7 Major Benefits of Generative AI for Businesses
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People use generative AI models for searching, creating art, writing papers, and conversing—whether politely or otherwise. But how can businesses leverage these powerful tools to meet real-world business needs?
Generative AI, as the name suggests, can generate images and text. This subset of artificial intelligence (AI) can also produce synthetic data. The technology builds on several advancements, including generative adversarial networks and large language models that may contain trillions of parameters.
These advancements enable data scientists to prepare models using vast amounts of training data, offering businesses the following seven benefits of generative AI.
1. Instant Content Creation
Rapid content creation is one of the most obvious advantages of generative AI. Arun Chandrasekaran, Vice President and Analyst at Gartner, notes that it is also one of the most accessible tools. Today, the ability to generate content like marketing newsletters and blogs provides tangible value.Gartner predicts that the media industry and corporate marketing will use generative AI to produce text, images, videos, and audio. According to market research, by 2025, 30% of outbound marketing messages from large organizations will be synthetically generated, compared to just 2% in 2022.
Customer interaction appears to be another early commercial application of generative AI. Businesses can benefit from using chatbots that provide more human-like responses to customer inquiries, thanks to the depth enabled by large language models.
Companies can deploy generative AI tools in a self-service model to handle routine customer queries. Industry executives also envision generative AI bots playing an agent-assist role in customer service, using natural language processing to listen to agent-customer discussions and retrieve relevant resources to support interactions.
Pablo Alejo, a partner at consulting firm West Monroe, said, "Imagine a scenario where ChatGPT is listening to a call and actively pulling content from a repository to help customer service agents deliver better service. This fundamentally changes how they operate."
Generative AI can help businesses enhance personalization. Machine learning algorithms can analyze users' purchase history and online behavior to improve product recommendations or generate customized content. Meanwhile, sales teams can create personalized presentations, and marketers can refine their campaigns.
Organizations may also benefit from improved personalized employee training. Bill Bragg, CIO of enterprise AI SaaS provider SymphonyAI, suggests that generative AI can act as a teaching assistant, supplementing human educators and delivering content tailored to individual learning styles.
Businesses can also benefit from rapid ideation and the ability to create new products and services. Generative AI has the potential to accelerate industries like pharmaceuticals, where drug discovery can take a decade or longer. Chandrasekaran notes that the ability to launch products while reducing R&D time and budgets is one of the most promising use cases.
However, privacy concerns, complex business processes, and the nascent state of the generative AI ecosystem make product creation one of the most challenging use cases, Chandrasekaran adds. Hyper-personalization also falls into this category.
Machine learning models can suggest application code to boost developer productivity. For example, ChatGPT can assist with website development, writing code in languages like JavaScript, and debugging.
Generative AI can also streamline other complex processes. Bragg cites the example of a software vendor's deal desk, a cross-functional team managing quotes, proposals, and contract processes. Currently, consolidating contracts—merging multiple agreements for a product or service into a single document—can involve extensive back-and-forth between the vendor's deal desk and the client.
However, Bragg points out that a generative AI-powered deal desk could gather data on disparate licensing models scattered across a client's business units. An AI agent that digests and learns from this data could give the deal desk a head start in contract consolidation.
Absorbing tedious tasks is likely to become a hallmark of this technology's commercial applications. Chandrasekaran states, 'Generative AI can abstract a significant amount of low-level tasks from business users, freeing up valuable time and unlocking productivity.'
Client-service-focused industries such as consulting can benefit from generative AI. Alejo highlights the technology's ability to absorb research data on specific topics, process it through models, and identify high-level patterns. With these insights, consulting firms can initiate the process of developing business strategies for clients.
Using generative AI in knowledge management can provide businesses with a competitive edge. Alejo notes that AI tools will emerge 'wherever knowledge management is critical—where there’s an accessible repository of information, and we can streamline the process of finding it.' He adds that subsequent advancements will focus on generating insights from this information.
Chandrasekaran observes growing interest in conversational AI-driven enterprise search and knowledge management systems in healthcare, financial services, and legal sectors. He adds that in these industries, generative AI 'has the potential to democratize institutional knowledge.'