Forbes: Vertical AI is the next revolution in generative AI

Source: Forbes

Compilation: Babbitt

Image source: Generated by Unbounded AI tool

The emergence of generative artificial intelligence (AI) has sparked great interest across industries. This powerful technology has the potential to revolutionize the way we work, create new possibilities, and transform every field.

This article explores what generative AI is, how it works, its growing momentum, and the expected impact of industry-specific AI (also known as Vertical AI). Additionally, this article will discuss the consequences of not adopting generative artificial intelligence in the modern business environment.

**01 What is generative artificial intelligence and how does it work? **

Generative artificial intelligence refers to the application of artificial intelligence technology, which allows machines to autonomously create, generate and produce new content. Unlike traditional AI systems that rely on existing data for analysis and decision making, generative AI utilizes advanced deep learning models to learn from existing data and generate raw, realistic output in an understandable format.

The technology works by training a model on large amounts of data to capture patterns, styles and correlations. Once trained, a generative AI model can generate new content by extrapolating from learned knowledge, allowing it to create original and often highly realistic output.

Generative AI is gaining momentum in various fields due to its potential to streamline workflows, automate creative processes and unlock new opportunities. Industries ranging from arts and entertainment to healthcare and manufacturing are recognizing its transformative power.

It's clear that generative AI is becoming a commodity. However, not all generative AI is the same, and it is expected to fall into two distinct categories: general and vertical.

General AI models such as ChatGPT and Google Bard are becoming more common - finding applications across industries due to their general capabilities. Vertical AI models, on the other hand, are more specialized in design, will be tailored to specific industries, and provide a significant and more immediate return on investment.

02 Growing demand for vertical AI models

The distinction between general and vertical AI models highlights the growing need for industry-specific solutions as businesses seek to harness the power of AI to optimize their operations and unlock new opportunities for growth.

Most companies need a model that integrates and transforms their vast amount of industry data and expertise into meaningful outputs that provide targeted solutions to meet the needs of a specific industry. These models require specialized algorithms designed specifically for the specific industry or use case they serve.

In field service, for example, vertical solutions are transforming service operations by addressing industry challenges such as rising customer expectations, labor shortages, and equipment complexity. By leveraging artificial intelligence designed to address specific service use cases, these solutions can help organizations diagnose and resolve issues faster than ever before, provide more self-service options, optimize resource allocation to overcome labor shortages, and enable proactive maintenance to minimize downtime. This improves operational efficiency and improves customer satisfaction, providing organizations with sustainable growth and a competitive advantage.

According to a McKinsey study, “Across a company of 5,000 customer service agents, the application of generative AI increased problem resolution per hour by 14% and reduced time spent handling issues by 9% ...it also reduced agent churn and reduced calls to managers by 25 percent.” Ultimately, the report confirms that these benefits are largely due to “artificial intelligence helping less experienced agents use less-skilled agents. high peers with similar skills to communicate".

Another important differentiator of vertical AI is the ability to incorporate subject matter expertise into the model. Our internal data shows that one-third of service solutions in leading service organizations cannot be found in historical service data. Instead, the best answers to any question can be found in data provided by subject matter experts, emphasizing the importance of incorporating human expertise into datasets.

Some vertical AI solutions have the ability to digitize expert knowledge, which means they can convert the knowledge stored in the heads of the company's experts into synthetic data. By leveraging the knowledge of subject matter experts, AI models can achieve more personalized and reliable results.

03 How Enterprises Integrate Vertical Artificial Intelligence Strategy

Incorporating a vertical AI strategy can be a transformative journey for businesses. Here are three steps to start this journey:

**1. Identify relevant verticals and use cases. ** Conduct a thorough analysis of business processes, customer needs, and market trends to identify areas where artificial intelligence can bring value. Look for specific use cases within these verticals where AI technologies can improve efficiency, enhance decision-making, or create new opportunities. For example, if you are in the service industry, you might consider applying AI to equipment diagnosis and troubleshooting, knowledge management or workforce management.

**2. Develop in-house expertise. **Developing a vertical AI strategy requires building in-house expertise in AI technologies and their application in the chosen vertical. This can be achieved by hiring AI experts, upskilling existing staff, or partnering with external experts or consultants.

**3. Start with a pilot project. **Start your journey by launching small-scale pilot projects in identified verticals. These projects will allow you to test the feasibility, potential impact and associated challenges associated with implementing AI solutions in real-world scenarios.

04 Consequences of ignoring generative AI

Generative AI represents a major breakthrough in artificial intelligence, and as mentioned earlier, this technology has the potential to become a commodity. Whether businesses leverage general AI or vertical AI will be up to them, but those who ignore the technology could face significant challenges. Without harnessing the power of generative AI, companies risk falling behind their competitors. They may face increased operating costs, slower decision-making processes, and missed opportunities for automation and optimization.

As generative AI continues to evolve, it's critical for businesses to explore its possibilities, integrate it into workflows, and leverage its capabilities to achieve new levels of success.