Shulou(Shulou.com)06/02 Report--

Artificial intelligence (AI) is reshaping business, subversion is not really coming, or even as fast as expected, but it is still developing rapidly compared to computers and the Internet.

The technology behind AI, such as development platform, powerful processing capacity and huge data storage space, is developing rapidly. Amazon even abandons the development of e-commerce in China and develops directly to the scalable cloud computing service platform, which shows the huge market prospect in the future, and even colleges and universities are groping to offer relevant courses.

Although the prospect is huge, the real development of enterprises is still slow, most enterprises only pilot artificial intelligence in part or in a business process, and there are still cultural and organizational obstacles to the implementation of artificial intelligence.

What are the factors that affect the transformation of corporate AI? Harvard Business Review recently surveyed thousands of senior executives to find out the relevant reasons and share their views as follows:

First, the three misunderstandings of AI

1. Including thinking that AI is a plug-and-play technology that brings returns quickly enough, the immediate investment will immediately see the return. The AI transformation of enterprises generally takes at least 18 to 36 months to change, or even more than 5 years, which is a medium-and long-term planning and implementation process.

two。 Enterprises have organized only a small number of pilot projects to think that they have done AI transformation, in fact, Sangnan has increased the focus of AI from decentralized issues (such as enhancing customer segmentation) to large business challenges (such as optimizing the entire customer journey).

3. The necessary conditions for the use of AI are not fully considered, in addition to cutting-edge technology and talents, the company culture, organizational structure and working methods are ignored. For the transformation of corporate AI, corporate culture, structure and working methods should support the wide application of AI, which is as important as technology and talents. But in most companies that are not born digital, traditional ways of thinking and working run counter to the needs of AI.

II. Four changes to be made in expanding the scope of application of AI

1. From isolated island operation to cross-domain cooperation.

AI, developed by cross-functional teams with multiple capabilities and perspectives, is the most influential. The collaboration of business and technical staff, together with analytical professionals, ensures that the project addresses the key issues of the entire organization, focusing not only on single sector issues, but also on operational changes required by the application of new technologies.

two。 From experience-based decision-making promoted by leaders to front-line decision-making driven by data.

When AI is widely used, algorithm recommendation will enhance the judgment and perception of employees at all levels of the organization, and get better answers that can not be obtained by humans or machines alone. On the one hand, we need the advice given by the trust algorithm of employees at all levels, and more importantly, we have the right to make decisions.

3. From rigid solidification and risk aversion to agility, experimentation and adaptability.

Organizations must get rid of the thinking that "only fully mature ideas can be implemented" or "only well-designed business tools can be used". AI applications need to be iterated and rarely have the functions required by the organization at the beginning of the application. Organizations should have a "learn from testing" attitude, turn errors into sources of new knowledge, and reduce concerns about errors. Collect feedback from early users to upgrade the AI tool to correct minor problems before turning into risk, and the development will gradually accelerate, allowing small AI teams to develop a minimum viable product in weeks rather than months.

4. Provide motivation and corresponding training.

To achieve fundamental change, leaders need to help employees prepare, provide motivation and corresponding training. But first, leaders themselves must be prepared.

Secondly, the company must train all personnel from top to bottom. This includes classroom teaching (online courses or face-to-face courses), seminars, on-the-job training, and even visits to experienced companies in the same industry, while developing sharing skills internally. Different focus training will be provided for leaders, analysts, interpreters and front-line personnel.

Leaders: have a high-level understanding of how AI works, learn to identify AI opportunities and judge their importance. Discuss the impact of AI on the functions of employees, obstacles to the promotion of AI and personnel training, and provide guidance for gradually promoting the cultural transformation needed by AI organizations. This includes learning to use AI tools in real business scenarios to assist in decision-making such as product release.

Analysts: continuously develop hard and soft skills for data scientists, engineers, architects, and other employees responsible for data analysis, governance, and AI solutions.

Interpreter: business people who need to interpret data and need basic technical training, such as using analytical methods to solve business problems, building AI practice cases, etc.

Front-line end users: give an overview of the new AI tools to be used, and then provide on-the-job training to teach them how to use AI tools. Strategic decision makers responsible for areas such as marketing and finance may need higher-level training courses to use AI tools to assist decision-making such as product release in real business scenarios.

The new applications will drive fundamental and sometimes difficult changes in workflows, functions, and culture. Leaders must carefully lead the organization through this stage. Human-machine cooperation can achieve higher results than the two sides work alone, and there will be more and more such cooperation in the future, and companies that successfully promote AI applications throughout the organization will have a huge advantage.

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