artificial intelligence in business essay

artificial intelligence in business essay

The Impact of Artificial Intelligence on Business: Opportunities and Challenges

1. Introduction to Artificial Intelligence in Business

Despite this, there is a good chance that end users in many businesses will effectively disregard the fact they are using AI. This is particularly the case for those individuals who are interfacing with business solutions at a process level. Yet if an AI system provides decision recommendations that directly lead to action, the importance of understanding such decisions is vital. With data-driven systems, however, the rationale or the processes of how a decision is made are often obscured to end users, making biases automatically held inside the system difficult to identify, understand, and address. Even if a business user could learn to understand the parameters driving bias in machine-based recommendations, do they have the necessary tools and experience to be able to understand how to control or remove this bias? With no one-size-fits-all recommendation for AI within a business context, developing a deeper understanding of the fit between AI and business is essential.

Artificial intelligence is becoming increasingly important in the world of business. Given its potential to deliver a range of solutions across departments and use cases, companies may soon have problems if they do not embrace AI. Increased efficiencies, more time to build customer relationships, and an ability to focus on high-value activities, just to name a few positive impacts, can lead to business success with greater returns. Yet AI also has the potential to disrupt business models and competition, pose ethical dilemmas, and confront the notion of the division of labor. New roles and responsibilities will be created by the rise of AI, with new areas of expertise available over and above those created so far.

2. Benefits of Implementing AI in Business Operations

Finally, we provide an overall conclusion on this important issue. We also note that it can be difficult to distinguish among different forms of AI from published studies. Therefore, we use AI as a generic term throughout the paper. However, when we discuss a study specifically using machine learning or other forms of AI, we use the specific term that the authors have used.

The final area of application is enhanced decision-making support. With AI being used as decision support, we find its utilization in prescriptive analytics and assistance in decision-making. From these presentations, we provide summarized lists of AI applications, the specific benefits expected to be gained from implementing the AI applications, and previous research that confirmed such benefits.

We present these benefits in an overall structure according to how AI has been applied, starting with the automation of insights, with its utilization in predictive analytics and optimization. The next area of application is improved interaction with systems, which uses AI to replace or augment human-system interactions.

Numerous studies have documented the implementation of AI into several different business operations. These studies provide insights into how businesses have put AI into business operations, along with their reasons for doing it. From these studies, the major benefits of each AI application already present in this range of business operations are summarized in the table. These include financial benefits, customer satisfaction improvements, acceleration of processes, and overall improvements in business performance.

Discovering the benefits of AI implementation in business operations requires a thorough review of how it has been utilized in business. In this section, we present the collective findings from many research studies explaining how AI has already been utilized, with a focus on its benefits in actual business operations.

3. Challenges and Ethical Considerations in AI Adoption

Artificial Intelligence (AI) and the organization of AI-based economy development need to include avoiding related risks and overcoming associated challenges as a prime condition for AI technology’s sustainable development. Successful AI implementation by businesses depends on the ethical aspects that are very important for societal trust: the ethical aspects of AI innovations, the possible elements of harm from AI, and the challenges of implementing AI in the operating business environment. The main challenge companies face in identifying and preventing potential AI harm effectively is due to the processing, learning, and decision environment of black-box algorithms. These algorithms frequently do not have a clear causal mental model for why a specific output has been generated, which attributes contributed mostly to it, and how to control it appropriately. Providing the trustworthiness of AI also requires the quality of data and decision process results.

Challenges in the adoption of AI in business are caused by several aspects, including ethical considerations, inadequate internal AI expertise, absence of clear understanding and knowledge on the construction or implementation of AI, and its potential applications. Unclear risk assessments, absence of confidence in data quality, and consequently in the decision outcomes are also important challenges. Another significant challenge in AI adoption is the changes in job descriptions due to AI, which impacts employees’ concerns. Data privacy, liability, and transparency of AI technology are the main areas where businesses find the most challenging in supporting the commercialization of AI. Ethical considerations and various aspects of AI, which will be described in this section, are repeated as the main barriers for AI adoption and market development.

4. Case Studies of Successful AI Integration in Business

Each case represents a different way in which AI can be implemented to aid business operations. Google DeepMind tackled the issue of reducing the huge energy consumed by data centers, which is essential to keep costs low and to maintain a high server running uptime. Tencent WeChat and Alibaba Alipay both leverage AI to analyze data from a messaging platform and from business partner relationships respectively, allowing the Chinese e-commerce sector to benefit from an AI-integrated business operation, in a country where logistics have still much room for opportunities for improvement. Considering these case studies together will provide some interesting insights. For example, it can be noted that AI technology often becomes particularly vital in addressing business operations problems where human limitations are found, as in the trading performance-assessment system in Tencent as mentioned earlier.

In this chapter, I have examined the impact of artificial intelligence (AI) on business operations by presenting three case studies: the case of Google DeepMind on reducing energy consumption in power-consuming data centers, the case of Tencent WeChat, and the case of Alibaba Alipay. These cases illustrate the ways in which AI has been successfully integrated into business operations and has led to opportunities for improving operations and challenges faced, which have been overcome by the mentor firms, leveraging the actively learning-dependent AI in the business. In these case studies, insights have been discussed at both the AI/technical configurations and business operations’ levels as the case examples require it.

5. Future Trends and Implications for Businesses

The use of AI by companies itself leads to a reinforcement of existing concentration, since AI training and the large data assets from which to extract training data is costly, a contest for the possession of which only companies with many resources and competitors can finance. AI has implications for competitive structure. As AI begins to influence most aspects of work, the nature of entire jobs will change, shifting them away from productivity relative to AI and toward productivity that comes from complementarity with other tasks performed by AI. Will depend on understanding how to design tasks, tools, and organizations in a way that AI moves rather than replicating human employee level. The presence of AI problems in companies is affecting the aggregate structure of job markets. Each of these areas will be discussed in greater detail over the next five sections.

Some firms think they need to be more aggressive in developing plans and objectives, and creating capability in AI and data. AI is a field of collective action. Each case of AI intensive production should stimulate a supply of innovations, but, at the same time, be an artifact of past innovations. Seven strategic considerations for firms of all types are developed. The implications for competitive behavior vary with the differing motivations that have within five categories. The major implication, however, is that the same action is not appropriate for all firms. The issues that research and policy should focus on are discovered markets as well as the forms that transaction cost externalities might take. Companies purchase products and services that use AI. The tendency towards concentration at the level of market winners is predicted.