Designing AI Products that have an impact: Part 3 of 4

The third stage of the AI design process - select the technology and the data approach for the business process defined in part 2

Third Stage: Define an AI approach

In the third stage of designing AI products that have an impact, it is time to define an AI approach. This involves selecting the technology and the data approach that will be used for the business process defined in part 2. The AI approach should align with the goals and objectives of the product, ensuring that it can effectively solve the identified problem or address the desired outcome.

Defining an AI approach requires a deep understanding of the available technologies and their capabilities. It is important to assess various AI technologies, such as machine learning, natural language processing, computer vision, and robotics, to determine which ones are most suitable for the specific product. Additionally, considering the intellectual property (IP) implications of the chosen AI technology is essential to protect the product's uniqueness and prevent potential legal issues.

Furthermore, the data strategy plays a crucial role in defining the AI approach. It involves determining the type and quality of data that will be used to train and improve the AI models. This includes identifying relevant data sources, ensuring data privacy and security, and establishing data collection and management processes. A well-defined data strategy ensures that the AI product has access to reliable and relevant data to make accurate predictions and deliver valuable insights.

Overall, the third stage of designing AI products focuses on defining an AI approach that leverages the right technology and data strategy to achieve the desired outcomes. By carefully selecting the AI technology and developing a robust data strategy, businesses can maximise the potential of their AI products and create a significant impact in their respective industries.

AI Technology & Intellectual Property (IP)

Technology

AI technology plays a crucial role in the design and development of AI products. It forms the groundwork for the AI capabilities and determines the product's ability to perform tasks, make predictions, and learn from data. When selecting AI technology, it is important to consider factors such as scalability, reliability, and compatibility with existing systems.

Certain decisions need to be made regarding:

Software:

• Should you opt for a custom-built solution or off-the-shelf software?
• Are there existing tools in your organization that can be reused or features that can be enabled?
• Do you need to update or upgrade existing applications?

• What level of availability is required?
• What response times are expected?
• What level of security is needed?
• Who can access the data, and what data?
• Should you host your solution on-premise or in the Cloud?

AI model(s):

• - What type of AI do you need (e.g. generative AI, machine learning, vision, etc)?
• - What model should you choose?
• - Are there pre-trained models that can be implemented, or do you need to build your own?

These decisions are just a sample of the kind you will have to make, and they greatly depend on your organisation's resources and goals.

Intellectual Property

Additionally, intellectual property (IP) considerations are vital in the AI design process. AI products often involve innovative algorithms, models, and architectures that may be eligible for patent protection. Protecting the IP associated with AI technology ensures that the product remains unique and gives the business a competitive edge in the market. It is important to work closely with legal experts to navigate the complexities of IP laws and obtain the necessary protection for the AI product.

AI technology and intellectual property go hand in hand in designing AI products that have an impact. By selecting the right AI technology and protecting its associated IP, businesses can create innovative and valuable AI products that drive growth and success.

Data Strategy

Data strategy is a critical component of designing AI products that have an impact. It is the foundation that supports the development of robust and effective AI models. The success of an AI product heavily relies on the quality and relevance of the data used to train and improve the models.

When designing a data strategy, it is important to recognise the close connection between the data and the algorithms that can be employed with it. The data serves as the fuel that powers the AI models, enabling them to make accurate predictions and deliver valuable insights. Therefore, it is crucial to have a comprehensive understanding of the data available and its characteristics.

Metadata is an essential aspect of the data strategy. It provides a descriptive framework that helps in understanding the nature and attributes of the data. By properly documenting the metadata, businesses can effectively organise and manage their data, making it easier to identify relevant datasets for training the AI models.

In some cases, certain data may already have models trained on it. This pre-existing knowledge can be leveraged to accelerate the development of AI products. By utilising existing models, businesses can save time and resources, allowing them to focus on optimising and fine-tuning the models to match their specific requirements.

There are three main strategies that can be employed in the data strategy: network externalities, system lock-in, and economies of scale. Network externalities refer to the increased attractiveness and value of the data as the size of the community using it grows. This can lead to richer and more diverse datasets, which in turn enhance the performance and accuracy of the AI models.

System lock-in focuses on building customer loyalty and retention. By creating a product that meets the needs and expectations of users, businesses can establish a strong customer base that generates consistent revenue. This enables the collection of more data, which can be used to further improve the AI models.
System lock-in also applies to advertisers, developers and 3rd party application developers.

Economies of scale involve leveraging the benefits of a larger budget for research and development. With a bigger R&D budget, businesses can invest in advanced technologies and techniques to enhance their AI capabilities. This includes exploring new algorithms, frameworks, and methodologies that push the boundaries of what the AI product can achieve.

Businesses that align their AI strategies with these dynamics can secure a lasting competitive edge that is both natural and sustainable in the long run.

Next: fourth stage - tinkering

Stage 4: Developing

After defining an AI approach, the next and final stage in designing AI products that have an impact is developing. Developing involves experimenting with the selected AI technology and data approach to fine-tune and optimise the AI models.

During the developing stage, businesses can conduct iterative testing and refinement of the AI models based on real-world data. This helps identify potential issues, improve accuracy, and enhance the performance of the AI product. Developing also allows for the incorporation of feedback and insights from users or stakeholders, enabling the AI product to better meet their needs and expectations.

Furthermore, the developing stage provides an opportunity to explore advanced techniques and algorithms to further enhance the AI capabilities of the product. This could involve leveraging cutting-edge research, exploring new AI frameworks, or integrating emerging technologies to push the boundaries of what the AI product can achieve.