays or MRIs
Synthesize images
Reconstruct images
Create reports about images

This technology can even generate new images to demonstrate how a disease may progress in time.

Discovering New Drugs

Researchers can use generative artificial intelligence via a related field called generative design to research and develop new medicines. Gartner projects that 30 percent of the new drugs created by researchers in 2025 will use generative design principles.

Simplifying Tasks With Patient Notes And Information

Healthcare professionals keep and take notes about patient medical care. Generative AI can:

Build patient information summaries
Create transcripts of verbally recorded notes
Find essential details in medical records more effectively than human efforts

Personalizing Treatment

Generative AI can consider much patient information, including medical images and genetic testing, to deliver a customized treatment plan tailored to the patient’s needs.

2. AI-Powered Advertising & Marketing Solutions

Advertising and marketing professionals rapidly adopt generative AI to create accurate, on-brand content for their campaigns while improving customer interactions. Here are some of the most effective applications of generative AI in advertising and marketing:

Generating Marketing Text And Images

Generative AI can help marketing professionals create consistent, on-brand text and images for marketing campaigns. This technology also offers translation tools to spread your marketing message into new territories.

Gartner predicts marketing professionals will use generative AI to create 30 percent of outbound marketing materials by 2025.

Generating Personalized Recommendations

Generative AI helps create powerful recommendation engines to help customers discover new products they might like. With generative AI, this process is more interactive for customers.

Creating Product Descriptions

Beyond flashy advertising campaigns, generative artificial intelligence can help with tedious or time-consuming content requirements, such as creating product descriptions.

Enhancing Search Engine Optimization

SEO professionals can use generative AI for tasks like image tags or page titles or to create content drafts. You could also use a tool like ChatGPT or Bard to recommend changes you could make to content to improve SEO ranking.

3. Manufacturing Improvements with Generative AI

Generative AI can help manufacturing professionals improve efficiency, anticipate maintenance needs, create better designs faster, and build a more resilient supply chain. Here are some of the different ways generative AI can enhance manufacturing:

Accelerating The Design Process

Using generative AI, engineers and project managers can work through the design process much faster by generating design ideas and asking the AI to assess them based on the project's constraints.

Providing Smart Maintenance Solutions For Equipment

Maintenance professionals can use generative AI to track heavy equipment performance based on historical data, potentially alerting them to trouble before the machine malfunctions.

You are improving supply chain operations. You could use generative AI to track down the cause of problems in the supply chain by speaking conversationally with the technology to sort through a vast amount of transactional or product data. Generative AI can also help generate delivery schedules or recommendations for suppliers.

4. Coding with Generative AI

For a software development team, generative AI can provide tools to create and optimize code faster and with less experience using programming languages. A few examples of the applications of generative AI in software development include:

Generating Code

Software developers can use generative AI to create, optimize, and auto-complete code. Generative AI can create code blocks by comparing them to a library of similar information.

It can also predict the rest of the code a developer begins to type, much like how auto-complete works while texting on a smartphone.

Translating Programming Languages

Generative AI can be a tool for developers to interact with software without needing a programming language. The generative AI would act as a translator.

Automating Testing

Developers can improve their automated testing processes using generative AI to highlight potential problems and execute testing sequences faster than other AI methods.

Generative AI can learn the logic of the software and how users will interact with it and create test cases to demonstrate various user scenarios.

5. Financial Services & Generative AI

According to McKinsey, generative AI could add $200 billion to $340 billion of value to the banking industry annually.

Some of the applications of generative AI in the financial services industry include artificial intelligence investment strategies, drafting documentation and monitoring regulatory changes, and using generative AI as an interpreter to facilitate communications between clients and investors.

Creating Investment Strategies

Generative AI can recommend the best investments according to you or your client’s goals. This technology can find and execute trades much faster than human investors and can do so within the parameters you set for the kind of transaction you want.

Communicating And Educating Clients And Investors

Financial services professionals sometimes need to communicate complex information to clients and colleagues. Generative AI can provide hyperpersonalized customer service without adding more customer service professionals.

Quickly Drafting Documentation And Monitoring Regulation

Generative AI can monitor regulatory activity, keep you informed of any changes, and create drafts of documents such as investment research or insurance policies.

6. Creative Content Generation for Media and Entertainment

Media and entertainment could embrace generative AI in several ways, considering the industry primarily engages in the same task as the tech:

Generating Unique Content

Generative AI can help create and edit visual content, create short highlight videos of sporting events, and make working with content management systems easier.

Creating Audio And Visual Content

Generative AI can create new video content from scratch. This tech can also help you make visual content faster by creating visual effects, adding graphics, or streamlining editing.

Generating Highlights For Sports And Events

Regarding sporting and live events, generative AI can instantly create highlight reels and allow fans to create their own custom highlights. For example, fans could generate highlights of a particular play or a tournament series.

Managing Tags For Better Content Management

Generative AI can tag and index extensive media libraries, making locating the files you need at any time easier. Like our manufacturing example above, generative AI allows you to use conversational language to find the information or media you’re looking for in a complex media library.

How to Successfully Build GenAI Applications

woman building apps - GenAI Applications

Many organizations have generative AI pilots for code generation, text generation, or visual design underway. To establish a pilot, you can take one of 3 routes:

1. Off-the-Shelf

You can use an existing foundational model directly by inputting prompts. For example, you might ask the model to create a job description for a software engineer or suggest alternative subject lines for marketing emails.

2. Prompt Engineering

Program and connect software to and leverage a foundational model. This technique, the most common of the three, allows you to use public services while protecting IP and leveraging private data to create more precise, specific, and useful responses.

An example of prompt engineering is building an HR benefits chatbot that answers employee questions about company-specific policies.

3. Custom

Building a new foundational model is beyond the reach of most companies, but it’s possible to tune a model.

This involves adding a layer of proprietary data that significantly alters the foundational model's behavior. While costly, customizing a model offers the highest level of flexibility.

Building GenAI Apps with Generative AI Tools

SaaS tools make building GenAI apps a breeze by offering pre-built, customizable solutions. These platforms offer a range of generative AI tools designed to enhance productivity and creativity. Platforms like IBM Watsonx and Vertex AI by Google Cloud combine multiple tools that save you time and effort.

Instead of building everything from scratch, you can better focus on how to build GenAI apps‘ unique features. GenAI SaaS platforms are focused on delivering a fantastic user experience. Using them allows you to implement Generative AI functionalities without being a machine learning expert.

Low-Code Tools

They ensure that your app is both powerful and user-friendly. Various models are available, whether you want to build an app for text generation or with enhanced artistic capabilities. Scale and SnapLogic are popular Generative AI low-code SaaS tools.

Alternative Platforms

Lamatic, Cohere, and Flowise are other such tools. These platforms have extensive documentation and community support to help you build a GenAI app.

Although you might encounter limitations (in one form or another), as HBR suggests, these SaaS tools streamline app development. They help integrate different modules and frameworks to build a cohesive GenAI app.

Building GenAI Applications with Third-Party APIs

Using third-party AI APIs is another way to build customized Generative AI applications. These APIs offer advanced generative models to integrate into your project. Each generative AI tool offers unique features that can be incorporated into your project to enhance its capabilities.

Although APIs can be more complex to implement than SaaS tools, they can significantly reduce development time. Over three-fourths of developers use some APIs in their day-to-day development.

Popular APIs

OpenAI, Gemini, and Anthropic are the most popular APIs for Generative AI low-code development. They offer state-of-the-art GenAI models that generate custom text and images. Apart from those, ElevenLabs and Synthesia are among the few that provide voice-generation capabilities.

To build video-generation apps, you might want to try HeyGen AI. The integration process usually starts with signing up for API keys from their respective sites. Once you have the key, you need to set up your development environment.

API Integration

You can start making API calls to access the functionalities when everything is in place. This can be a code-intensive process at times. Different vector databases, frameworks, and user interface modules come with their own processes for developing Generative AI apps.

Clarity on what app you want to build makes choosing the right Generative AI developer tools easy. A proper setup ensures a seamless incorporation of GenAI functionalities in your app. You can connect different APIs for building Generative AI apps.

Building GenAI Applications with Agent Frameworks

Large language model (LLM) frameworks are also handy Generative AI developer tools when building apps. These frameworks often come with a new AI system that enables advanced functionalities like creating realistic images from natural language descriptions.

These frameworks are trained on huge sets of data to understand natural language and enhance user interactions. They can make your app smarter and more intuitive. These frameworks provide pre-trained models you can fine-tune for your specific needs.

LLM Frameworks

Once you install a framework and set up your development environment, you can explore their functionalities. You can also integrate them with other tools and databases (via APIs) to enhance their capabilities. LangChain, for instance, makes it easy to build apps powered by popular LLMs like:

Anthropic
OpenAI

It’s an open-source framework with several tools to help you manage and deploy complex language models. LlamaIndex and Cohere are similar data frameworks for building LLM applications. Although building GenAI apps with LLM frameworks can be as code-intensive as with APIs, the documentation is usually comprehensive.

Development Tools

They provide plenty of examples and tutorials to help you get started. You can build automated AI-based intelligent applications with just a few lines of code.

These GenAI dev tools provide advanced development and improved user experience, making your applications more engaging and effective.

Lamatic: A Powerful Managed Generative AI Tech Stack

Lamatic offers a comprehensive managed generative AI tech stack. Our solution provides:

Managed GenAI Middleware
Custom GenAI API (GraphQL)
Low Code Agent Builder
Automated GenAI Workflow (CI/CD)
GenOps (DevOps for GenAI)
Edge deployment via Cloudflare workers
Integrated Vector Database (Weaviate)

Generative AI App Builders vs. Building AI Features Into Apps

man working on new tools - GenAI Applications

If you're looking to build apps with AI features, say, you want to develop an app that summarizes text or turns it into audio—there are many options you can look at. You can build your own chatbots with Zapier's AI chatbot tool.

Start by telling it what to do, connect knowledge sources to ground it on your data, and share it with others. It's great for implementing robust internal tools, fast. OpenAI's GPTs offer similar possibilities. You can customize ChatGPT for unique cases, such as:

Creative writing
Handling negotiations
Anything else you can come up with

No-Code Tools

One step more complex, there's Glide. It's a no-code app builder that offers AI-powered interface components. When you add them to your app, you can generate text, extract text from images, or render audio into text.

If you want to create a unique app with custom AI features, you can do so with any app builder supporting API connections. Choose the AI model provided.

Advanced Customization

OpenAI, Anthropic, or a Hugging Face community model, among others, get the API keys and plug them into the app editor. Then, set up the calls and how they're received on your app.

This option is more advanced, but you can create any kind of AI-powered app with complete freedom.

Building Apps With Generative AI Is A Speed Game

Building apps with AI is still in the early days, but surprisingly powerful tools are already out there. It won't get you across the finish line with just a prompt, but AI app builders give you a good speed boost to get you there.

You can deploy apps faster or use the extra time to work on the more important features, improving your apps' quality over time.

How Will Generative AI Contribute to Business Value?

how AI contributes to business value - GenAI Applications

Generative AI provides new and disruptive opportunities to increase revenue, reduce costs, improve productivity, and better manage risk.

It will soon become a competitive advantage and differentiator. Gartner divides the opportunities into three categories.

Revenue Opportunities

Product Development

Generative AI will enable enterprises to create new products more quickly. These may include:

New drugs
Less toxic household cleaners
Novel flavors and fragrances
New alloys
Faster and better diagnoses

New Revenue Channels

Gartner research shows that enterprises with greater levels of AI maturity will gain greater benefits to their revenue.

Cost And Productivity Opportunities

Worker Augmentation

Generative AI can augment workers’ ability to draft and edit text, images, and other media. It can also:

Summarize, simplify, and classify content
Generate, translate and verify software code
Improve chatbot performance

At this stage, the technology is proficient at creating a wide range of artifacts quickly and at scale.

Long-Term Talent Optimization

Employees will be distinguished by their ability to conceive, execute, and refine ideas, projects, processes, services, and relationships in partnership with AI.

This symbiotic relationship will accelerate time to proficiency and greatly extend the range and competency of workers.

Process Improvement

Generative AI can derive real, in-context value from vast stores of content that may have gone largely unexploited until now. This will change workflows.

Risk Opportunities

Risk mitigation

Generative AI’s ability to analyze and provide broader and deeper visibility of data, such as customer transactions and potentially faulty software code, enhances pattern recognition and the ability to identify potential risks to the enterprise more quickly.

Sustainability

Generative AI may help enterprises comply with sustainability regulations, mitigate the risk of stranded assets, and embed sustainability into:

Decision-making
Product design
Processes

Which Industries Are Most Impacted By Generative AI?

Generative AI will augment core processes with:

AI models
Affecting the pharmaceutical
Manufacturing
Media
Architecture
Interior design
Engineering
Automotive
Aerospace
Defense
Medical
Electronics
Energy industries

Industry Disruption

It will impact marketing, design, corporate communications, training, and software engineering by augmenting many organizations' supporting processes. For example:

By 2025, more than 30% of new drugs and materials will be systematically discovered using generative AI techniques, up from zero today. Generative AI looks promising for the pharmaceutical industry, given the opportunity to reduce costs and time spent on drug discovery.
We predict that by 2025, 30% of outbound marketing messages from large organizations will be synthetically generated, up from less than 2% in 2022. Text generators like GPT-3 can already be used to create marketing copy and personalized advertising.

In the manufacturing, automotive, aerospace, and defense industries, generative design can create designs optimized to meet specific goals and constraints, such as:

Performance
Materials
Manufacturing methods

This accelerates the design process by producing various potential solutions for engineers to explore.

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