Generative AI explained - WRITER

Generative AI explained

The complete guide for enterprise business leaders

Introduction

Generative AI is positioned to transform everything you can imagine about work and business, and the pace of innovation is only going to increase.

Generative AI isn’t just another tech buzzword — it’s reshaping the business landscape as profoundly as the internet did. Analysts project that with generative AI alone, the global economy will get an immense boost of $7.9 trillion annually, apart from the $17.7 trillion of economic value that non-generative AI contributes yearly.

If you’re an enterprise exec, you’re probably feeling the pressure to embrace generative AI from all sides. A recent study from IBM shows that it isn’t just competitors or consumers pushing for generative AI adoption — business leaders surveyed say that employees, board members, and investors are demanding to see‌ AI strategies in place.

In this new era, effective business leadership will require having a solid grasp of generative AI. You’re going to need to understand the risks and rewards, work AI into your digital transformation strategy, and act as a steward for responsible, ethical AI implementation and adoption. But fear not: you don’t need a PhD in data science to wrap your head around generative AI.

We’ve teamed up the creative prowess of our human writers with the capabilities of our generative AI platform, WRITER, to break down complex concepts into plain, no-nonsense language. This collaboration means you’ll get quick answers to your burning questions, along with detailed breakdowns that’ll make you the ultimate AI expert in the boardroom.

With this comprehensive guide, you’ll gain the knowledge and insights needed to navigate the enterprise generative AI landscape. Stay ahead of the curve, unlock new opportunities, and position your business for success in the era of AI-driven innovation. So, let’s dive in and conquer the AI universe, one bold decision at a time.

Generative AI, LLMs, training data, and RAG in plain language

Let’s begin by establishing quick definitions for some of the key concepts related to generative AI in the enterprise.

Generative AI

Generative AI uses natural language processing (NLP) and machine learning (ML) to create data or content that looks like it came from a human. What makes it different from other ML technology, like search algorithms, is that it doesn’t just recognize and categorize data. It can also create something that’s new and original, often in the form of writing, artwork, or music.

It’s this ability to come up with something new that makes generative AI so powerful and exciting. It has the potential to totally transform industries across the business landscape and create incredible new experiences for people.

Machine learning

Machine learning (ML) is a subset of artificial intelligence (AI) that enables computers to learn from data without being explicitly programmed. In an enterprise context, machine learning holds immense relevance for business leaders. It empowers organizations to make smarter decisions by predicting trends, understanding customer behavior, and identifying business patterns.

Large language models (LLMs)

Large language models (LLMs) are the intelligence behind generative AI. They use machine learning algorithms to find patterns and structures in language, allowing them to create new natural language text.

Training data

Training data refers to the information and examples used to teach an AI model how to generate new content. This data is crucial for the model to learn patterns, understand context, and produce output that aligns with human-like quality.

GPT-4

GPT-4 is a powerful machine learning model developed by OpenAI, makers of ChatGPT. It’s their fourth iteration of a type of LLM known as a Generative Pre-trained Transformer (GPT).

Palmyra LLMs

Palmyra LLMs are the enterprise-grade foundational models that power the WRITER generative AI platform.

Retrieval-augmented generation (RAG)

Retrieval-augmented generation (RAG) is a natural language processing approach that combines information retrieval and text generation to generate human-like text.

Separating generative AI hype from reality

Before the end of 2022, some individuals and companies were dipping their toes into the generative AI early adoption waters, but their exploration was happening quietly. Then OpenAI’s chatbot tool, ChatGPT entered the … well … chat.

What’s hype

In the business world, the cacophony of ChatGPT chatter has led many professionals to believe (mistakenly) that OpenAI’s chatbot encompasses all generative AI technology.

What’s real

Generative AI is far from a fleeting trend. It has arrived and it’s here to stay. And its applications go far beyond ChatGPT.

The anatomy of full-stack generative AI

Before realizing the benefits of a full-stack generative AI solution, enterprise leaders might try out different approaches.

Full-stack generative AI

A full-stack generative AI platform for enterprise companies consists of several essential components that work together to provide a comprehensive solution. These components include:

How can our business use generative AI right now?

Many organizations are open to exploring generative AI, but before they invest, they want to see real evidence of their value and effect.

The key capabilities of enterprise generative AI

Enterprise generative AI offers a comprehensive set of capabilities that cater to the diverse needs of organizations.

What influence will generative AI have on our customers?

Generative AI will become an intrinsic part of our lives. It has the potential to significantly alter the way people behave, how they come to conclusions, and how they engage with their surroundings.

For general consumers: conversational interactions everywhere

If you’re an exec at a consumer brand or public-facing organization, consider how your customers’ lives will change now that generative AI is increasingly a part of daily life.

For B2B customers: industry-specific solutions everywhere

If you’re a leader of a B2B organization, identify the industries that your top customers are in. Consider the services they offer and how generative AI might change how they go about delivering those services.

What are the business risks of using generative AI — and how do we avoid them?

Risk mitigation is necessary for adopting new technology, and that’s equally true about generative AI.

Countermeasures

Enterprise-grade AI will give you brand-specific output and security features that your IT teams will insist on …

How do we train our team to use generative AI?

Rolling out new technology takes employees’ time away from other projects for training.

Use generative AI tools where your team already works

The most straightforward way to get team members started on using generative AI? Let them try it out in the tools they already use.

Train “AI whisperers” on your teams

Knowing how to write an effective AI prompt is crucial when working with generative AI.

How can we get the most out of a generative AI investment?

To maximize the return on investment (ROI) of your generative AI implementation, it’s essential to follow a strategic approach. Here are some key steps to help you get the most out of your generative AI solution:

By following these steps, you can ensure that your generative AI investment delivers tangible benefits, such as increased productivity, improved content quality, and better decision-making capabilities.