What is generative AI?

With its ability to learn and replicate complex patterns, this type of artificial intelligence delivers smarter ways to create content. For example, GenAI can understand what makes a musical composition touching — and generate a song that mimics these qualities.

In fact, GenAI transforms the way people get creative and the way businesses operate, from accelerating research to automating tasks and beyond. According to the PwC Global CEO Survey, 68% of leaders report that GenAI technology has increased employee efficiency, while 44% have observed growth in profitability, and 35% have reported a boost in revenue.

To understand the importance and potential of this generative AI revolution, let’s look into its benefits, use cases, and challenges.

Generative artificial intelligence: Definition and evolution

GenAI is a subset of artificial intelligence centered on creating content, while other AI systems primarily focus on analyzing, categorizing, or predicting based on existing data. This technology combines computational power and advanced machine learning algorithms, including large language models (LLMs), that learn from vast datasets and create AI content such as text, images, videos, music, and even code.

Today, GenAI tools are popularly used to create memes and images online, but the technology behind this trend has roots dating back to the 1950s. In that era, researchers developed early neural network models capable of learning simple patterns, such as distinguishing squares from circles.

Since then, GenAI has evolved in terms of hardware, algorithms, and research, leading to significant development and adoption in recent years.

More recently, the launch of tools like ChatGPT has been a game-changer in the evolution of this technology, making advanced language models accessible to users around the world. With the rapid recent progress in LLMs, GenAI has gained popularity and found a wide range of uses, unlocking new possibilities for innovation and creativity.

How does generative AI work?

Behind the user-friendly generative AI platforms you know, there are advanced algorithms and machine learning models that identify patterns within existing data and then use these patterns to create new content. The process typically involves the following steps:

1. Data collection and preparation: The system gathers a large dataset relevant to the type of content it aims to generate and processes it to remove errors or inconsistencies
2. Model training: Generative AI models train on the dataset, which involves feeding them with data and allowing them to learn patterns and structures
3. Content generation: Once trained, GenAI uses its learned patterns to produce content — for some models, this might start with a prompt, while others can automatically generate text or images resembling those in the training set
4. Accuracy and enhancement: Retrieval-augmented generation (RAG), one of the key technologies enabling content generation, enhances information by integrating knowledge sources, such as documents — making the output more accurate and contextually relevant
5. Refinement and iteration: The system refines generated content through feedback and training to improve its quality and realism over time

Benefits of generative AI technology for enterprises

According to Gartner, GenAI has surpassed the peak of hype typically associated with emerging technologies, yet the buzz surrounding it remains strong. As you develop your enterprise AI strategy, you can integrate this technology into your existing systems, leaving behind expectations and bringing real benefits across various areas.

Here are the top benefits of generative AI for your business:

• Improved efficiency and productivity: A Deloitte survey shows that 34% of enterprises that adopted GenAI said higher efficiency and productivity were the most important benefits their organizations achieved. Enterprise generative AI tools automate time-consuming tasks like proofreading documents, freeing up your team to focus on other activities.
• Accelerated creativity and innovation: You can use GenAI as a virtual brainstorming tool and create dozens of ideas in a matter of seconds. These tools can also inspire the creation of new content, designs, and solutions that align with your customers’ needs.
• Greater data accessibility: GenAI helps you uncover insights hidden in your unstructured data, such as documents, images, PDFs, and other files that are isolated in content repositories and difficult to access. AI-powered tools allow you to easily locate files in the cloud by searching their content and even asking questions across multiple documents.
• Enhanced customer experience: In the past, interacting with chatbots proved frustrating because of their limitations in understanding queries. Today, thanks to AI, the conversations sound more natural. These systems integrate generative AI and conversational AI to interpret intent and deliver more personalized answers. GenAI also enables content and product recommendations, facilitating your customer journey.

Explore the factors fueling the growing adoption of artificial intelligence in enterprises.

Best generative AI use cases

The global generative AI market size is estimated to grow from 20.9B in 2024 to $136.7B in 2030, representing a CAGR of 36.7% in the forecast period. The countless possibilities of automating digital workflows, such as creating content in so many formats, drive the growth of generative AI solutions. But how do you take full advantage of this technology?

Generative AI adoption by businesses is a gradual process. Typically, you build your enterprise AI strategy based on areas and applications where this technology can deliver more value. Then, you measure their impact before expanding the scope of your AI applications.

Asana’s State of AI at Work reports that, on average, knowledge workers have deployed GenAI for five different use cases, while 52% of respondents said they use the technology every week.

Let’s look at the best applications of generative AI for businesses:

Explore ways to use AI for your business success.

Overcoming the challenges of generative AI

Despite GenAI’s possibilities, businesses often wrestle with data privacy, security, and ethical concerns, which can hold back the full adoption of this technology. To ensure the sustainable use of GenAI, make sure to:

• Scale your GenAI initiatives: Integrating GenAI solutions across different departments requires team training, policies to ensure smooth implementation and operation, and scalable data storage to accommodate increasing volumes of content. Consider AI-powered solutions that allow integration with software and applications you already use and plan gradual upgrades that maintain cost efficiency.
• Ensure data privacy and security: GenAI systems often require access to large volumes of data, which can include sensitive or personal information. Set data governance policies and consider platforms with end-to-end encryption, granular access controls, and other advanced security measures to protect information. Establishing the right permissions for accessing your content is critical for leveraging secure RAG while reducing the risk of AI inadvertently leaking information that the user may not be authorized to view.
• Manage ethics and responsibility: One of the business concerns with GenAI adoption is the use of data. Make sure you opt for solutions with AI principles. These platforms enable the secure and responsible use of AI in current and evolving technologies.
• Measure the ROI on AI: Evaluating the return on investment for GenAI projects requires aligning your business goals, your team’s contact with the technology, and the impact on efficiency and customer satisfaction. Define KPIs for evaluating the performance of your overall AI strategy and track both quantitative and qualitative outcomes to maximize the value of your investments.

How can generative AI and RAG address enterprise challenges?

Integrating retrieval augmented generation with GenAI can enhance the accuracy and relevance of generated content. And it doesn’t just mean providing more accurate responses to your queries. By leveraging RAG, you can improve your ability to find and analyze information across document collections. For example, RAG helps identify specific data points within contracts, extract key information from research papers, or locate relevant sections in legal documents.

Here’s how generative AI and RAG address challenges enterprises face:

• Content management: RAG improves content management and accessibility by retrieving relevant data from vast, distributed systems and integrating it into the content generation process, ensuring AI outputs are up to date and contextually relevant.
• Customer support and engagement: Generative AI, enhanced by RAG, creates precise and personalized responses in real time. By accessing FAQ documents and knowledge bases, AI enhances the efficiency and effectiveness of your interactions.
• Content creation and marketing: GenAI automates the creation of high-quality, relevant content such as marketing materials, product descriptions, and reports. With RAG, this content is enriched with data and insights, ensuring it is creative and accurate.
• Quick decision-making: RAG pulls relevant data from multiple spreadsheets, documents, and presentations, providing real-time insights and detailed analysis. It enables more informed and timely business decisions.

Discover how

Implement secure AI with Box

Box AI brings new intelligent capabilities to help you integrate advanced generative AI models into your operations — leveraging RAG in a secure and permissions-integrated manner. With the Intelligent Content Cloud, you transform content management by automating critical processes, enabling easy collaboration on documents and uncovering valuable insights from your unstructured data.

Enterprise Plus plan users have unrestricted access to Box AI features, including:

• Unlimited AI queries and deep insights in Box Notes and documents (extract value from your data without unexpected expenses)
• Box Hubs, which integrates GenAI with RAG models, allows you to ask questions across multiple files, get instant answers, summarize information, and compare documents in seconds
• Box AI API — Extract, enabling extraction of key information to populate metadata fields
• Content creation capabilities that streamline the drafting of personalized emails, meeting agendas, and other text-based formats with ease
• AI principles that ensure complete control over AI usage, strict adherence to permissions, and trustworthy AI models

Contact our team to discuss the best enterprise generative AI solutions for your business.

While we maintain our steadfast commitment to offering products and services with best-in-class privacy, security, and compliance, the information provided in this blog post is not intended to constitute legal advice. We strongly encourage prospective and current customers to perform their own due diligence when assessing compliance with applicable laws.