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Options for Implementing AI and Key Considerations

Technological advances, like AI, can present significant opportunities for businesses. It can also pose a significant threat. While AI is not a new concept, the topic of AI has become more commonplace. Depending on the industry, using AI can be straightforward, while industries with more complexity require a more thorough deep-dive and strategic approach.

There are two key considerations when determining how to implement AI: the sensitivity of data handled and the level of control required, as well as internal resource availability and expertise, which then determine one of six options.

Key Considerations

1. Sensitivity of data and control

  • High: Data is highly confidential (e.g., healthcare, finance, defense). It requires maximum control over data and AI models.
  • Moderate: Data contains some sensitive information, but doesn't require the strictest control.
  • Low: Data is less sensitive, and control is less of a concern.

2. Resource availability and expertise

  • High: Requires a significant budget, in-house AI experts, and a robust infrastructure.
  • Moderate: Requires a reasonable budget, some AI expertise, and a willingness to use managed services.
  • Low: Great with a limited budget and minimal AI expertise; rely on readily available tools.
Considerations On-Premise / Private AI Hybrid AI Public Cloud / Open AI Custom AI Hybrid AI with some API Public API AI with basic use
Data sensitivity and control required High High / Med Med Med Low Low
Resource availability and expertise High High / Med Med Med Low / Med Low
Most likely business Enterprise Enterprise SMB / Enterprise Enterprise Solopreneur / SMB Solopreneur / SMB

Options

Option 1: On-Premise/Private AI

Build your own AI lab. You have total control over your data. This is a super-secure option, but expensive. You can deploy AI models and infrastructure on-premises or in a private cloud. You would use self-hosted models, federated learning, and secure data enclaves.

Examples

  • Use its own servers to run TensorFlow or PyTorch models for fraud detection.
  • Use a private instance of a large language model (LLM) like Llama 2, running on their own hardware.
  • Deploy AI applications using Docker and Kubernetes within a secure, isolated data center.

Tools that may be used

Option 2: Hybrid AI Approach

A mix of your own and cloud services, secure data handling, balanced control, moderate cost, and enhanced privacy. You would combine on-premises, edge, and cloud AI components.

Examples

  • Use edge devices (local servers) to process patient data near the source, sending only anonymized summaries to the cloud for analysis.
  • Employ differential privacy techniques to add noise to datasets before sharing them with third-party AI services.
  • Use a mix of on-premises AI for sensitive data and cloud-based APIs for less sensitive tasks.

Tools that may be used

  • Edge AI frameworks (e.g., TensorFlow Lite)
  • Differential privacy libraries (e.g., Google Privacy)
  • Local data processing servers
  • Annonymization

Option 3: Public Cloud / API AI

Rent AI tools from big companies. It is cheap, easy, and enables fast deployments, however, you are reliant on their (standard) security. You can leverage pre-trained models and AI APIs from public cloud providers and use APIs for specific tasks, and ensure careful data processing agreements (DPAs).

Examples

  • Use Google Cloud's Natural Language API for sentiment analysis of customer reviews.
  • Use Amazon Rekognition for image analysis in marketing campaigns.
  • Use Azure cognitive services for translation of customer support documents.

Tools that may be used

Option 4: Custom AI Development

Build everything from scratch. You develop AI models and in-house solutions; it is tailored and you own the proprietary IP. This, however, is expensive and time-consuming.

Develop AI models and solutions in-house.

Examples

  • Develop custom AI algorithms for autonomous navigation using in-house AI engineers.
  • Create a proprietary AI model for drug discovery.
  • Build a custom LLM fine-tuned on their own data.

Tools that may be used

  • Custom-built AI models
  • In-house AI infrastructure
  • Specialized hardware

Option 5: Hybrid AI with Some API

Mix of self-built and pre-built AI. This would be a combination of in-house development API and AI usage. It will balance control and faster development speed.

Examples

  • Use an open source computer vision library like OpenCV for basic image processing, but use a cloud API for complex object recognition.
  • Use a locally hosted model for customer classification, and a cloud API for automated language translation.
  • Use a pre-trained model as a starting point, and fine-tune it with their own data on their own servers.

Tools that may be used

Option 6: Public API AI with basic usage

Simple, ready-to-use AI tools. Cheap, fast, but little data control.

Examples

  • Uses an online AI tool for automated product recommendations.
  • Use a chatbot platform like Dialogflow or ManyChat for customer support.
  • Use an online AI writing tool to create marketing copy.

Tools that may be used