“
Embracing AI
continued...
AI without data is like a car
The potential of AI and machine learning in enhancing data management and transformation is vast. The automation of data quality checks, AI-driven monitoring, and the application of Large Language Models (LLM) hold promise for the future and supply high levels of excitement for technology leaders today. Particularly notable is the utilization of AI to identify and rectify data quality issues early in the data engineering process, showcasing the significant downstream impacts. A few technology leaders highlighted how the rapid advancement of Generative AI is putting companies in a difficult spot. They have to balance very real data security risks with the need for innovation. While many companies do not yet have an enterprise AI plan, their employees are already using AI as individual contributors – whether Grammarly is helping them write emails or ChatGPT is debugging their code. As employees become more empowered with AI tools, enterprise technology leaders need to create systems to keep their business’ data strategy consistent and secure. In other words, the effectiveness of AI and machine learning (ML) hinges on a robust data management infrastructure that encompasses data aggregation, curation, and considerations of privacy, governance, and provenance.
with no fuel, and using only the
publicly available model without
any enrichment is about as
personalized as public
transportation. You’ll get there,
but never any quicker than your
competitors. Data engineers,
data scientists, business analysts,
and strategists are all crucial for
a successful AI program.”
Krissy Tripp Senior Director of Decision Science at Concord
CIO INSIGHTS REPORT
24
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