Artificial intelligence (AI) is transforming many industries and businesses. But with so many different AI technologies and applications available, it can be difficult to know where to start.
One way to think about it is to consider the AI value chain. The AI value chain is a model that describes the different stages of AI development and deployment. The six stages of the AI value chain are:
- Computer hardware:?This is the physical infrastructure that AI applications run on.
- Cloud platforms:?Cloud platforms provide the resources and services that AI applications need to run.
- Foundation models:?Foundation models are large language models (LLMs) and other AI models that can be used to build a wide variety of AI applications.
- Model hubs:?Model hubs are repositories where AI models can be shared and discovered.
- Applications:?AI applications are the products and services that are used by consumers and businesses.
- Services:?AI services are the tools and resources that help developers to build and deploy AI applications.
Where are the picks and shovels in general AI?
The picks and shovels in general AI are the tools and resources that help AI developers to build and deploy AI applications. These tools and resources can be found at all stages of the AI value chain.
Here are some examples of picks and shovels in general AI:
- Computer hardware:?Graphics processing units (GPUs) and field-programmable gate arrays (FPGAs) are two types of computer hardware that are optimized for AI.
- Cloud platforms:?All major cloud providers offer AI services, such as machine learning frameworks, pre-trained AI models, and managed services for running AI applications.
- Foundation models:?Foundation models are large language models (LLMs) and other AI models that can be used to build a wide variety of AI applications. Some popular foundation models include GPT-3, LaMDA, and Megatron-Turing NLG.
- Model hubs:?Model hubs are repositories where AI models can be shared and discovered. Some popular model hubs include Hugging Face Hub and TensorFlow Hub.
- Applications:?AI applications are the products and services that are used by consumers and businesses. Some examples of AI applications include self-driving cars, virtual assistants, and fraud detection systems.
- Services:?AI services are the tools and resources that help developers to build and deploy AI applications. Some examples of AI services include machine learning frameworks, cloud-based AI development tools, and AI consulting services.
How can better data usage help companies solve problems or capitalize on opportunities?
Better data usage can help companies to solve a variety of problems and capitalize on a variety of opportunities. For example, better data usage can help companies to:
- Improve the quality of their AI models:?AI models are trained on data, so better data usage can lead to better AI models.
- Reduce the cost of AI:?By using data to optimize AI applications, companies can reduce the cost of running AI.
- Improve the adoption of AI:?By using data to understand the needs of employees and customers, companies can develop AI applications that are more likely to be adopted.
- Scale AI applications:?By using data to monitor and analyze the performance of AI applications, companies can identify and address bottlenecks and other challenges that can prevent them from scaling AI applications.
Pinpoint specific business challenges where new insights from better data usage could have an outsized impact.
Here are some specific business challenges where new insights from better data usage could have an outsized impact:
- Customer segmentation and targeting:?By using data to understand customer behavior and preferences, companies can develop more effective customer segmentation and targeting strategies. This can lead to increased sales and improved customer satisfaction.
- Product development:?By using data to understand customer needs and market trends, companies can develop products that are more likely to be successful. This can lead to increased revenue and improved profitability.
- Risk management:?By using data to identify and assess risks, companies can develop more effective risk management strategies. This can reduce costs and improve the bottom line.
- Operational efficiency:?By using data to identify and address inefficiencies, companies can improve their operational efficiency. This can lead to reduced costs and improved profitability.
How to choose where to enter the AI value chain
The best place to enter the AI value chain depends on your company's strengths, resources, and goals. If you have expertise in computer hardware, you may want to consider selling hardware that is optimized for AI. If you have expertise in cloud computing, you may want to consider offering cloud-based AI services. If you have expertise in a particular industry, you may want to develop AI applications that are specific to that industry.
No matter which stage of the AI value chain you choose to enter, it is important to focus on providing value to your customers.
