Scaling AI agents with trustworthy data
Business and technology leaders need no convincing that the time of agentic AI is here. Organizations are rapidly adopting agents, and few executives doubt the technology’s potential to transform work. But many organizations find that realizing the desired return on investment (ROI) from AI hinges on having the right foundation, with inadequate infrastructure and data…
In an era where agentic AI is rapidly gaining traction among businesses, there's a growing recognition that the technology's potential to revolutionize work depends heavily on the foundation it's built upon. The shift from mere question-answering to decision-making and action-taking has made AI agents demand access to a broader array of data, encompassing both structured and unstructured formats, all while needing seamless integration with the organization's operational systems.
However, legacy data systems, despite recent updates, are often ill-equipped to meet these increased demands.
A recent survey of 300 data and technology executives sheds light on the challenges faced by organizations in this regard. The findings reveal that, on average, AI agents have access to only 45% of a company's data, a figure that drops to 30% or less in organizations classified as "data laggards." Conversely, a select group of "data leaders" manage to grant agents access to over 70% of their data, leading to greater success in deploying AI agents.
Trust in the decisions made by AI agents is closely tied to the readiness of the data systems. Currently, only around half of the surveyed organizations trust the accuracy and relevance of their AI agents' decisions. In stark contrast, all the "data leaders" express complete trust in their agents' decisions, underscoring the critical role of a reliable data foundation in ensuring the reliability of AI.
These data leaders have found it easier to achieve scalability and speed with their AI agents. Three out of every four data laggards report that legacy data systems hinder the scaling of AI agents and delay the decision-making process. However, the data leaders have largely overcome these constraints, with only 8% still facing such issues. The urgency is evident - within two years, all respondents plan to utilize agentic AI, with 69% expecting it to be widely adopted.
To make this transition successful, the report emphasizes the need to eliminate constraints within data systems. Without addressing these data system limitations, agentic AI will be unable to deliver the promised speed and efficiency gains. Data access and contextual understanding emerge as the top priorities in enabling scaling among all respondents. Enhancing data governance with business context and automating data management processes are also high on the priority list for data leaders.
Written by urgent.news from MIT Technology Review's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
