The following is the Selected AI cases in 2021 – boosting the large-scale application of AI From Deloitte Consulting recommended by recordtrend.com. And this article belongs to the classification: artificial intelligence, Deloitte Consulting , research report.
From alphago to alphafold, the algorithm continues to iterate and the intelligent boundary continues to expand. In just a few years, AI (Artificial Intelligence) has not only moved from science fiction to reality, but also achieved leapfrog development at the technical level. However, for most organizations, the main challenge is to apply AI on a large scale and how to maximize the value of AI.
Recently, Deloitte AI Research Institute released the report “selected AI cases”. The report carefully selects and analyzes the most eye-catching and applicable AI cases in six key industries (consumption, energy, resources and industry, financial services, government and public services, life sciences and health care, technology, media and Telecommunications). Each case summarizes key business problems and opportunities, how AI can help and possible benefits. These cases more or less imply future trends, It is expected to have a significant impact in the future.
Consumer industry
The consumer industry covers a wide range of businesses, including consumer goods, retail, automobile, accommodation, catering, tourism and transportation. What these seemingly completely different businesses have in common is that they all focus on serving consumers and giving them a better user experience. The consumer industry has been actively exploring various modes combined with AI, and many valuable cases have emerged, such as AI helping to optimize traffic routes, AI helping to improve the personalization of customer experience, AI enabling intelligent marketing, etc. In the future, AI will have closer interaction with consumers in terms of unmanned retail stores, automatic driving and automatic wear suggestions, and will also bring users a better experience.
Energy, resources and industry (ER & I)
Compared with other industries, the application and deployment of AI in the field of energy, resources and industry (ER & I) seems not extensive and mature enough. The challenge mainly focuses on data. Unlike many other data centric industries, er & I still revolves around physical assets, many of which are geographically dispersed and disconnected from digital networks. The application of IOT related technologies and the rise of edge computing are beginning to fill this data gap. At present, the key areas of AI application in Er & I are AI optimizing the performance of industrial machines, predicting faults, and using AI + IOT to optimize industrial production processes. In addition, exploring AI’s response to extreme weather and other unpredictable events and automatically generating alerts is also the main direction at present. In the future, AI will usher in great breakthroughs in accelerating material R & D, supply chain planning and optimizing plant operation through digital twins.
Financial services (FSI)
Most companies in the financial services industry (FSI) are still in the early stages of AI adoption and investment. Although FSI leaders generally recognize the importance of AI and the value it brings to the business, most AI applications are limited to small-scale pilots and independent business units. In the financial service industry, AI application focuses on using AI to improve customer experience, such as chat robot, AI driven personalized customized financial scheme, etc. Another rapidly emerging application area of AI is the automation and enhancement of key FSI processes, such as fraud detection, payment processing, cash reconciliation, credit risk analysis, underwriting and claim management. Some of these processes are highly repetitive and labor-intensive, which makes them the first choice for AI intelligence. In the long run, an important trend for AI to take root in the financial services industry is to use AI and data to break the departmental shaft and generate insights across the entire value chain. For most FSI companies, it is important to start embracing AI and industrializing it so that AI solutions can be deployed on a large scale throughout the enterprise and play a greater value.
Government and public services (GPS)
In government and public service (GPS), the adoption and maturity level of AI often vary with different government agencies, the dependence of existing infrastructure on the original system and the adaptability of staff. Defense, intelligence and law enforcement agencies are generally deploying and expanding AI, actively adopting advanced technologies such as computer vision, using image recognition to analyze and find suspicious activities and participants. Another common trend is the increasing use of robotic process automation to reduce the workload of employees. At the same time, AI catalyzed by COVID-19 is playing a great role in predicting public health and environment. In the future, AI technology will be used to detect potential security threats faster and more accurately, and there is still great potential in maintaining urban infrastructure, so as to make urban operation more stable, safe and efficient.
Life sciences and health management (lshc)
So far, most organizations in the field of life sciences and healthcare (lshc) have only scratched the surface of AI potential. They mainly use AI to automate repetitive tasks and standard business processes. For example, natural language understanding technology is used to interpret cases, realize automatic clinical trial data management, AI intelligent guidance, AI assisted film reading, etc. However, how to better integrate AI with life science is now widely regarded as a strategic issue in this field. In particular, AI has great value space in accelerating the drug development cycle, helping researchers identify and verify gene targets, and designing new compounds.
Technology, media and Telecommunications (TMT)
In this area, the adoption and maturity of AI vary from industry to industry. AI technology has been widely used in customer-oriented activities, such as virtual voice assistant, AI translation and so on. What is likely to be common in the next few years is the use of AI for predictive analysis, which can translate a large amount of customer data of telecom companies into valuable insights, so as to further promote the acquisition of more data. In the media field, most of AI’s focus has been on the recommendation of personalized content, and this trend may increase in the future.
Looking at all cases of the six major industries, AI has undoubtedly created a lot of value for all walks of life:
Reduce costs: apply AI and intelligent automation solutions to solve relatively low value and frequently repeated tasks, so as to reduce costs and increase efficiency.
Accelerate execution: reduce the time required to achieve operational and business results by minimizing delays.
Reduce complexity: improve understanding and decision making by being more proactive, predictive, and able to see patterns in increasingly complex sources.
Change the participation mode: change the way people interact with technology, so that enterprises can interact with people in a “people-oriented” way, rather than forcing people to interact with machines.
Promote innovation: use AI to innovate new products, markets and business models, and redefine where to implement and how to win.
Strengthen trust: protect enterprises from fraud and network risks, improve quality and consistency, and improve transparency to enhance brand trust.
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