Artificial Intelligence and Business Value: a Literature Review

What was the main purpose of the study?

To review literature (2010–2020) on how AI technologies create business value and identify factors that enable or hinder their adoption.

What framework did the study develop?

A framework showing how organizations can leverage AI to gain competitive advantage.

What are the three main categories of factors that enable or inhibit AI use?

Technological, organizational, and environmental factors.

Why is data quality important for AI adoption?

Because poor or biased data leads to inaccurate results—“garbage in, garbage out.”

How does infrastructure influence AI success?

Access to powerful computing systems or cloud-based AI services improves scalability and performance.

What role do algorithms play in AI adoption?

The sophistication of AI models determines accuracy, flexibility, and scalability.

How does organizational culture affect AI use?

Innovative, learning-oriented cultures are more open to adopting AI technologies.

Why is top management support critical in AI implementation?

Leadership ensures commitment, resource allocation, and alignment with strategic goals.

What does organizational readiness involve?

Having adequate financial resources, technical expertise, and domain knowledge.

Why is employee trust important in AI adoption?

Workers must understand and trust AI systems for successful integration and collaboration.

What is the role of AI strategy and compatibility?

Ensuring AI initiatives align with overall business objectives.

What environmental factors affect AI adoption?

Ethical and regulatory issues, industry competition, and government policies.

How do ethics and regulations impact AI use?

Issues like bias, privacy, and fairness (e.g., GDPR compliance) can limit adoption.

How does competition influence AI adoption?

Competitive pressure and customer expectations push firms to innovate with AI.

What are the two main ways AI is used in organizations?

Automation and augmentation.

What is AI automation?

The replacement of repetitive or cognitive human tasks with AI systems.

Can you give examples of AI automation?

Chatbots, robotic process automation (RPA), and predictive maintenance.

In which sectors is AI automation commonly used?

Manufacturing, finance, and customer service.

What is AI augmentation?

The enhancement of human decision-making through insights and recommendations.

Examples of AI augmentation include what?

Predictive analytics, personalized marketing, healthcare diagnostics, and recommender systems.

What is the focus of AI augmentation?

Collaboration between humans and machines to improve decisions.

What are the first-order (process-level) impacts of AI?

Improved efficiency, better insights, and process transformation.

What are the second-order (firm-level) impacts of AI?

Improved operational, financial, market, and sustainability performance.

How does AI affect operational performance?

It enhances quality and supports the creation of new products and services.

How does AI improve financial performance?

Through higher revenue, lower costs, and increased profitability.

How does AI strengthen market performance?

By enabling more precise customer segmentation and targeted marketing.

How can AI improve sustainability performance?

By optimizing resource use and increasing transparency.

What does “AI capability” mean?

The organization’s ability to combine data, people, and processes to create value from AI.

What are the components of AI capability?

Technical resources (data, algorithms), human resources (skills, management), and organizational systems (strategy, structure).

What determines whether AI generates value in a company?

Strategic integration, not just technical implementation.

Which factors are most critical for AI success?

Data quality, organizational culture, and alignment with business goals.

Why do many firms fail to realize AI’s potential?

Due to lack of readiness, understanding, or strategic direction.

What areas should future AI research explore?

Long-term organizational impacts, ethics, and cross-industry application