Generative Artificial Intelligence Strategy Approach
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Artificial intelligence has been around since the 1950’s, yet even for skeptics, recent advances in Generative AI (GenAI) have significantly moved the needle forward. Due to massive early adoption, Goldman Sachs estimates that Generative AI could raise global GDP by 7% within 10 years [1]. However, while the focus has been on GenAI, it’s important to note that any GenAI strategy requires the right data and the ability to govern the data effectively with the GenAI tool. A strong GenAI strategy will include the following key components.
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(A) GenAI Engine: The engine enables you to train, validate, tune and deploy generative AI foundation models and machine learning capabilities with ease, and build AI applications in a fraction of the time with a fraction of the data. Key use cases built on the engine enable sophisticated Q&A, summarization of content, classification of content and generation of content for specific purposes.
(B) Data Engine: The GenAI engine needs large amounts of data. It’s necessary for enterprises to scale analytics and AI with a fit-for-purpose data store supported by querying with open data formats to access and share data. It’s essential to connect to data at rapid speed, quickly get trusted insights and reduce your total costs. There will be existing data engines in most enterprises, but they need to be extended to support data from multiple sources including data warehouses and data lakes quickly and, in a cost-effective manner. Use cases include deploying AI/ML at scale, applying real-time analytics/BI and streamlining data engineering.
(C) Governance Engine: The GenAI engine needs data that will drive responsible, ethical decisions across the business. This includes the ability to direct, manage, and monitor your organization’s AI activities, strengthen your ability to mitigate risk, manage regulatory requirements and address ethical concerns. Key use cases include lifecycle governance, risk management and regulatory compliance.
In summary, it‘s essential to consider the above three components as part of your GenAI strategy — the ability to ensure you are using the right data, which can be trusted and the right GenAI engine which is integrated with the data and governance engine. These technologies should be built in an open environment using the best AI and cloud technologies available running in a hybrid cloud environment with access to innovation from the open community.