The long-term advantage of privately deployed artificial intelligence (AI) is its ability to provide businesses with a deeper level of control, cost-effectiveness, and flexibility. Here are the benefits, challenges, and specific examples of private AI deployments.

Benefits of private AI deployment

• Cost effective: Private AI deployments provide a more predictable and efficient cost structure through resource sharing models, such as sharing GPU, network, and memory resources. For example, enterprises can avoid the unpredictable costs of cloud services and realize cost savings, sometimes running AI services on-premises for only one-third to one-fifth of the cost of cloud options.
• Control and compliance: Private AI deployments give enterprises full control over the storage and processing of data, which is critical for industries that require strict compliance with data protection regulations, such as finance and healthcare. For example, a financial institution can process sensitive financial data locally without violating privacy regulations.
• Operational flexibility: Private AI deployments provide greater operational control, allowing companies to choose hardware and models based on demand, rather than being constrained by cloud service providers. For example, a manufacturing company can choose the AI model and hardware that best fits its production line based on its needs.
• Avoid tech debt: Private AI deployments support a modular and platform-based approach that enables businesses to quickly adapt to new technologies and avoid long-term dependence on outdated technologies. For example, a tech company can quickly integrate the latest AI algorithms to keep its products competitive.
• Business Value realization: Private AI deployments can help organizations achieve quantifiable business results by demonstrating the direct return on investment of AI through specific business cases. For example, one retail company deployed AI to optimize inventory management, reducing inventory costs and improving customer satisfaction.

The challenges of private AI deployment

• Initial investment and maintenance costs: Private AI deployments require large initial investments, including the construction of hardware, software, and infrastructure. In addition, companies will need to regularly update and maintain their AI systems to ensure their performance and security.
• Technical complexity: Private AI deployments involve complex technical management, including hardware maintenance, software updates, and security. Companies need to make sure their private AI systems are resilient to cyberattacks and keep their data secure.
• Skills required: Private AI deployments require specialized technical teams to manage AI systems, which may require companies to hire or train people with AI and data science skills. Scalability issues: As business needs change, private AI systems may need to scale, which can lead to additional cost and complexity.

Illustrate by example

Benefit Example: A hospital optimizes its medical image analysis system with a private AI deployment. With the ability to control the processing and storage of data, hospitals are able to ensure the security and compliance of patient data, while improving the accuracy and efficiency of diagnostics.

Example challenge: In the same hospital, the initial cost of a private AI deployment includes expensive servers and security systems. In addition, hospitals will need to regularly update their AI models to adapt to new medical technologies and changing regulatory requirements.

Through these examples, we can see that private AI deployments bring significant benefits to enterprises, but also come with a host of challenges. Companies need to weigh the pros and cons of private AI deployments based on their business needs and resources to achieve the best return on technology investment.