AI Readiness Demands Buy-in, Tech, and Governance

With the increasing interest in natural language processing, predictive analytics, and other augmented intelligence capabilities, more than just tooling is required for enterprises to properly prepare.

Data management and analytics have entered a new era, with AI being the main focal point of users’ interests and vendors’ product development. However, readiness for AI is crucial for organizations before they can harness the power of cutting-edge capabilities.

Image: The intersection of AI and analytics

In the past, self-service analytics required modernizing data infrastructures and developing data governance frameworks. Now, in the era of AI, organizations need to modernize again, according to Fern Halper, vice president of research at TDWI.

“Organizations are trying to get ready for AI because many of them are viewing it as an imperative for something like digital transformation, competitiveness, operational efficiency, and other business drivers,” Halper said.

Ensuring Readiness

Ensuring readiness for developing and deploying AI models and applications is a process that includes proper data preparation, operational readiness, and AI governance.

Image: Data preparation for AI

Support for AI begins with organizational buy-in. Those who want to use AI to surface insights and inform decisions need to get support from the executive suite that trickles down throughout the rest of the organization.

The Rise of AI

The new era of AI in data management and analytics began when OpenAI released ChatGPT, marking a significant improvement in generative AI capabilities. This has the potential to change the way analytics is used, enabling true natural language processing and automating repetitive tasks.

Image: The power of generative AI

As a result, many vendors have made generative AI a focus of their product development, building tools such as AI assistants that can be used in concert with an enterprise’s data to enable natural language queries and analysis. Simultaneously, many enterprises have made generative AI a focus of their own development, building models and applications that can generate insights and automate tasks.

Seven Benefits of Generative AI

Assuming there’s organizational support, AI readiness begins with the data at the foundation of any model or application. Models and applications trained with high-quality data will deliver high-quality outcomes.

Image: The importance of high-quality data

Accessing unstructured data, such as text and images, and transforming that unstructured data to make it discoverable and usable, takes a modern data platform. This includes combining that data with a large language model, such as ChatGPT or Google Gemini, to apply generative AI.

AI Governance

Governance is critical to protect organizations from potentially exposing sensitive information, violating regulations, or simply taking actions without proper due diligence. Guidelines must be in place to limit who can access certain AI models and applications and how those can be used.

Image: The importance of AI governance

“For AI to succeed, it’s going to require governance,” Halper said.

Governance encompasses diverse data and multiple environments to address AI readiness. It also includes oversight of the various types of AI, including generative AI, to determine whether outputs are toxic or incorrect as well as whether there’s bias in a model or application.

Conclusion

AI readiness requires a commitment to preparing for AI development and deployment. This includes organizational buy-in, proper technology, and good governance. By understanding the business problems that can be solved with AI and demonstrating the potential results, organizations can get executive support and start their AI journey.

Image: The importance of AI readiness