Author | Li Dongmei
The rise of generative AI has brought unprecedented opportunities and challenges to businesses and individuals. Recently, Tracy Tsai, research vice president at Gartner, shared the three disruptive powers that generative AI can bring to enterprises: a minimalist user interface, a "human-centric" experience, and clear delivery value. She emphasized that generative AI is not just a technology, but a business revolution that will disrupt traditional business processes, ways of working, and human-machine interaction experiences.
The most obvious example of this is the emergence of the iPhone, which has changed people's perception of what mobile phones look like. The introduction of the iPhone, with its minimalist user interface and intuitive touch-based interactive experience, has had a profound impact on the consumer market, and has quickly spread to enterprise applications, prompting enterprise applications to shift to more intuitive and user-friendly interactions. OpenAI's generative AI, such as the GPT series, has once again led the trend of large model inclusiveness with a lower threshold for entry, and has gradually penetrated into enterprise application scenarios.
Generative AI: An accelerator for enterprise innovation
According to Gartner, "generative AI" is seen as a key technology that can enable rapid growth. Businesses are exploring the value of their applications, such as improving product/service quality, reducing time-to-value, increasing employee productivity, and improving the customer experience. However, to effectively leverage generative AI for product innovation, companies need to focus on customer value rather than the technology itself.
Generative AI isn't just a technical innovation, it's a profound business revolution. It will disrupt business processes, ways of working, and human-computer interactions, and impact every business unit and role. For example, in the field of customer service, generative AI can replace human customer service, provide intelligent service 24/7, respond quickly to customer needs, and provide personalized solutions. In human resource management, generative AI can automatically screen resumes, identify key information, improve recruitment efficiency, and help companies find more suitable talent. In terms of marketing, generative AI can generate personalized advertising content based on customer data and preferences, and deliver targeted advertising to improve marketing effectiveness......
To better understand the needs of technology providers, Gartner conducted a survey asking the top four customer value that technology providers want to leverage generative AI to deliver or enhance.
According to the survey, technology providers are most concerned about customer value, including improving product/service quality, reducing time-to-value, increasing employee productivity, and improving customer experience.
Despite the "magic" of AIGC, the market environment faced by enterprises when conducting business is often complex and volatile, and it is also affected by multiple factors such as regulations, security, and API specifications, which makes the implementation and application of generative AI face many challenges.
Enterprise applications need to comply with strict regulations and standards to ensure data security and privacy protection, which places higher demands on the application of generative AI. In addition, generative AI needs to be integrated with the many and complex APIs within the enterprise to enable efficient applications. Solving these problems requires several core key capabilities for generative AI to drive value in the enterprise.
Grasp the four key capabilities to maximize the value of AIGC
According to Gartner, synthetic data, personalization, conversational AI capabilities, and AI agents are the four key capabilities of generative AI to effectively deliver customer value.
- Synthetic data: Make up for data deficiencies and biases, improve data quality, and achieve accurate predictions and personalized recommendations.
- Personalization capabilities: Enhance the customer experience by providing personalized solutions based on customer behavior and feedback.
- Conversational AI capabilities: Quickly realize value and streamline operations with natural language understanding and reasoning.
- AI agents: Autonomous or semi-autonomous perception, decision-making, action, and achievement of goals to increase employee productivity.
Huifen Tsai used several case studies to demonstrate the use cases of generative AI. Taking synthetic data as an example, in banking scenarios, banks can use synthetic data to simulate fraud behaviors and quickly identify and prevent fraud risks. Synthetic data can be used to simulate customer behavior, optimize product pricing, and improve marketing effectiveness.
Individualization is very important in education and training. For example, Khanmigo, an educational software, is able to provide personalized guidance based on student learning to improve learning, while conversational AI capabilities are embedded in almost all conversational bot products on the market. With the help of the ability to summarize large models, the chatbot can adjust its personality according to the user's preferences and enhance the interactive experience.
AI agents are the general trend of AIGC development in the future. Microsoft's AutoGen enables developers to quickly build generative AI applications, with AI agents helping employees complete tasks such as auto-replying emails, finding information, and booking hotels.
Future Trends: AI agents will be the general trend
Gartner predicts that AI agents will become the order of the day, and embedding AI capabilities into existing applications will improve user experience and personalization.
An AI agent is defined by Gartner as an autonomous or semi-autonomous software entity that uses AI technology to sense, make decisions, take actions, and achieve goals in a digital or physical environment. This agent can perform multi-functional and complex tasks, which can be automated, human-machine-cooperating, or guided from start to finish, depending on what the user's decision is.
According to Gartner, AI intelligence will play a key role in the future: how to fill the development and application side capabilities that enterprises need in embedded AI applications. This situation can be solved by a single AI technology, but by the full convergence of hardware, software, and services.
An end-to-end solution powered by AI agents is different from traditional point solutions, and it focuses more on a systematic, end-to-end solution strategy. Taking analog digital moments as an example, when "people, things, and things" may trigger a series of events in the interweaving of virtual and real scenes, such a solution can use its powerful data synthesis capabilities to simulate the various online and offline impacts that these events may bring, and generate corresponding solutions accordingly.
In the case of flight delays, for example, passengers often face a series of problems, such as connection times, hotel reservations, car rental arrangements, and meeting adjustments. However, with an end-to-end solution, the optimal connecting flight time can be quickly simulated and hotels, car rental companies and meeting organizers can be automatically notified to adjust accordingly. In this way, passengers arrive at the airport with a new arrangement, reducing unnecessary anxiety and distress.
In addition to aircraft delays, many event-based scenarios in smart cities can also benefit from this end-to-end solution. For example, in the event of a car accident, generative AI can quickly collect on-site data, including recent GPS information, to simulate the scene of the accident and provide claims recommendations to insurance companies and rescue guidance to police. At the same time, it predicts when the ambulance will arrive and coordinates traffic lights to ensure that the ambulance can pass smoothly. This rapid response, from simulation to execution, is exactly what generative AI is capable of in the digital moment.
Gartner believes that generative AI, with AI agents as the main trend, will continue to play an important role in cross-domain convergence and end-to-end solutions to drive a more intelligent and efficient society.
Original link: Gartner: These four key capabilities are the cornerstones of AIGC's value realization in the enterprise_Generative AI_ Selected articles by Li Dongmei_InfoQ