What is Generative AI?

Understanding generative AI

In an era where applications like ChatGPT have rapidly gained millions of users and showcased impressive expertise across various topics, the concept of generative artificial intelligence (Gen AI) has created a lot of buzz, especially in the digital workplace. As we explore the capabilities of generative AI, it’s important to understand its history and development.

Related: Discover how AI can help create a modern employee experience

A brief history of generative AI

Generative AI has its roots in the early days of artificial intelligence, dating back to the 1950s. The first neural networks were developed during this time, laying the groundwork for future advancements. One of the earliest examples of generative AI was the ELIZA chatbot, created in 1961, which could simulate conversations using simple pattern-matching techniques. 

However, in 2014, generative AI began to evolve with the introduction of generative adversarial networks (GANs) by Ian Goodfellow. GANs consist of two competing neural networks: one generates content while the other evaluates its authenticity. 

Since then, generative AI has rapidly advanced, with significant milestones including developing variational autoencoders (VAEs) and introducing large language models like OpenAI’s GPT series. These advancements have enabled generative AI to produce coherent text and stunning visuals, transforming businesses’ operations and enhancing user experiences.

How generative AI works?

Generative AI uses machine learning models, particularly generative models, which analyze vast amounts of training data to understand underlying patterns and structures. When a user provides a prompt—be it text, images, or other forms of input—the AI generates corresponding outputs, such as essays, images, or even music. This process often involves advanced techniques like generative adversarial networks (GANs) and transformers, which help the AI refine its outputs for realism and relevance.

For instance, in text generation, the AI predicts the next word in a sentence based on the context provided by previous words, allowing it to create coherent and contextually relevant responses. Similarly, in image generation, the AI learns from thousands of images to produce new visuals that resemble the training data while still being unique.

What generative AI can create?

Generative AI can create a wide range of outputs. Each creation often involves using existing data to learn and simulate human-like creativity and innovation. 

• Text and content: With generative AI, you can generate everything from instructions and documentation to brochures, emails, website copy, blogs, articles, reports, papers, and creative writing. It can also automate repetitive tasks or tedious tasks, such as drafting document summaries or creating meta descriptions for web pages.
• Image and video synthesis: Generative AI can create realistic images based on textual descriptions. A well-known example is DALL-E, developed by OpenAI, which generates images from prompts. Users can input a description, and DALL-E will produce unique images matching the criteria.
• Sound, speech and music: Generative AI creates sound, speech, and music by analyzing existing audio data and learning patterns. It uses techniques like tokenization to break down audio into manageable pieces, allowing it to generate new sounds based on prompts. For example, models like OpenAI’s Jukebox can compose original music, while DeepMind’s WaveNet generates realistic human speech. 
• Software code: Generative AI can produce software code by understanding natural language descriptions of what the code should do. For example, tools like GitHub Copilot and Google’s Gemini allow developers to write code more efficiently by suggesting snippets or entire functions based on user input.

Related: Read our blog on ethical AI in software development

Generative AI models and how they have evolved

Generative AI models have undergone significant evolution over the past decade. Initially, it relied on simpler algorithms and smaller datasets, but today, it encompasses a much broader spectrum.

Here’s a rundown of some of the most important generative AI model innovations:

• Variational autoencoders (VAEs): Introduced in 2013, VAEs consist of two neural networks. One encodes or compresses a huge amount of unstructured training data into parameters, and the other decodes those parameters to reconstruct the content. Although autoencoders can generate new content, they’re more useful for compressing data for storage or transfer. VAE applications include image generation, anomaly detection, and natural language processing. 
• Generative adversarial networks (GANs): Developed in 2014 by Ian Goodfellow, GANs consist of two neural networks—a generator and a discriminator—that work against each other. The generator creates new content while the discriminator evaluates its authenticity. This adversarial training process has led to advancements in generating realistic images, videos, and audio, making GANs a cornerstone of modern generative AI.
• Diffusion models: Introduced in 2014, diffusion models operate by initially adding noise to the training data until it becomes completely random and unrecognizable. The algorithm is then trained to gradually reverse this process, refining the noise to uncover a clear and desired output. This method has proven to be very effective for generating realistic images. DALL-E and Open AI’s image-generation tool are examples of this model.
• Transformers: Transformers are a type of neural network architecture that excels at processing sequential data. They have become the backbone for many large language models (LLMs), such as OpenAI’s GPT series.

Benefits of generative AI

Working on mundane and repetitive tasks can be draining and time-consuming for employees. Generative AI can speed up the process, offer valuable insights and improve employee productivity and efficiency. Here’s how generative AI creates measurable advantages:

Automates content creation

One of the primary use cases many businesses see with generative AI is with content creation. With an optimized AI prompt, you can generate articles, social media posts, product descriptions, email copies and more in just minutes. Additionally, generative AI can adapt content into the format best suited for each individual. This advancement holds immense significance for creating communications that deeply resonate.

Increases employee productivity

According to a study, GenAI can improve a worker’s performance by nearly 40% compared to those who do not use it. Using employee-facing applications such as AI-powered enterprise search, writing assistance and workflow automation can really make an immediate difference in terms of productivity.

Related: Learn more about using GenAI to increase productivity

Hypersonalization

By enabling hyperpersonalization, generative AI enhances employee communications, ensuring that messages resonate more with individuals. In fact, according to a study, 89% of leaders believe personalization is crucial to their business’ success in the next three years. Adding to this, Carolyn Clark, VP of Employee Experience Strategy at Simpplr, stated:

I truly believe if we can start to personalize the way we communicate, if we can authentically give people things in the way they need to learn it, they’re going to consume it more.

Inspires creativity and innovation

Generative AI inspires creativity and innovation by assisting in idea generation and problem-solving. It provides new perspectives and insights by analyzing data and patterns, encouraging teams to explore uncharted territories. Designers, writers, and artists, for instance, use generative AI to brainstorm creative concepts and prototypes, pushing the boundaries of traditional thinking and creating groundbreaking work.

Adaptive learning

Generative AI systems are designed to learn and adapt continuously by incorporating feedback and new data. This ability enables the models to enhance their performance over time, producing outputs that align more closely with user preferences and goals. For example, in personalized education, generative AI can dynamically adjust learning and training materials to suit each student’s unique needs and learning styles.

Saves time and reduces cost

Because of their speed, generative AI tools save time and cost. It reduces errors, minimizes downtime, and identifies redundancies and other costly inefficiencies within workflows.

However, it’s important to note that generative AI can sometimes produce inaccurate information, commonly referred to as “hallucinations.” Therefore, human oversight and quality control remain essential to ensure accuracy and maintain high standards.

Drives digital transformation

Generative AI is a catalyst for digital transformation within organizations. By integrating AI-powered solutions, companies can automate processes, improve decision-making, and enhance customer experiences. This transformation aligns with the evolving digital landscape, ensuring internal communication teams remain strong and businesses remain agile.

Related: Read our blog on internal communications and digital transformation

Challenges of generative AI

While generative AI offers numerous benefits, it also presents several challenges that must be addressed to ensure its ethical and responsible use. Here are some of the key challenges:

• AI threatens data privacy and security: Generative AI systems require vast datasets, raising concerns about the potential exposure and misuse of sensitive information during training. Implementing robust data anonymization and encryption techniques is crucial to mitigate these risks.
• AI generates inconsistent output: While AI can generate output in minutes, the quality and relevance of that output can vary significantly. For instance, an AI model might produce a well-written article one time and a less coherent or off-topic piece the next. Such variability can undermine trust in the technology and complicate its integration. 
• AI gets influenced by biases: Generative models may learn societal biases present in the training data, leading to outputs that reflect stereotypes or unfair assumptions. To prevent biased outputs from their models, developers must prioritize diverse and representative datasets and implement regular audits to identify and mitigate biases in AI-generated content.
• AI raises intellectual property issues: Using generative AI brings up important questions about copyright and who owns the content it creates. Since AI can generate text, images, or music based on existing works, it can be unclear who has the rights to that new content. This can lead to legal challenges and confusion about ownership.

Combining GenAI within a modern intranet

GenAI exists in standalone tools, such as ChatGPT or Bard, but it can also be incorporated into existing software like a modern intranet. And this effective integration can empower employees to interact with content in a more meaningful and individualized way. Organizations that embrace AI within the right intranet platform experience improved operational efficiencies.

While AI is still emerging for many IPS competitors, Simpplr offers fully functional and integrated AI applied to a wide range of functions.

According to 2024 Gartner® Magic Quadrant™ for Intranet Packaged Solutions report:
“Simpplr is a Leader in this Magic Quadrant. Its product provides a unified, personalized employee experience, irrespective of work location. Available in a multitenant SaaS deployment model, it offers a comprehensive intranet experience out of the box for desk, frontline and contingent workers, encompassing social, communication, collaboration and knowledge management tools.”

Simpplr’s AI acts as an invisible ally for employees. Being a proud member of the Responsible Artificial Intelligence Institute, it is committed to use AI responsibly to deliver an amazing employee experience and reduce HR helpdesk tickets by giving your employees everything they need to do their best work.

How to improve employee experience using GenAI with Simpplr

With purpose-built, platform-wide AI capabilities powering every aspect of the employee experience — from personalization to automated issue resolution — Simpplr ensures that employees have everything they need to thrive in one centralized location.

Here are a few extensive AI capabilities that only Simpplr offers in the EX platform space:

• Auto-governance: Keeps the intranet a reliable and secure hub for communication, collaboration and knowledge-sharing. Simpplr launched the industry’s first auto-governance engine to combat the challenge of stale intranet content — a top reason why intranets fail. It ensures that the information employees find on the intranet is always fresh, trustworthy and inoffensive — fostering a high level of engagement and trust.

• Adaptive personalization: Unlike traditional rules-based systems, Simpplr’s user-driven adaptive personalization ensures that relevant information finds its way to the right person at the right time and fine-tunes the intranet experience.

• Insights and analytics: For HR and leadership executives, creating a company culture where employees feel heard and understood is crucial. Therefore, a modern intranet like Simpplr incorporated employee listening tools by combining survey responses with AI-powered passive listening to detect underlying sentiments and emotions — so leaders can take data-driven action to keep morale high and retain talent.

• Intelligent search: The time an employee spends looking for information is wasted. And that wasted time gets filled with frustration, resulting in a negative employee experience. This feature combines federated and neural search to quickly deliver relevant, accurate, personalized and conversational search results — reducing frustration and improving productivity.

• Writing and virtual assistance: Our AI-powered writing and virtual assistance empower employees at all levels across the organization. Powered by genAI, you can use these tools to elevate and scale the writing process, get personalized answers securely and complete simple tasks within seconds.

Putting it all together

Generative AI is no longer just for tech giants or cutting-edge companies. It continues to evolve, influencing various industries by enabling new forms of content creation and innovation. With innovative tools like those integrated into Simpplr’s intranet, employees across various departments—be it communications, IT, operations, or human resources—can leverage generative AI to streamline their workflows. 

Discover how Simpplr, an AI-powered employee experience platform, can streamline workflows and improve the employee experience within your organization. Request a demo today!