#CIO, Understand and leverage Generative Artificial Intelligence (GenAI). Model development and data.

??? Generative Artificial Intelligence (GenAI) is a rapidly evolving technology that offers intriguing possibilities for client value creation. Companies developing products and services based on Generative Artificial Intelligence must master the technologies to be used in the short term before investing in this field in the long term.

Synopsis

??? Understand Generative Artificial Intelligence implications to plan your investments and strategy. We recommend, as part of the development of products and services based on Generative Artificial Intelligence, that you:

• ??? Create a deployment and testing plan.
• ??? Initially focus on the most widespread applications, those already providing real added value to users.
• ??? Establish an investment roadmap that prioritizes different applications.
• ??? Strive to create a competitive advantage.
• ??? Defer future long-term investments in Generative Artificial Intelligence technologies.

Each of the 25 technologies and trends on the implications radar falls into one of the following four themes.

1. Model-related innovations
2. Model efficiency and artificial intelligence security
3. Model development and data
4. Applications based on artificial intelligence

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Theme 3: Model development and data

??? This theme addresses some of the important phases and decisions related to the development of a generative Artificial Intelligence model. The following technologies and trends fall into this category:

• ??? Knowledge graphs are machine-readable data structures representing knowledge from physical and digital worlds, including entities and their relationships, following a graph data model.
• ??? Multimodal generative Artificial Intelligence models leverage multiple types of source and target data, such as images, videos, sound, text, and numerical data, within a single generative model.
• ??? Generative Artificial Intelligence-generated synthetic data are a class of data often derived and extrapolated from a real dataset but artificially generated rather than collected from real events.
• ??? Scalable vector databases offer vector (semantic) search capability and are used in conjunction with large language models to implement the model's ability to respond to natural language with personalized or domain-specific information.
• ??? Generative Artificial Intelligence engineering tools allow businesses to implement models faster, balancing both governance and time-to-market deadlines.

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??? Finally, to achieve your business goals, focus on short-term available technologies before investing in Generative Artificial Intelligence in the long term, to determine how best to combine the technologies and trends affecting this Artificial Intelligence.