How does artificial intelligence affect the business context? A systematic literature review

Introduction

Artificial intelligence (AI) has emerged as a revolutionary technology with the potential to transform various aspects of businesses and industries. As organizations navigate the digital era, the integration of AI capabilities into their business and IT strategies has become crucial for creating new business models, enhancing competitiveness, and driving sustainable value creation.

However, the journey to successfully leverage AI is not without its challenges. Many organizations find it difficult to effectively adopt and harness the full potential of AI, while some pioneers have been able to leverage AI to gain a competitive advantage. To better understand the factors behind the successful integration of AI and the resulting business value, a comprehensive review of the extant literature is necessary.

This systematic literature review aims to investigate the influence of AI on business value in the digital era of strategy. By synthesizing the existing research, the study seeks to identify the performance advantages, success criteria, and challenges associated with the adoption and implementation of AI in organizations. The review also aims to uncover the open issues and future research directions that can guide both academics and practitioners in effectively leveraging AI to enhance various business value streams.

The Role of Artificial Intelligence in the Digital Era

Artificial intelligence (AI) is a broad and multifaceted field that encompasses a wide range of technologies and techniques. At its core, AI refers to the ability of a system to identify, interpret, make inferences, and learn from data in order to achieve predetermined organizational and societal goals. (Mikalef & Gupta, 2021)

In the digital era, AI has become a crucial enabler of business transformation and value creation. Organizations are increasingly adopting AI-powered solutions to automate processes, enhance decision-making, improve customer experiences, and drive innovation across various functional areas. The integration of AI capabilities into business and IT strategies has the potential to unlock new avenues for competitive advantage and sustainable growth.

Systematic Literature Review Methodology

To conduct a comprehensive investigation of the influence of AI on business value, a systematic literature review was performed following the guidelines provided by Webster and Watson (2020). This approach ensures a rigorous, evidence-based synthesis of the existing research, providing a solid foundation for identifying current trends, uncover key insights, and outline future research directions.

The review process consisted of the following steps:

1. Literature Search: A keyword-based search was conducted in the Scopus database, a widely recognized resource for academic research in the fields of business, management, and information systems. The search terms included “artificial intelligence,” “business value,” “business strategy,” and “IT strategy.”

2. Inclusion and Exclusion Criteria: The search results were filtered to include only peer-reviewed journal articles published in English. Articles not focused on the intersection of AI and business value were excluded.

3. Data Extraction and Analysis: The selected articles were thoroughly reviewed, and relevant information was extracted, including the research objectives, methodologies, key findings, and implications. The data was then synthesized to identify common themes, patterns, and gaps in the existing literature.

4. Synthesis and Reporting: The findings of the literature review were organized and presented in a structured manner, addressing the performance advantages, success criteria, and challenges associated with AI adoption and implementation in the business context.

Findings from the Systematic Literature Review

The systematic review of 139 peer-reviewed articles revealed several key insights regarding the influence of AI on business value in the digital era.

Performance Advantages of AI Integration

The literature highlights various ways in which the integration of AI can enhance business performance and create value:

1. Improved Decision-Making: AI-powered systems can analyze vast amounts of data, identify patterns, and provide real-time insights to support more informed and data-driven decision-making processes. This can lead to better strategic choices, enhanced operational efficiency, and improved resource allocation.

2. Increased Operational Efficiency: AI can automate repetitive tasks, streamline workflows, and optimize resource utilization, leading to cost savings, improved productivity, and enhanced overall operational efficiency.

3. Enhanced Customer Experience: AI-driven applications, such as chatbots and personalized recommendations, can improve customer engagement, reduce response times, and tailor experiences to individual preferences, resulting in increased customer satisfaction and loyalty.

4. Accelerated Innovation: AI can aid in the development of new products, services, and business models by identifying market trends, generating novel ideas, and automating certain aspects of the innovation process.

5. Improved Risk Management: AI-based systems can detect and predict potential risks, enabling organizations to proactively mitigate threats, enhance resilience, and make more informed risk-management decisions.

Success Criteria for AI Integration

The literature also highlights several critical factors that contribute to the successful integration of AI into business and IT strategies:

1. Alignment with Business Objectives: Aligning AI initiatives with the organization’s strategic goals and priorities is crucial to ensure that the technology is leveraged to address key business challenges and drive measurable value.

2. Organizational Readiness: Successful AI integration requires a supportive organizational culture, the availability of necessary skills and expertise, and the establishment of appropriate governance frameworks to manage the risks and ethical considerations associated with AI.

3. Data Maturity: Robust data management practices, including data quality, accessibility, and integration, are essential for feeding high-quality data into AI systems and unlocking their full potential.

4. Collaborative Ecosystem: Effective collaboration with various stakeholders, including technology providers, industry partners, and academic institutions, can enhance the organization’s access to the latest AI innovations, talent, and knowledge.

5. Iterative Experimentation: Adopting a mindset of continuous learning and experimentation, with a focus on proof-of-concept projects and pilots, can help organizations navigate the complexities of AI integration and identify the most promising use cases.

Challenges in AI Integration

Despite the potential benefits, the literature also highlights several challenges associated with the integration of AI into business and IT strategies:

1. Technological Complexity: The inherent complexity of AI technologies, including the need for specialized skills, infrastructure, and data management capabilities, can create significant barriers to adoption, especially for smaller organizations.

2. Ethical and Regulatory Concerns: The use of AI raises ethical considerations, such as bias, privacy, and transparency, which must be carefully addressed to ensure responsible and trustworthy AI deployment.

3. Change Management: Successful AI integration often requires significant organizational transformation, including the adoption of new processes, the development of new skills, and the adaptation of existing business models, which can be challenging to manage.

4. Measuring and Demonstrating Business Value: Quantifying the tangible and intangible benefits of AI investments can be challenging, making it difficult to justify and sustain long-term investments in the technology.

5. Talent Availability: The limited availability of AI-skilled professionals, combined with intense competition for such talent, can hinder organizations’ ability to build the necessary in-house capabilities to effectively leverage AI.

Implications and Future Research Directions

The findings of this systematic literature review have several important implications for both academics and practitioners:

1. Aligning AI with Business Strategy: Organizations should focus on aligning their AI initiatives with their overall business strategy, ensuring that the technology is leveraged to address key strategic priorities and deliver measurable business value.

2. Developing Organizational Capabilities: Investing in the development of organizational capabilities, including data management, technical skills, and change management, is crucial for successful AI integration and sustainable value creation.

3. Addressing Ethical and Regulatory Concerns: Proactively addressing ethical considerations and regulatory requirements surrounding AI deployment is essential to build trust, mitigate risks, and ensure the responsible use of the technology.

4. Fostering Collaborative Ecosystems: Engaging with a diverse range of stakeholders, including technology providers, industry partners, and academic institutions, can help organizations access the latest AI innovations, talent, and knowledge.

5. Adopting Agile Experimentation: Embracing an experimental mindset and iterative approach to AI integration can help organizations navigate the complexities of the technology and identify the most promising use cases.

In terms of future research directions, the literature review highlights several key areas that warrant further investigation:

• Quantifying the Business Value of AI: Developing robust frameworks and methodologies for measuring the tangible and intangible benefits of AI investments to support more informed decision-making.

• Exploring AI Governance and Ethical Considerations: Examining the ethical, legal, and regulatory implications of AI deployment, and identifying best practices for responsible AI governance.

• Investigating the Role of Organizational Culture: Exploring the influence of organizational culture, leadership, and change management on the successful integration of AI into business and IT strategies.

• Analyzing Industry-Specific AI Use Cases: Conducting in-depth, industry-specific studies to uncover the unique challenges and success factors associated with AI integration in different business contexts.

• Examining the Coevolution of AI and Business Models: Investigating how the integration of AI capabilities can enable the development of novel business models and revenue streams.

By addressing these research directions, academics and practitioners can contribute to a more comprehensive understanding of the strategic role of AI in the digital era and provide valuable insights to guide organizations in their pursuit of sustainable business value.

Conclusion

The systematic literature review presented in this article has highlighted the transformative potential of artificial intelligence (AI) in the business context. The findings reveal that the successful integration of AI into business and IT strategies can deliver a range of performance advantages, including improved decision-making, increased operational efficiency, enhanced customer experiences, accelerated innovation, and improved risk management.

However, the integration of AI is not without its challenges. Organizations must address technological complexities, ethical and regulatory concerns, change management issues, and the need to demonstrate tangible business value. Additionally, the availability of skilled talent and the ability to foster collaborative ecosystems emerge as critical success factors.

To effectively leverage AI for sustainable business value creation, organizations must align their AI initiatives with their strategic priorities, develop the necessary organizational capabilities, and address the ethical and regulatory implications of AI deployment. By adopting an experimental mindset and fostering collaborative partnerships, businesses can navigate the complexities of AI integration and unlock the full potential of this transformative technology.

The future research directions outlined in this review emphasize the need for further investigation into the quantification of AI’s business value, the exploration of AI governance and ethical considerations, the role of organizational culture, industry-specific use cases, and the coevolution of AI and business models. Addressing these research gaps will provide valuable insights to guide both academics and practitioners in their efforts to harness the power of AI for competitive advantage and long-term growth.

In conclusion, the systematic review underscores the strategic importance of AI in the digital era and the imperative for organizations to proactively integrate this transformative technology into their business and IT strategies. By doing so, they can unlock new avenues for value creation, strengthen their competitive position, and thrive in the rapidly evolving business landscape.