Making The Most Out Of Generative AI In Your Enterprise

Making The Most Out Of Generative AI In Your Enterprise

Generative artificial intelligence (AI) has come to the forefront of business leaders' minds with the ongoing stories and innovative capabilities of ChatGPT and the waitlist for its rival, Google Bard.

Generative AI is a part of AI focused on generating audio, text, video, 3D items, code and other data types into brand-new content. Generating this data comes directly from data that already exists. In ChatGPT's case, this is data from all over the web, but other platforms can use smaller data sets to ensure the model outputs are relevant and targeted to the user.

Enterprises can always benefit from saving time, but generative AI also creates revolutionary outputs to optimize products and the entirety of business processes.

Harms Of Unfocused Large Language Models

Patterns in training data are how large language models learn to create outputs. As mentioned, the training data of ChatGPT's model is from across the internet, using content from almost everywhere. However, the model is also trained from the actual inputs, leading to a slippery slope—false and unhelpful outputs are just two examples.

If you are a numbers person, an article from Reuters suggests data has been input by 100 million users monthly as of January 2023, including 13 million unique visitors per day in January alone.

Training models strictly from company data and trusted sources can eliminate the mixed information we see online. This approach empowers quick learning applicable to specific companies and industries, opening the door to alternative enterprise solutions.

Business Cases Where Generative AI Comes Into Play

A targeted mindset is critical for companies looking to measure these models' return on investment (ROI).

Product Development

There is no such thing as product development that doesn't require extensive research, prototyping and testing. Knowledge of what target customers seek is paramount for product development efforts. What trends exist, and what are competitors doing in the respective space? Answering these questions can take days or weeks—or sometimes longer.

Increasing research speed by inputting the information you seek can inspire product ideation in fewer hours. A generative AI assistant does research for you. In addition, the market research done by large language models can help teams decide on pricing models, strategies, and features to update and develop.

Gartner Inc. highlighted drug design as an example, noting that "the discovery process took a whopping three to six years." Generative AI has been used to cut down the drug design process to months rather than years—reducing costs and time for pharmaceutical companies.

In addition, Gartner shared that generative AI has been used for chip design with reinforcement learning, a machine learning technique that cuts development time from weeks to hours.

Customer Support And Sales

Top-notch support is mandatory when there is so much competition in every industry. Customers today are looking for personalized answers customized to their needs.

Generative AI can allow support teams to tailor their approaches and troubleshoot efforts for the specific customer. Within companies, data exists on each customer in several different data sources. Connecting these sources to help teams draft responses and compile content to give back to the customer is important for customer satisfaction and fast, accurate assistance.

The exact process can be used for potential customers you want to win business from. Generating sales strategies based on data from successful correspondence in the past gives salespeople the key to adding business to the company portfolio.

Salesforce noted that generative AI can help fix customer relationships through "digital empathy," with 84% of IT leaders believing generative AI will help companies serve customers better. Generative AI had led to more innovative chatbots with the ability to recognize visual and audio cues to determine customer emotions so the correct cases can be escalated to the top of customer support teams' priority list. It also noted that "nearly two-thirds of service professionals credit self-service with easing case volume."


All types of law firms, government institutions, general counsel, corporate counsel and more have loads of documentation related to all types of cases such as civil, criminal, corporate, contract disputes and complaints against cities. There are countless regulatory documents that write out the rules all parties need to adhere to. These documents exist throughout internal and external data sources. Lawyers often have multiple complex questions for each project or case they are working on.

The sheer capability to type a question into a software platform and receive generated answers in seconds can save time and ensure compliance. These models can also help revise current legal documents and update the verbiage used in lengthy legal reports.


So many areas stretching from industry to industry can take advantage of this life-changing technology, but misuse can cause major headaches for enterprises. It is important to remember that models trained on company data are required to ensure confidential information stays confidential. Having safeguards and training the model on data that belongs to the company is the only way organizations should move forward with generative AI.