Problems that generative AI can solve in your business

Currently there is a boom with everything related to artificial intelligence, with thousands of advocates talking about the benefits of these tools, especially generative AI. But many companies wonder what is really behind these solutions and how much they could solve for their businesses by implementing them.

At the end of 2022, the influence of ChatGPT exploded, which generated several changes and transformations in business dynamics, since generative AI represented a significant increase in productivity and use of available resources in work teams. “The results speak for themselves: fewer bottlenecks, greater productivity and quality, and hours and hours saved on tedious tasks”, they noted in a Writer’s Room article on the subject.

In this article we are going to go a little deeper into the current context of generative AI, its use and what problems it can solve within companies.

Let's review some statistics about generative AI

The market size in the generative AI market is projected to reach $36.06 billion by 2024, according to a report by Statista. The market size is expected to show an annual growth rate of 46.47%, resulting in a market volume of $356.10 billion by 2030, they added. In global comparison, the largest market size will be in the United States ($11.66 billion in 2024).

Additionally, according to Salesforce research, 61% of current workers already use generative AI or plan to use it soon. “Two out of three workers say that generative AI will help them provide better service to their customers”, they noted. They also see generative AI as a tool to help them get more out of other technologies, such as AI and machine learning models.

Problems that generative AI solves in companies

• Reduction of repetitive tasks in the sales area

“Generative AI helps sales teams and representatives reduce repetitive activities, such as generating sales call transcripts, follow-ups, and designing the content of presentation materials to adapt to the new customer segment”, they explained in a statement. article published by Forbes.

• Processing of high volumes of information

“Generative AI use cases are already taking flight across industries. “Financial services giant Morgan Stanley is testing the technology to help its financial advisors better leverage insights from the company’s more than 100,000 research reports”, they noted in a report from the firm McKinsey.

• Increase innovation

“Generative AI holds immense promise for unlocking creativity across various industries. Companies can leverage generative AI to increase human creativity and accelerate innovation by driving operational efficiency, creating engaging marketing campaigns, detecting fraud, or generating realistic virtual agents. With generative AI applications and the right data, companies can explore more possibilities”, they noted on the Red Hat portal.

• Elevate personalization for the customer

The success of companies is based on customer personalization, and generative AI emerges as a key player in this aspect. By comprehensively analyzing customer data, AI offers business leaders a deep understanding of preferences, behaviors and trends. This information allows companies to dynamically generate personalized recommendations, ads, and experiences, driving greater customer loyalty and engagement.

• Improves decision making

Generative AI is emerging as a crucial tool for data-backed decision making. Companies can, through this technology, create and explore alternative scenarios, test hypotheses and make predictions based on historical data and simulations. By analyzing massive volumes of information, identifying patterns and generating forecasts, generative AI becomes an essential ally in decision-making processes. Her ability to provide meaningful insights, optimize operations, and support strategic planning positions her as an invaluable resource in today's business environment.

Key aspects about implementing generative AI efficiently

The safe implementation of generative AI involves several key aspects that must be considered:

Data collection

In the Harvard Business Review magazine they emphasize that one of the most frequent challenges for companies is the collection of data prior to the implementation of any artificial intelligence solution, because a lot of data is usually found in disparate systems, which makes this more difficult. process.

“Many artificial intelligence systems can write the code necessary to understand the schemas of two different databases and integrate them into a repository, which can save several steps in data schema standardization”, they added in Harvard Magazine.

Have a layer of rules

When it comes to what a customer can request in an input box, AI must have clear guidelines that ensure it responds appropriately even to requests that are out of its scope or inappropriate, they also added in the Harvard article. This underscores the importance of laser-focusing on the rules layer, where experience designers, marketers, and business decision-makers set the goals that AI should optimize.

Define specific use cases

The generative AI provider must define with the client what the final objective is sought with this AI solution. In this way, the provider will work based on that need, adapting generative AI to the client's interests.

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