Generative AI Benefits for Business

It’s been a year since ChatGPT, a generative AI chatbot, became an inherent part of our lives. Artificial intelligence and machine learning solutions are accessible and can be used not only by developers but also by ordinary users. Copywriters, designers, and programmers often hear the question — “Are you worried that ChatGPT will put you out of a job?”. It’s partly true because ChatGPT can write a blog post, product description, or even code in 10 seconds.

Current generative AI technologies are capable of automating labor activities that today take up 60-70% of employees’ time, a McKinsey report declares. Still, generative AI is not a panacea — it may also provide fictional or incomplete data and even cause data leaks. These are the reasons why generative AI cannot yet replace human employees.

However, with a competent approach, generative AI solutions can facilitate and improve your business. In this article, we will go into more detail about what generative AI benefits there may be in store for your company.

What is Generative AI?

Generative AI is an artificial intelligence technology that creates new content — code, text, image, audio, video, or other data types humans can produce. It does this by analyzing and capturing patterns from data.

Generative AI models could be based on one of three deep learning architectures: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers.

GANs can generate creative, and at the same time, realistic outputs, based on the competitive output of two neural networks — the“generator” and the “discriminator”. The generator generates the image, and the discriminator tries to recognize it.

VAEs also consist of two neural networks — the “encoder” and the “decoder”. The first one analyzes the data, and the second one produces something similar on the basis of the resulting array.

Transformers learn to understand the context of text, thanks to the “self-attention” mechanism.

One of the generative AI model’s benefits is the ability to use different learning approaches for training, including unsupervised or semi-supervised learning.

Among the most popular generative AI examples are ChatGPT, DALL-E, Bard, Scribe, Jasper, GitHub Copilot, and AlphaCode.

Even so, generative AI capabilities for advanced writing and visuals are still in the early stages. AI models often produce “weird” content with bias or hallucinations. With the various models learning fast, generative AI has big potential to transform businesses.

Learn more about how generative AI works.

Why Is Generative AI Important for Businesses?

Generative AI’s impact on productivity can make a significant change to the way we work.

Gartner predicts that 30% of enterprises will have AI-augmented development and testing strategies in place by 2025. This is a significant increase from the 5% adoption rate in 2021.

About 75% of the value that generative AI use cases could deliver falls across four areas: customer operations, marketing and sales, software engineering, and R&D (McKinsey). Banking, high-tech, and life sciences are among the primary industries that can significantly increase revenue by fully implementing generative AI technology.

For example, if generative AI technology is fully implemented in the banking sector, it could add additional revenue of $200 billion to $340 billion per year. In retail trade and consumer goods production, the potential effect is also significant — a potential revenue increase from $400 to $660 billion a year.

Generative AI is important because it is transforming the way businesses operate through automation, personalization, and data-driven decision-making. It makes generative AI a must-have for businesses to thrive in the digital era.

We will discuss generative AI benefits in more detail in the next section.

How Do Businesses Benefit from Generative AI?

Generative AI could be widely used in various areas of business. Here we highlight five main generative AI benefits, which companies could take advantage of.

Use Data for Better Decision-Making

Generative AI is a source for more intelligent decision-making: it transforms insights previously buried in the data.

We can see it clearly in the retail industry, where generative AI algorithms uncover valuable insights by analyzing customer reviews, social media sentiment, and market trends.

Amazon summarizes customer reviews with the help of generative AI. It provides a short paragraph on the product detail page that highlights product features and customer opinions often mentioned in written reviews to help shoppers determine at a glance whether a product is right for them.

Enhance Customer Service

Customer service is a pretty broad area where generative AI could be applied, so here are the most common cases with generative AI benefits:

• AI chatbots. Implementing AI chatbots on websites and messaging platforms has become a common practice for many companies. These chatbots operate 24/7, responding to typical customer queries.
• Virtual assistants. Siri, Alexa, and Google Assistant answer ordinary users’ questions, provide product recommendations, fetch information from the Internet, and so on.
• Sentiment analysis. Generative AI can analyze reviews and comments on social media and transform them into insights about customer sentiment and satisfaction. Airbnb, for example, uses sentiment analysis to track guest feedback and improve user experience.

Simplify Data Exploration and Analysis (Talk To Your Data)

Talk-to-your-data generative AI applications are chatbots trained on the company’s databases, documentation, and policies to respond to customer support requests. Such applications
are among the most common inquiries from corporate companies.

The Talk To Your Data approach simplifies the processes of data exploration and analysis. Data becomes more understandable and accessible to people without special data science or analytics knowledge. Business users can get valuable insights without complex queries or coding.

For example, ask the chatbot questions like, “What were the total sales last month?” or “Which product sold the most?” The generative AI understands your questions and provides you with the relevant answers from the data.

The main benefit of talk-to-your-data generative AI applications for business is the ability to interact with the AI and extract insights from data through natural language conversations.

Optimize Software Engineering

Among crucial generative AI benefits in software engineering are automating tasks, improving code quality, and security. Generative AI solutions speed up development, minimize errors, and allow developers to focus on creative tasks. Take a look at the following generative AI benefits for software engineering:

• Code writing: OpenAI’s Codex and GitHub Copilot tools can assist developers in writing code.
• Code review, optimization, and refactoring recommendations: Generative AI analyzes codebases using advanced algorithms and machine learning techniques to recommend refactoring. Gen AI tools could be used for efficient code analysis and improvement suggestions.
• Bug detection: DeepCode and CodeWhisperer tools can detect potential software bugs, security vulnerabilities, and code quality issues.
• Automated testing: Generative AI can automatically generate test cases and scenarios, e.g., Diffblue Cover writes unit tests for Java applications automatically.

Fast content creation and personalization

Automated content creation and personalization are the quintessential generative AI benefits. In media, entertainment, content production, and distribution, it’s a must-have nowadays. Here we explain this generative AI benefit in more detail:

• Copywriting and marketing: With ChatGPT, you can generate blog posts, social media content, emails, SEO-optimized content, product descriptions and reviews, and more.
• Visual content: Midjourney, Canva, and Adobe’s Sensei provide various templates and options, so even non-designers could create high-quality visuals for marketing, social media, and other communication needs.
• Video content: Lumen5 and Runway video creation platforms implement generative AI algorithms to generate video content from text or images. Lumen5’s AI can automatically create an engaging video based on news posts. It would have a story summary, complete with visuals, animations, and voiceovers.
• Music and digital art: MuseNet and OpenAI’s DALL-E can generate music compositions and create unique digital artworks.

Reduce Employee Workload

Automation is one of the most critical generative AI benefits. Let’s have a look at how it works in real-life generative AI use cases.

In general, financial institutions use AI models to automate financial report analysis, identify trends, and make investment recommendations.

Amazon uses generative AI to forecast inventory in different locations and to develop optimal last-mile delivery routes for drivers. The process for creating product pages is also simplified; the third-party sellers provide minimum information, with the models doing the rest.

Generative AI tools like Microsoft Copilot, a knowledge base chatbot, help to reduce workload significantly through automating tasks and content creation. This generative AI assistant is fully integrated with other Microsoft products such as Word, Excel, PowerPoint, Teams, and Outlook. It also comes with a new chat experience called Business Chat.

Cost Savings

As more and more companies use generative AI, they find that it’s a great way to save money and stay competitive in today’s fast-changing business world.

With generative AI, businesses can automate routine tasks and spend less money on special software and equipment.

This automation extends to functions ranging from content creation and customer support to data analysis and software development.

Generative AI Challenges for Business

Despite the generative AI benefits outlined earlier, its implementation raises serious concerns. In addition to traditional AI implementation challenges, such as possible scalability issues and failure to replicate the achieved results outside controlled environments, the Exadel AI team singles out three challenges that are unique to generative AI:

Hallucinations

Generative models may fabricate information; this phenomenon is also known as hallucinations. It means that AI models generate content that sounds plausible, but is entirely fictional or has no factual basis. For example, ChatGPT can produce fake news if you give it misleading information. A fact worth recalling: Google’s Bard chatbot incorrectly claimed that the James Webb Space Telescope had captured the world’s first images of a planet outside our solar system.

To prevent the risk of hallucinations, our specialists recommend training AI models on high-quality data, with incorporated human feedback, in conditions of transparency and continuous quality control. Model fine-tuning and prompt engineering techniques should be included as well.

Quality Assurance

Guaranteeing the quality of generative AI outputs can be difficult as the ‘correct’ output can vary greatly. Quality assurance is challenging for generative AI due to the risk of generating inaccurate or biased content. Generative AI models can produce content that may contain factual errors, misleading information, or unintended biases. So, QA processes should ensure the reliability and ethical use of AI-generated output.

To cope with this generative AI challenge, our AI team recommends covering this functionality with unit tests, very similar to usual software development. OpenAI models change over time, so it’s crucial to check whether its responses have also changed. GPT could be also used as a part of those tests.

Prompt Hack

Prompt hacking, as a generative AI challenge, refers to attacks for Large Language Models (LLM)-based tools to generate malicious or harmful content through specifically-created input prompts. Without proper control, AI models can generate harmful content. Preventing prompt hacking requires ongoing vigilance, robust safeguards, and ethical guidelines to ensure that AI systems produce safe output.

To mitigate prompt injection and leading, the Exadel AI team provides a defensive framework that reduces the overall risk of malicious content appearing in your inputs and outputs. It includes implementing intelligence-driven defense, regularly monitoring LLM behavior and outputs for unusual activity, and using fine-tuning and other techniques.

Exadel’s Case Study: How One Consulting Company Tapped Benefits from Generative AI with A Tax Research Tool

At Exadel, our AI specialists provide reliable solutions that are aligned with your business needs. Here are four steps to implement a generative AI strategy:

1. Discover the potential of Generative AI
2. Identify use cases for your company
3. Build a technology strategy
4. Get started with high-ROI use cases

We want to share a real-life use case that shows how a company benefited from using a generative AI tool. Our client, a global consulting firm, has more than 8,000 consultants who each used to spend around 25 minutes on a single research task. The client wanted to use generative AI to automate the research process and reduce their employees’ workload.

Our specialists faced a couple of generative AI challenges while working on that case. First of all, our specialists had a massive amount of data to work through (600,000 documents or about 10GB). The second stumbling block was the unique language of tax data, which meant that standard search algorithms needed to be revised. The third generative AI challenge was to estimate the accuracy of the data, taking into account the complexity and high costs associated with such an investigation.

Our AI specialists selected some documents, artificially generated questions, and tested for data accuracy in the following manner: if the result was developed based on the document used to create the question, then it was correct. It had a 70% search accuracy. This is also an example of quality assurance in generative AI.

Our generative AI tool works in a similar way to ChatGPT. You ask a question, then move on to the optional stage when you can ask the user additional qualifying questions. Then the actual research starts, which takes around 10 minutes to complete. Finally, the user receives a detailed answer with the document links.

It is a custom hierarchical knowledge base and hybrid search engine. This generative AI solution is capable of effectively searching over 10GB of text data and saves 65% of the time required for research. The tool operates in two modes: online and research. The online mode provides real-time conversations. In research mode, there is an intelligent agent which can answer more complex questions.

To summarize the generative AI benefits that our solution yielded for the customer: they saved more than one million working hours, which translates into an estimated $43 million.

Generative AI Benefits for Business: Conclusion

The business benefits of generative AI are multi-faceted. From automating content creation to improving customer experience, generative AI significantly changes how businesses are organized. Implementing generative AI solutions is becoming a must-have for companies wanting to survive and outperform the competition.

When companies face the challenges of implementing generative AI, the key is to take a strategic approach that fits the organization’s needs. Check our next articles to learn more about generative AI use cases and how to get started with generative AI implementation.