The arrival of ChatGPT in late 2022 jolted the business world, introducing Generative AI (GenAI) to a much wider audience beyond those working in technology. More than a year later, businesses are scrambling to discern this nifty wave of innovation and its potential impact. With new and practical use cases emerging every day, GenAI has put the focus back on AI and machine learning (ML) technology and effectively obliterated the hype around other emerging technologies such as the meta-verse, crypto, blockchain, and Web 3.0.
Interest in GenAI is at an all-time high primarily because it has practical real-world business applications leading to increased productivity and creativity. According to Gartner, by 2026, more than 80% of enterprises will have used GenAI application APIs or models, and/or deployed GenAI-enabled applications in production environments, up from less than 5% in 2023. As this rapid adoption continues, let’s quickly review GenAI basics and highlight a select set of GenAI use cases that we view as enterprise priorities to solve business challenges.
GenAI (aka Generative Modeling) uses ML on existing content (datasets) to generate seemingly human-produced new content that is far more intuitive and, in some cases, superior to human creativity. While traditional AI is used for decision making and solving specific problems using predefined algorithms, GenAI is primarily used for content generation (text, images, audio, video, etc.) based on patterns from existing data and ML models.
At the core of GenAI are Large Language Models (LLMs) aka Foundational Models. They’re called “large” because they have billions of parameters that shape their responses. These models use deep learning algorithms that can recognize, summarize, translate, predict, and generate content using very large datasets. Aside from the large training dataset, a core component of a LLM is the transformer, which is a neural network that learns context and meaning by tracking relationships in sequential data, like the words in this sentence.
To leverage all this tech power, our client discussions across different industries on GenAI have centered around three primary areas:
- Developer Productivity: GenAI provides the rocket fuel to turbocharge software development. Developers can use prompt engineering to generate code in any language, and GenAI helps developers by automatically suggesting code snippets or completing code as they write. GenAI also can auto-generate code documentation (which is critical if future modifications are needed) and translate code from one language to another.
- Horizontal use cases: Gen AI has variety of use cases that can horizontally cut across various business units of an organization such as customer support, IT, HR, etc. For example, GenAI use cases for customer support include support ticket automation, self-service leveraging bots, recommendations, and assistant support agents.
- Domain specific use cases: Similarly, Gen AI can be used across various stages of domain-specific applications such as sales, MarTech, HR, supply chain, etc. For instance, for sales GenAI can assist in lead qualification, account research, demo calls, and the creation pf customer proposals and sales enablement materials.
At Persistent, we are constantly finding ways to solve customer pain points by leveraging top-of-mind technology, including GenAI — in fact, third-party analyst firm HFS recently named Persistent as a GenAI leader. Contact our team of experts today to faststart your GenAI journey toward increased productivity and greater efficiency.
