Artificial Intelligence in Business: State of the Art and Future Research Agenda

Artificial Intelligence in Business: State of the Art and Future Research Agenda

The integration of artificial intelligence (AI) in business operations has become increasingly prevalent in recent years. AI systems are designed to possess specific capabilities that enable them to effectively contribute to various aspects of business operations. These capabilities include natural language processing, knowledge representation, automated reasoning, and machine learning. With these abilities, AI systems can communicate in a natural language, store and retrieve information, analyze data, and adapt to new circumstances.

The worldwide spending on cognitive and AI systems has been steadily increasing, with a total expenditure of $24.0 billion in 2018 alone. This substantial investment highlights the growing recognition of the potential benefits that AI can bring to businesses. However, in order to further advance research and development in the field of AI applications for business, it is crucial for practitioners and researchers to have a comprehensive understanding of the existing knowledge and its applications across various business domains and disciplinary fields.

In a review conducted by Tkáč and Verner (2016), two decades of research on the application of artificial neural networks in business were analyzed. The review found that the majority of the examined articles focused on expert systems with practical applications. This indicates that there has been a significant emphasis on utilizing AI technologies to enhance decision-making processes in business operations. However, with the increasing availability of consumer information and the rise of the Internet of Things (IoT), companies have begun to place greater importance on data science skills and business analytics.

According to Gartner, the maturity of business analytics can be measured in four stages. The first stage involves the descriptive use of information, where data is analyzed to gain insights into past events. The second stage is diagnostic analysis, which focuses on identifying the root causes behind specific outcomes. The third stage is predictive analysis, which utilizes historical data to forecast future events. Finally, the fourth stage is prescriptive analysis, which provides recommendations on actions to be taken to achieve desired outcomes. This maturity model provides a framework for organizations to assess their current level of analytical capabilities and strive for continuous improvement.

In addition to the advancements in AI technologies, there are several concerns that have been highlighted in past research. One major concern is the need for organizations to re-envision their organizational structure, job functions, and skill sets to effectively incorporate AI into their operations. This requires a strategic approach to workforce planning and development, ensuring that employees possess the necessary skills to work alongside AI systems. Another concern is the education and preparation of future workforce, particularly students, who will be entering a job market that is heavily influenced by AI technologies.

As AI continues to evolve and reshape the business landscape, there are three actionable pieces of advice that businesses can consider:

• 1. Invest in AI skills development: Organizations should prioritize the development of AI skills among their workforce. This can be achieved through training programs, workshops, and partnerships with educational institutions. By equipping employees with the necessary knowledge and skills, businesses can effectively leverage AI technologies to drive innovation and enhance their competitive advantage.

• 2. Foster interdisciplinary collaboration: Given the multidisciplinary nature of AI applications in business, organizations should encourage collaboration between different fields and departments. This can facilitate the exchange of ideas, expertise, and resources, leading to more comprehensive and impactful AI solutions. By breaking down silos and promoting cross-functional collaboration, businesses can unlock the full potential of AI technologies.

• 3. Embrace ethical considerations: As AI becomes more integrated into business operations, it is crucial for organizations to prioritize ethical considerations. This includes ensuring transparency and accountability in AI algorithms and decision-making processes, as well as addressing potential biases and discrimination. By adopting a responsible and ethical approach to AI, businesses can build trust with their customers and stakeholders, while also mitigating potential risks.

In conclusion, the integration of artificial intelligence in business operations holds immense potential for driving innovation and enhancing efficiency. With the rapid advancements in AI technologies, it is crucial for practitioners and researchers to stay updated on the state of the art and collaborate across disciplines to further advance AI applications in business. By investing in AI skills development, fostering interdisciplinary collaboration, and embracing ethical considerations, businesses can position themselves at the forefront of AI-driven innovation and gain a competitive edge in the digital era.