AI Value Chains: Structuring the Future of Intelligent Business

Mohammed Zufian^*

Department of Communication, University of Kazakhstan, Kazakhstan

*Corresponding Author:

Mohammed Zufian
Department of Communication, University of Kazakhstan, Kazakhstan
E-mail: md@zufian.kz

Received: 02-June-2025; Manuscript No. gmj-25-169549; Editor assigned: 04-June-2025; Pre QC No. gmj-25-169549 Reviewed: 17-June-2025; QC No. gmj-25-169549; Revised: 23-June-2025; Manuscript No. gmj-25-169549 (R); Published: 30-June-2025; DOI: 10.36648/1550-7521.22.70.492

Citation: Zufian M (2025) AI Value Chains: Structuring the Future of Intelligent Business. Global Media Journal, 23:75.

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Introduction

Artificial Intelligence (AI) is reshaping industries at a staggering pace, but its true value is unlocked not just by deploying smart algorithms—it’s about integrating AI into value chains [1]. The concept of an AI value chain represents the entire process of developing, training, deploying, and scaling AI systems to deliver strategic business outcomes.

As companies race to harness AI, understanding the AI value chain becomes critical. It provides a structured view of the components and stages involved in AI creation and implementation—ensuring investments lead to real, repeatable value.

What Is an AI Value Chain?

An AI value chain is the set of interrelated activities involved in creating and delivering AI solutions. It spans from raw data collection to the final business application and impact [2]. Each step in the chain contributes to refining the value AI provides to users, businesses, and society.

According to the World Economic Forum, the AI value chain can be broken down into five main layers (WEF, 2022):

Data Layer

Algorithm and Model Layer

Computing Infrastructure Layer

Application Layer

Business Integration Layer

Key Components of the AI Value Chain

Data Collection and Management

Data is the foundational fuel of AI. This includes:

Gathering structured and unstructured data from multiple sources

Ensuring data quality [3], labeling, and compliance with privacy laws (like GDPR)

Creating data pipelines for continual input and learning

Organizations like Google and Amazon invest heavily in proprietary data assets to maintain their AI edge (McKinsey, 2023).

Model and Algorithm Development

This is the “intelligence” stage where:

Data scientists design, train, and validate machine learning (ML) models

Algorithms are tailored for tasks like classification, prediction, natural language processing, or image recognition

Open-source tools like TensorFlow and PyTorch play a vital role in accelerating development (TensorFlow, 2024).

Infrastructure and Compute

AI models require massive computing power. This layer includes:

Cloud platforms (e.g., AWS, Google Cloud, Azure)

Hardware accelerators (e.g., GPUs, TPUs)

Edge computing for real-time AI inference at the device level

Without scalable infrastructure, even the most advanced models can’t function effectively.

Application and Deployment

Here, AI capabilities are embedded into products, platforms, or services such as:

Chatbots

Predictive analytics tools

Computer vision systems

Autonomous vehicles

Deployment must consider latency, scalability, and integration with existing systems [4].

Business Integration and Feedback

AI must generate measurable business outcomes. This includes:

Automating workflows

Enhancing customer experience

Generating insights for decision-making

A feedback loop is essential—AI models need continuous refinement based on real-world performance (Deloitte, 2023).

Why the AI Value Chain Matters

Strategic Clarity: Understanding each layer helps businesses pinpoint gaps and invest wisely.

Cross-Functional Collaboration: The value chain highlights the need for coordination between data teams, engineers, domain experts, and decision-makers [5].

Innovation Scalability: A mature AI value chain enables faster and more scalable innovation cycles.

Use Case Example: Healthcare

In healthcare, the AI value chain is transforming diagnostics and treatment:

Data Layer: Medical imaging, EHRs, and patient histories

Model Layer: AI algorithms trained to detect anomalies in X-rays

Infrastructure Layer: Cloud-based systems enabling remote analysis

Application Layer: Decision support systems for radiologists

Business Integration: Faster diagnosis, reduced costs, and improved patient outcomes

Challenges and Considerations

Ethical AI and Bias: Biased data leads to biased outcomes. Fairness and transparency must be baked into the value chain.

Data Privacy: Especially in regulated industries, AI systems must protect personal data.

Skills Gap: The complexity of the value chain requires diverse expertise—data scientists, AI engineers, ethicists, and business strategists.

Conclusion

The AI value chain is more than a technical roadmap—it's a strategic framework for building intelligent systems that deliver real-world value. By investing in each layer—from high-quality data to ethical deployment—organizations can transform AI from a buzzword into a sustainable business advantage. As the AI landscape evolves, those who master the value chain will lead the next wave of innovation.

References

1. World Economic Forum (2022) Building an Equitable AI Value Chain. World Economic Forum.

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2. McKinsey & Company (2023) The AI Bank of the Future: Building the Capabilities to Deliver. McKinsey Digital.

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3. TensorFlow (2024) Build and Train Machine Learning Models. org.

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4. Deloitte (2023) Mapping the AI Value Chain: A Business Perspective. Deloitte Insights.

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5. Nature Medicine (2023) AI in Medical Imaging: Trends and Ethical Considerations. Nature Medicine.

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