AI creates value through a chain of connected pieces: hardware, data, infrastructure, models and applications. It starts with chips and data, passes through building and deploying models, and only pays off when a business uses it to run better, serve customers well or launch something new.
The chain also shows who earns what. Chip makers sell computing power, cloud providers rent out infrastructure, AI developers build the models, and software companies package those models into products. But you don’t have to own every layer to win. A business with specialised data, a real grasp of its customers’ problems and AI built into its core workflows can capture value without building the rest itself.
The stakes are rising. Stanford’s 2026 AI Index reports that global corporate AI investment more than doubled in 2025, and 88% of surveyed organisations said they use AI. Yet AI agent deployment stayed in the single digits across nearly every business function. Plenty of companies have adopted AI tools, but far fewer have woven them into how the business actually runs.
Understanding the AI value chain makes the big decisions clearer: where to invest, what to build in-house, what to buy, and how to tell whether AI is earning its keep.
What is the AI Value Chain?
The AI value chain is everything it takes to turn data and computing power into a useful AI product or business result: the infrastructure, technology, processes and services behind it.
An AI system isn’t like traditional software. It can rely on huge datasets, specialised processors, trained models, serving infrastructure, constant evaluation and ongoing monitoring. And how well it performs depends on the quality of what goes in and the environment it runs in.
McKinsey’s framework for the generative AI value chain splits it into six layers:
- Specialised hardware: the chips and servers that train and run AI models.
- Cloud platforms: the computing, storage and networking services that make that hardware accessible.
- Foundation models: general-purpose models that can generate text, analyse images, write code and more.
- Model hubs and MLOps: the tools for accessing, adapting, deploying and managing models.
- Applications: products that use AI to solve specific problems.
- Services: the expertise behind implementation, integration, training and ongoing support.
These layers don’t run in a neat line. One application might use a third-party model, pull from a private database, call outside tools and run on a cloud platform, all at once.
For business leaders, the framework works as a practical map. It shows what your AI depends on and where a competitive advantage could take hold.
1. AI Hardware: The Computing Foundation
Every AI model runs on physical infrastructure: processors, memory, storage, networking equipment and the facilities that house them.
Graphics processing units (GPUs) are the go-to chips for AI training and inference because they can run huge numbers of calculations in parallel. Other accelerators, such as tensor processing units and custom AI chips, are built for specific workloads.
The hardware layer has four key parts:
- AI chips: processors designed or tuned for machine learning.
- Servers and memory: the systems that run computations and move data quickly.
- Networking: the links that let many processors and servers work together.
- Data centres: the buildings that hold the equipment, cooling systems and power supply.
This layer demands serious capital. The International Energy Agency reported that data centres used around 415 terawatt-hours of electricity worldwide in 2024, about 1.5% of global consumption. Its 2025 base-case projection puts demand at roughly 945 TWh by 2030.
For businesses adopting AI, the takeaway is that costs go well beyond a model subscription. Larger workloads can also bring inference charges, data transfer fees, storage, latency requirements and infrastructure commitments.
Most companies will never make AI chips or build a data centre. What they do need is a clear view of what the computing behind their AI costs, whether it’s available and how it performs.
2. Cloud Platforms and Data Infrastructure
Cloud platforms let organisations use computing power, storage and networking without buying and maintaining their own hardware.
Businesses can rent GPU capacity, deploy models through managed services and scale up or down as demand shifts. Depending on their security, performance and cost needs, they might also run private infrastructure or a hybrid setup.
Data infrastructure matters just as much, because AI is only as good as the information it can reach. That information has to be accurate, relevant and easy to access.
A typical data pipeline involves:
Collecting information from business systems, documents, sensors and outside sources.
Cleaning records and fixing inconsistencies.
Managing permissions, privacy and data quality.
Organising structured and unstructured information.
Getting the right data to the models and applications that need it.
Picture a retailer building an AI system to forecast demand. The model might be technically brilliant, but unreliable inventory records, mismatched product identifiers or gaps in sales history can wreck its predictions. That retailer may get more from fixing its data pipeline than from buying a pricier model.
It’s one of the core principles of the AI value chain: a better model can’t consistently make up for poor business data.
3. Foundation Models and AI Development
Foundation models are trained on broad datasets, so they can handle many different tasks. Large language models generate and analyse text, while multimodal models can work with text, images, audio and other inputs together.
Businesses can use these models directly through application programming interfaces (APIs), adapt them with extra training or connect them to outside sources of information.
There are three common approaches:
- Using an existing model: you connect an established model to your application through an API. It’s often the quickest route, with the lowest upfront cost.
- Retrieval-augmented generation (RAG): the application pulls relevant material from approved documents or databases and hands it to the model when answering a question. This brings in company-specific context without retraining the whole model.
- Fine-tuning: developers train a model further on selected examples so it performs better on a particular task or in a particular domain.
They solve different problems. RAG helps with access to current or proprietary information, while fine-tuning helps a model handle specialised tasks and response styles. Neither guarantees accuracy on its own.
The right choice depends on your use case, how sensitive the data is, the response quality you need, latency and cost.
For many businesses, the advantage won’t come from building a foundation model from scratch. It will come from applying an existing model to a problem that competitors understand less well, or can’t solve as efficiently.
4. Model Hubs, MLOps and Deployment Tools
Between a trained model and a working product sits an operational layer that helps teams manage AI systems day to day.
Model hubs give access to models and related resources. Machine learning operations, or MLOps, covers how teams build, test, deploy and maintain machine learning systems. In generative AI, these practices often overlap with large language model operations, usually called LLMOps.
This layer can include:
Versioning for models and prompts.
Automated testing and evaluation.
Deployment pipelines and rollback procedures.
Access controls and usage monitoring.
Tracking of model performance and cost.
Logging, incident response and updates.
Take a customer-support tool that does well in testing but gives inconsistent answers when users ask unusual questions. Monitoring catches those failures, and evaluation plus controlled updates help the team fix them.
So, running AI in production takes more than picking a model and plugging it into an interface. An organisation needs a repeatable way to test changes, watch behaviour and respond when performance slips.
5. AI Applications: Where Business Value Becomes Visible
The application layer translates AI capabilities into products, services and business processes. It is often the most accessible entry point for organisations that want to use AI without developing their own infrastructure or foundation models.
Common applications include:
|
Industry |
AI application |
Business objective |
|
Healthcare |
Medical image analysis and clinical documentation support |
Assist professionals and reduce administrative work |
|
Banking |
Fraud detection and transaction monitoring |
Identify suspicious activity and manage risk |
|
Retail |
Demand forecasting and product recommendations |
Improve inventory planning and customer relevance |
|
Manufacturing |
Predictive maintenance and visual inspection |
Reduce equipment downtime and detect defects |
|
Logistics |
Route optimisation and shipment forecasting |
Improve delivery efficiency |
|
Professional services |
Document analysis and research assistance |
Reduce time spent on repetitive information tasks |
These applications differ in their technical requirements and risk levels. An AI tool that drafts internal meeting summaries does not require the same validation process as a system that supports medical decisions or evaluates credit applications.
The business case must also account for adoption. A technically capable application may deliver little value if employees do not trust it, its outputs require extensive correction or it creates additional work elsewhere in the process.
Successful implementation usually involves workflow redesign, staff training, appropriate human oversight and clear performance measures.
6. Services, Integration and AI Governance
The services layer connects AI technology to the needs of an organisation. It includes consulting, system integration, implementation, employee training, security assessments and ongoing support.
This layer matters because businesses rarely operate on a single technology platform. AI applications must often work with customer relationship management systems, enterprise resource planning software, document repositories and internal databases.
Governance also belongs throughout the AI value chain, rather than being treated as a final approval step.
The National Institute of Standards and Technology’s Generative AI Profile provides guidance for identifying and managing risks associated with generative AI across its lifecycle. Relevant considerations include inaccurate outputs, privacy, information security, harmful bias and the potential for misuse.
Businesses should define who is accountable for an AI system, which information it can access, when human review is required and how incidents will be handled.
For applications involving sensitive decisions, governance may also require documented testing, audit trails, restricted permissions and a clear process for challenging or correcting outputs.
How the AI Value Chain Works in Practice: A Retail Example
Consider a retailer that wants to reduce stockouts and excess inventory. Instead of treating AI as a standalone software purchase, the business can map the complete value chain.
- Step 1: Data collection. The retailer combines historical sales, current inventory, supplier lead times and seasonal demand patterns.
- Step 2: Infrastructure. Data is stored and processed using existing systems or cloud services.
- Step 3: Model selection. The team selects a forecasting model suited to its products, data and planning requirements.
- Step 4: Evaluation. Predictions are tested against historical periods and compared with the retailer’s existing forecasting process.
- Step 5: Integration. The model’s outputs are incorporated into inventory planning software, where staff can review proposed orders.
- Step 6: Monitoring. The business tracks forecast errors, stockouts, excess inventory and the cost of operating the system.
The retailer should measure the financial results against a baseline, accounting for changes in demand, supplier performance and other factors that influence inventory.
This example illustrates why the AI value chain must connect technical performance with operational results. A model can produce accurate forecasts without reducing costs if the purchasing process does not use them effectively.
How Businesses Can Capture More Value from the AI Value Chain
Companies do not need to control every layer to build a defensible AI business. They need to choose where ownership, specialisation or integration offers the greatest advantage.
Build around a specific business problem
Start with a measurable operational challenge, such as reducing document-processing time, improving forecast accuracy or shortening customer-response times. Define the existing baseline before introducing AI.
Use existing infrastructure where possible
Buying computing hardware or developing a foundation model can require substantial capital and specialist expertise. Managed services and established models may provide a faster route to testing demand.
Develop proprietary data and domain knowledge
A generic AI model is available to many competitors. A well-maintained proprietary dataset, deep industry expertise or a workflow that integrates multiple business systems may be harder to replicate.
Measure total cost, not just model price
Include model usage, computing, storage, integration, security, human review, maintenance and employee training. A cheaper model can become more expensive if it requires frequent corrections or additional processing.
Track financial and operational outcomes
Useful measures include cost per completed task, error rates, processing time, customer retention, revenue per employee and the cost of human review. Select metrics that directly reflect the business case.
Establish governance before scaling
Set access controls, test outputs, define escalation procedures and assign responsibility for failures. Risk controls should reflect the consequences of an incorrect output and the sensitivity of the information involved.
AI Value Chain Trends to Watch in 2026
The AI value chain is changing as models become more capable, inference demand grows and businesses experiment with systems that can carry out multi-step tasks.
1. Application-specific AI is becoming more important. Organisations can build specialised products around existing models rather than competing directly in the expensive business of training frontier models. Industry knowledge and access to relevant data remain important differentiators.
2. Inference economics are gaining attention. Training a model is only one part of its cost. Once deployed, the model may be called thousands or millions of times. Model selection, caching, routing, hardware efficiency and usage controls can significantly affect operating expenses.
3. AI agents are moving into business workflows. Agents can use tools, retrieve information and perform sequences of actions with varying degrees of autonomy. However, Stanford HAI’s 2026 AI Index found that agent deployment remained in the single digits across nearly all surveyed business functions. This indicates that broad organisational AI adoption does not automatically translate into widespread autonomous execution.
4. Energy and infrastructure constraints matter. The IEA’s analysis highlights the growing electricity requirements of data centres. Businesses evaluating large-scale AI workloads must consider capacity, cost, reliability and efficiency alongside model performance.
5. Governance is becoming an operational capability. Organisations need repeatable processes for evaluating models, monitoring deployed systems and documenting risks. Governance can help businesses scale useful applications while limiting avoidable failures.
These developments reinforce a broader point: the AI value chain is not fixed. As infrastructure and models become more accessible, opportunities may shift towards specialised applications, integration, proprietary information and the ability to deliver measurable results.
Conclusion
The AI value chain explains how hardware, cloud infrastructure, data, foundation models, operational tools, applications and services work together to turn artificial intelligence into business value.
For most organisations, the immediate opportunity is not to own every layer. It is to identify the parts of the chain that influence their costs, customer experience and competitive position, then invest accordingly.
A company may rely on external chips and models while creating a differentiated product through proprietary data, industry expertise and integration into an essential workflow. Another may gain more by improving its data infrastructure and deployment practices than by adopting a more advanced model.
The most effective AI strategy connects technical choices to a clear business objective, measures the full cost of delivery and establishes accountability for results.
Author’s Note
In my view, the most useful way to evaluate an AI investment is to work backwards from the business outcome rather than forwards from the technology. A company that begins with a new model may spend months experimenting without establishing what success should look like. A company that starts with a specific problem can make more disciplined decisions about the data, computing resources, model and integration it actually needs.
This difference also changes how businesses should think about competitive advantage. Access to AI tools alone is unlikely to remain a lasting differentiator as more organisations adopt similar capabilities. The stronger opportunity lies in combining those tools with proprietary knowledge, reliable processes and a clear understanding of customer needs.
That is where the AI value chain becomes more than a technical framework: it becomes a way to decide where a business should invest, what it should own and how it can turn AI capability into sustained economic value.
