Jump started by the uber popularity of ChatGPT, artificial intelligence is entering a new phase. It’s changing the way we communicate, create and work on a daily basis. The excitement is centered around the generative ability of computers, or generative AI. It can be used to create new ideas, content, and 3D models that had never existed before. 

The opportunities of AI have not been lost on businesses. AI adoption more than doubled in the five years leading up to 2022, according to a McKinsey survey. The most popular AI use cases included service operations optimization at the top. It was followed by the creation of new AI-based products. With ChatGPT taking generative AI mainstream, of adoption and development of AI to augment human capabilities will only increase. 

A graph depicts AI adoption over the last five years

What you need to know to take advantage of generative AI for your business in this rapidly evolving environment? Let’s unpack.

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What Is Generative AI?

Generative AI is a type of artificial intelligence that creates something new and original from existing data using algorithms. It represents a breakthrough in machine learning. This involves models that can learn from data automatically without being programmed to do so. Traditionally, machine learning models could identify patterns and predict content. But with generative AI they are creating novel works that previously had not existed. 

Large language models (LLMs), such as OpenAI’s GPT series (Generative Pre-trained Transformer) and the conversational variant ChatGPT, are a type of generative AI specifically designed for natural language generation. These models are trained on massive volumes of data and use deep learning to generate human-like text. The latest models are impressive for their range of abilities from drafting emails to generating code. 

In addition to text, generative AI today includes the creation of new images, music, simulations or computer code. DALL-E, also from OpenAI, is an AI model that generates entirely new and realistic images from natural language descriptions.

A DALL-E generated image shows an astronaut riding a horse in space
Caption: Credit OpenAI

How Does Generative AI Work?

Generative AI works by using algorithms to detect patterns and relationships in data to generate entirely new data or content. The starting point to generative AI is the collection of large amounts of data from various sources that contain content such as text, images, audio or code. Data sets are fed into an AI model that learns patterns, relationships and nuances in the data through a process called training. The model learns a probability distribution across its training data and uses that distribution to generate new data similar to the original.

A neural network allows an AI model to process large amounts of data, using complex algorithms to identify patterns and continuously learn. A neural network is made up of layers of interconnected nodes designed after the structure of the human brain. Over time, the AI improves its performance as it weighs its output against what’s expected given its input in a process.

For example, an LLM uses a large neural network to analyze massive amounts of natural language data, such as Wikipedia articles and novels, to make predictions based on that data and produce new text when prompted. In cases like ChatGPT, the AI model can carry conversations and develop new skills such as essay writing, translations to different languages and computer coding.

Once an AI model has been developed and trained, humans can fine tune them for specific tasks and industries by feeding them additional data, such as for biomedical research or satellite imagery in the defense industry.

What Can Generative AI Do?

The uses of generative AI are far reaching, touching all industries including robotics, music, travel, medicine, agriculture, and are still in development. Generative AI’s major appeal is in its ability to produce high-quality content with little human effort involved. Companies outside of tech are already taking advantage of generative AI for its ability to create content so good it sometimes seems human made. For example, clothing brand Levi’s announced they will use AI-generated clothing models to provide its customers with more diverse shopping experiences. 

When it comes to language, all industries or job functions dependent on clear and credible writing stand to benefit from having a close collaborator in generative AI. In the business world, these can include marketing teams that work with AI models to  create content such as blogs and social media posts more quickly. Tech companies can benefit from coding generated by AI, saving time and resources for IT professionals so they can pursue opportunities for creating greater value for their businesses.

Graphic illustrates three generative AI use cases in service and support
Customer service and support can see incredible gains from well-planned applications of generative AI.

The customer service industry, in particular, is poised for incredible gains from this technology. By 2026, conversational AI will cut the labor costs of contact centers by $80 billion, according to Gartner. Conversational AI will make agents more efficient and effective with one in 10 agent interactions set to be automated by 2026. AI models will allow support agents to dedicate themselves to different and better work.

Let’s take a closer look at what generative AI can do for businesses.

Contact Center

Generative AI has the potential to significantly benefit the contact center for both agents and customers. Currently many organizations struggle with agent staff shortages and high labor expenses, which make up 95% of contact center costs, according to Gartner. Companies who use generative AI to automate even parts of interactions, such as identifying a customer’s policy number, will greatly reduce time spent on tasks typically supported by humans. 

AI-powered bots can use natural language to perform a sentiment analysis, empathize with unhappy customers and improve their experiences. They are able to not only answer questions but have conversations with customers both digitally and with voice.

Knowledge Base and Search

People working in customer service are faced with the need to learn and access a lot of information to serve customers. There is a paradigm shift happening in support organizations from search to answers. Generative AI can improve the self-serve knowledge base experience to help customers find answers quickly and intuitively on their own. 

With LLMs, customers will be able to ask a question and get a high quality answer right away, structured like a conversation with a human, instead of the traditional list of results. To accomplish this, companies will need to invest in training models to be tuned to their specific domains. 

We will cover generative AI’s impact on search further below. 


Generative AI can be a powerful way to create interactions with customers at a deeply personal level. AI models are capable of analyzing massive amounts of customer data, such as purchasing behavior and profile data, to understand what a customer wants and respond in a human-like way. Over time, the AI model will improve its performance on serving customers as it gathers more information and learns through trial and error. 

In the area of customer self service, generative AI can also help solve the comprehension problem. This is the failure of getting a customer a satisfactory answer because the content isn’t relevant to them or they don’t understand the terminology. AI can fill in the gaps that lead to failures in addressing customers’ inquiries by automatically adjusting content to their levels of expertise or experience.

An image shows search results with a generated answer at the top.
Generative AI use cases abound — it’s particularly suited to help refine and summarize answers in the support industry.

Content Creation

Enterprise content is key to shifting the cost structure from labor involved in delivering answers to developing LLMs to perform the heavy lifting. Agents and knowledge workers can lean on generative AI to draft content in their areas of expertise to produce content in the knowledge base, product documentation and help articles. This in turn builds up the repository of content necessary for LLMs to assemble the best answers for customers.   

Ecommerce companies can use LLMs to create content for product catalogs (including images) and help create instruction manuals and other sales collateral.

What Are AI Hallucinations?

As impressive as generative AI is, there are risks involved in using them. The outputs from these AI models seem accurate and convincing, but it’s become clear that they often repeat wrong information. For example, several people have recorded instances when ChatGPT referenced studies that did not actually exist. This tendency for AI models to produce something that appears completely true but is made up has been coined “hallucinations.” 

Graphic depicts an AI hallucination
Asking ChatGPT to generate citations for research papers. None of the articles above exists and all links lead to other publications.

Why do they occur? One reason could be that hallucinations occur by making deductions from incorrect, inadequate, insufficient and stale data used in training for the task at hand for that AI system. There is no shortage of “fake news” on the web, and unfortunately it can get regenerated as fact. 

In some cases these hallucinations are easy to spot and amusing. But other times, they can have serious consequences. For businesses, misinformation and inaccuracies can lead to an erosion in customer trust and harm to their reputations.

While generative AI such as ChatGPT can be a great benefit to businesses, organizations will want to put safeguards in place to account for AI models’ inabilities to ensure accuracy or to discern truth. One way to prevent hallucinations is to fine-tune pretrained models to enterprise-specific data so they are not relying on their internal memory.

Enterprises can protect their knowledge and IP by safeguarding LLMs with a relevance layer
Enterprises can protect their knowledge and IP by safeguarding LLMs with a relevance layer

For example, a system that gets search results first through search powered by a unified index and then uses ChatGPT to construct an answer, with links to original sources, can minimize hallucinations and make fact checking possible. Additionally, a layer of human supervision is necessary to verify the factual accuracy of AI, especially in scenarios where accuracy is paramount. 

Businesses can also limit the AI system’s access to sensitive information to prevent unintended disclosures of confidential information and account for the unpredictable nature of language models’ responses.

Will Generative AI Replace Search?

That brings us to the question of whether generative AI will take over for search. We’ve covered the tendency of generative AI to hallucinate answers, which would impact the trustworthiness of search results, but there are other risks and limitations involved in using this technology. 

The large language models that get trained on massive amounts of texts on the internet are also reflecting the biases that may be present in its source materials.This poses a danger for companies in producing responses to customers that are discriminatory. 

Search relies on the most up-to-date data, but LLMs work from fixed knowledge and it would take an incredible amount of resources and expenses to retrain them continuously to ensure data freshness. Additionally, LLMs can be vulnerable to data extraction attacks, putting sensitive and confidential information at risk.

Due to these limitations and risks, generative AI isn’t a replacement for search. But generative AI can significantly augment search. After all, there are many instances when all we want from a search is “the answer” or a summary of the main points in an understandable manner, both scenarios that we can get closer to achieving with generative AI. 

Coveo AI’s approach is to use generative AI in conjunction with search to create a digital experience that is trustworthy, secure and up to date in its results. We recommend using large language models to assemble the answer and rely on a company’s enterprise content for the data. This can be achieved through Retrieval-Augmented Generation (RAG), which we outline in this blog and involves finding relevant documents first and then using LLMs to generate the answer. 

A secure search technology used with generative AI can also ensure the permissions of source systems are always in place and you are respecting the privacy of your customers. 

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Ending Thoughts

Generative AI is a relatively new field that is quickly growing, particularly given the push and excitement created by ChatGPT’s release into the mainstream. New opportunities for businesses can develop in months or even weeks. However, despite its potential, the field is still maturing and implementing generative AI in an enterprise environment will take much measured progress and experimentation in addition to careful investment in resources. 

In applying generative AI to business scenarios, companies will need to invest in the infrastructure to ensure content is fresh, secure and protected against hallucinations. As AI makes its way further into more use cases, businesses will benefit from remaining vigilant in new advances and mapping developments to their real business needs.

For best practices on using generative AI for your business, watch our webinar “Scaling Customer Service in the Era of ChatGPT.”

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