What is generative AI? Meaning and examples in business

For hundreds of years, revolutionary industrial inventions were bulky and formidable, forged of iron and steel. Today, however, the most transformative enterprise technologies are invisible – residing within the digital foundations that power modern business. Generative AI is just such a tool. And the secret to getting the most out of it is recognizing that it’s just that: a tool. It’s not a magic bullet – and like any good tool, it is ideally suited for some purposes and not others. So, while GenAI is no replacement for human creativity or strategy, its instant and awe-inspiring ability to deliver useful output from information, patterns, and disparate data sets make it a boundlessly powerful business asset. Especially in the hands of experienced teams and creatives who know how to leverage it with care and skill.

Generative AI is a subset of artificial intelligence that is specifically designed to produce new content – including text, images, videos, code, and audio – by learning patterns from vast amounts of existing data. Unlike traditional AI systems, which primarily focus on analyzing or interpreting existing information, generative AI actively creates new outputs based on its learned understanding of data structures and relationships. 

GenAI systems learn by studying enormous amounts of existing data such as text, images, audio, or code. This information is then stored within its neural network. And it’s no coincidence that the term “neural network” sounds a bit eerily human because in fact, this computational model was indeed inspired by our own brain structures. The AI neural network consists of interconnected layers of artificial “neurons” that process and input all the data that is taken in. 

Depending on the nature and purpose of the GenAI tool, there can be various algorithmic models used in this process. But in a nutshell, as the AI goes through repeated learning cycles, it increasingly recognizes various patterns and relationships. Throughout this iterative process, these networks learn to adjust and refine their connections, leading to their ability craft accurate better and better outputs over time. And when a GenAI model has training that includes a deep dive into your industry or company, it can be prompted to help you create things like product manuals or visual training simulations – the potential is practically endless.

Generative AI is an amalgamation of several sophisticated techniques and models. While there are many approaches, these three models are currently among the most influential and commonly used:

• Large language models (LLMs):
  These models, like ChatGPT, analyze extensive volumes of text data from diverse sources. They use a powerful form of neural network known as a transformer. This gives them the ability understand context and generate highly coherent writing. Businesses use LLMs for tasks such as drafting emails, summarizing reports, or developing product specs and manuals.
• Diffusion models:
  Diffusion models excel at tasks like creating realistic product images or automatically designing variations of existing visuals. These models work by refining and re-refining random noise until the AI can eliminate extraneous components and become as accurate as possible. This makes them very useful for generating visuals from text prompts.
• Multimodel transformer models:
  These models go beyond text and can process and generate across multiple types of input—like text, images, audio, and video. They form the basis of AI systems that can “see,” “listen,” and “talk.”

Generative AI has a secret superpower that is often overlooked: its ability to show you how to best integrate it into your existing workflows and processes. You can literally ask GenAI how best to make it help you, and it will guide you through, step by step.

• Internal knowledge sharing
  Employees often spend hours digging through internal documentation, project notes, or knowledge bases. Generative AI can instantly produce clear, concise summaries of extensive documents, quickly giving teams the essential information they need to make informed decisions and get back to productive work.
• Content creation and automation
  Assisted text authoring supports diverse teams, from product teams creating item descriptions for an international market to distributors generating shipment communications. Generative AI automates the creation, summarization, and translation of these materials, improving accuracy, ensuring brand consistency, and reducing manual effort. 
• Application and product development
  Engineering teams routinely expend considerable effort on repetitive coding and documentation tasks. Generative AI efficiently produces boilerplate code, software documentation, and even automated testing scripts. This lets developers concentrate on complex problem-solving and innovative features, significantly accelerating product development timelines.
• Customer support and service
  With the limited resources of human customer care teams, many companies took the less-than-ideal route of sending customers to libraries of FAQs or pre-prepared answers. But with generative AI, this experience can now become interactive, with customers being able to ask AI questions specific to their unique use cases and getting answers that reflect their needs. 
• Operational efficiency
  Many routine business processes involve repetitive tasks like data entry, report generation, and workflow management that consume valuable employee time. Generative AI automates these tasks by generating accurate documents, analyzing operational data, and orchestrating workflows. This reduces errors, speeds up processes, and frees staff to focus on higher-value activities that drive innovation and growth.

Generative AI’s benefits are clear and impressive, but moving from understanding the technology to practically adopting it in your enterprise requires careful planning. Here are several strategic, constructive steps to help your business successfully begin its generative AI journey:

1. Identify clear use cases
   Get started with well-defined, high-impact scenarios within your business where generative AI can deliver the most obvious benefit. This may include summarizing large volumes of data, developing maintenance schedules based on IoT data analytics, or preparing scenario templates to help your customer service teams navigate any type of potential issue.  
2. Assess your data readiness
   You wouldn’t expect good long-term health feeding a human a diet of junk food and sugar. Nor will your GenAI tools perform well on a diet of junk data. That is their food and what will eventually comprise the outcomes they give you. It is essential to invest carefully in the step of data collection and preparation – and to ensure you are using best practice protocols at every stage. 
3. Start with focused pilot projects
   In addition to starting with more obviously GenAI-suited projects, you should dip your toe in with a few pilot projects at first. This will let you focus attention on the outcomes and learnings from these pilots without spreading yourself too thin. It also makes it easier to demonstrate value to stakeholders, building buy-in and momentum for broader adoption.
4. Choose the right platforms and tools
   Today’s best enterprise cloud solutions focus on ease of cross-system integration. So it should be with your GenAI technology. Evaluate potential platforms based on criteria such as ease of integration, security features, scalability, usability, and alignment with your enterprise’s specific needs and existing technology stack.
5. Train and empower your teams
   You can’t just send your teams off for a few hours on a training day and call it done. GenAI is one of the most powerful and potentially disruptive tools you will bring into your company’s tech stack. For the best outcomes for your business, your teams, and your customers, it’s essential that GenAI training becomes an embedded part of your operational protocols.
6. Monitor, iterate, and scale up
   Once your initial pilots are live, regularly measure their performance and gather feedback from users. And remember: Generative AI is always learning. It’s essential that all these insights are gathered and used to iteratively refine your generative AI applications, steadily scaling successful implementations throughout your enterprise.

Conclusion

When it comes to generative AI, there’s a big digital elephant in the room which is the fact that it’s actually kind of awesome and terrifying. Most of us use it (intentionally or unintentionally) several times a day. And we quite rightly love the convenience and flexibility it brings to our lives. But underneath all that is a technology with the power to not only emulate us down to the letter, but do so faster and more accurately than we could ever hope to do ourselves. For today’s businesses, this brings with it enormous opportunity – and likewise – enormous responsibility. To compete, you don’t have a choice but to lean into and leverage this new tool. But to care for your teams and your customers, it’s also essential that you approach this amazing new frontier with respect and a commitment to good stewardship.