Are your AI initiatives proceeding at the pace to maintain competitive advantage?

Are your AI initiatives providing measurable value?

Are you able to manage risks and compliance through an organized AI governance framework? 

Naturally, there is increased attention towards the first two bullet points, causing a reduced focus on the third. While AI governance itself is lacking, the other critical factor that most organizations deal with is the lack of alignment between data governance and AI governance. While everyone understands that data is the lifeblood for all forms of AI, even enterprises that have implemented safe and responsible AI practices tend to have a siloed approach to data governance and AI governance, based on my experience working with Fortune 500 corporations.

Data governance

Most enterprises have implemented a data governance tool or, in some cases, multiple tools to manage data quality, data lineage, data security and data retention needs of the organization. However, most large enterprises suffer from the lack of a single source of truth across several domains, as they have tried to keep pace with the emerging technology solutions over the decades, from RDBMS to data warehouses to data lakes. Data proliferation inherently makes it harder to manage and govern the data. Data latency is another issue that impacts use cases that require real-time data. In their rush to jump on the AI bandwagon, organizations tend to use this current state of data that is plagued with issues and hence are unable to derive the full value of their AI investments.

AI governance

As previously noted, most organizations have yet to implement a robust AI governance framework. The ones that have implemented some form of AI governance have a centralized approach through an AI center of excellence. (AI COE). In most cases, these COEs are managed by a chief AI officer (CAIO). The CAIO tends to focus on AI governance considerations such as model governance, bias, toxicity, hallucination, jailbreaks and the like, and not so much on the underlying data, as this is managed typically by the chief data officer.