**2. Artificial intelligence: applications and challenges**

AI capabilities are rapidly evolving, and it is essential to build a framework to model the AI application process from study to implementation. **Figure 1** illustrates the general application of AI.

The critical challenge for using AI in industrial projects is to demonstrate the value of processed data in making intelligent predictions and optimizing decisionmaking. Overall, there are four significant challenges for the employment of AI in different industries: data, speed, high reliability, and interpretability.

*Advanced Analytics and Artificial Intelligence Applications*

point, which can change from time to time [3].

data can improve firm performance.

improve business processes.

electronics [37], sensors [38], and transport [39, 40].

data and manage its complexity.

The growing rate of data production in the digital era has introduced the concept of big data, which is defined by its significant volume, variety, veracity, velocity, and high value. Big data has created challenges for analysis, requiring organizations to deploy new analytical approaches and tools to overcome the complexity and magnitude of different data types (structured, semi-structured, and unstructured). Thus, BDA offers a sophisticated technique that can analyze an enormous volume of

BDA can be used to support projects in innovation, productivity, and competition [2] by examining, processing, discovering, and exhibiting results to uncover hidden patterns and provide insights into interesting contextual relationships [3]. Complexity reduction and managing the cognitive burden of a knowledge-based society are key benefits of BDA. The most critical contributor to the success of BDA is feature identification, which defines the most crucial elements affecting results. This is followed by identifying correlations between inputs and a dynamic given

As a result of the rapid evolution of BDA, e-commerce and global connectivity have flourished. Governments have also taken advantage of BDA to provide improved services to their citizens [3]. Specific applications of BDA for the management and analysis of big data include business and social media. BDA can improve understanding of customer behaviors and handling of the five features of big data—volume, velocity, value, variety, and veracity. BDA not only provides businesses with a comprehensive view of consumer behavior but also enables organizations to be more innovative and effective in deploying strategies. Smalland medium-sized companies can use BDA to mine semi-structured big data, improving the quality of product recommendation systems and website design [4]. As suggested by [5], the use of BDA technology and techniques for large volumes of

AA, AI, ML, predictive and prescriptive analytics, optimization models, decision-making algorithms, natural language processing, and robotic process automation have been popular keywords in various industrial studies in recent years. Industry managers and key decision-makers who are aware of the ability of AA to solve business problems are now competing for intelligent technologies and experts to operate them. However, investments in technology and data scientists do not guarantee success. Industrial managers must develop a strong foundation by embedding an understanding of AA within their companies. Three factors contribute to a thriving AA culture: people, strategy, and technology. The knowledge of individuals in companies plays a critical role in the success of the analytics revolution because there are no practical solutions for applying AA when a company is faced with unacceptable levels of knowledge and experience. Management strategies can be implemented to ensure that project teams are sufficiently flexible to adapt to solutions to work processes suggested by the AA. The level of technology is also essential for the accuracy of analytics and can be a critical parameter when the outcomes of AA are used to

AA and AI, defined as intelligence demonstrated by machines, have many applications. AA has been applied in many fields and industries, including agriculture [6, 7], oil and gas [8], aviation [9–15], computer science [16], deepfake [17, 18], education [19–21], finance [22], government, heavy industry, history [23], telecommunication maintenance, toys and games [24], hospitals and medicine [25–27], recruiting, human resources and job search engines [28, 29], military [30, 31], news services [32, 33], writing and publishing, online conference services [34–36], power

**2**

**Figure 1.** *Steps in applying artificial intelligence (AI).*

#### **2.1 Data**

Industrial systems produce large volumes of data, and advanced engineering is undoubtedly a big data environment. Collected data are typically structured. However, when faced with a low-quality dataset, industrial operations can generate data with "3B" (**b**ad, **b**roken, and illogical **b**ackground) issues. 3B issues can potentially create challenges in implementing ML and AI solutions for solving business problems. In some industries, the quality of data is insufficient to train and validate sophisticated algorithms such as deep learning models. This problem has been a major challenge for data scientists and data analysis in the development of prediction and optimization applications.

Moreover, real collected data from sites cannot cover all requirements and there are many gaps in datasets. A lack of data can be a crucial problem when data scientists are seeking comprehensive datasets to cover all working conditions. Further, AI solutions should be generated based on reliable historical information; however, in many cases, there is a lack of available data to make sustainable models.

#### **2.2 Speed**

With the evolution of technology across various industries, operational processes can rapidly produce large amounts of information. The use of intelligent applications is essential for working with enormous amounts of generated real-time data to reduce resource waste and operational risk. Nowadays, key industries use cloud-based approaches not only to store data but also to improve ease of access to information. However, these approaches still fail to meet the specific requirements for calculation effectiveness.

### **2.3 High reliability**

AI solutions are strongly related to background processes and collected datasets. In other words, the reliability of AI applications depends on the quality of historical information. AA applications usually deal with critical challenges related to security, maintenance, operations, energy consumption, and safety. Dissatisfaction with prediction, optimization, or decision-making algorithms may lead to negative outcomes and discourage users from relying on AA approaches such as AI systems.

### **2.4 Interpretability**

AI can help improve the accuracy and reliability of prediction and optimization of industrial applications. However, interpreting the results is a significant challenge for experts and managers when using AI to solve business problems. A practical solution for industry may be to train experts, specialists, and managers to operate the analytics and provide root cause analysis for anomalies. This implies that during the development of applications, data scientists should work with experts and managers to include domain knowledge in algorithm expansion processes and ensure that models can adaptively learn and accumulate knowledge.
