By Utpal Mangla, IBM (LI) & Mathews Thomas, IBM (LI)

Artificial intelligence has been around since the 1950’s, but even to skeptics, the recent advances with Generative AI (GenAI) significantly move the needle forward. There has been massive early adoption and 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 is important to remember that any GenAI strategy needs the right data and be able to govern this data effectively with the GenAI tool. A strong GenAI strategy will include the following key components.

(A) GenAI Engine: The engine that 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 this engine enable sophisticated Q&A, summarization of content, classification of content and generation of content for a specific purpose.

(B) Data Engine: The GenAI engine will need large amounts of data. It is 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 is 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/business intelligence (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.  The key use cases include, lifecycle governance, risk management and regulatory compliance.

In summary, it is essential that you 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 need to be built in an open environment using the best AI and cloud technologies available running in a hybrid cloud environment with access to the innovation from the open community.

Reference:
[1]https://www.goldmansachs.com/insights/pages/generative-ai-could-raise-global-gdp-by-7-percent.html