tech
Scaling AI agents with trustworthy data
How companies are freeing themselves of legacy data systems to power AI agents that deliver trusted, autonomous action.
TL;DR
- Agentic AI adoption is rapid, but inadequate infrastructure and data are significant barriers to realizing ROI.
- Legacy data systems struggle to meet the demands of AI agents needing enterprise-wide, real-time data access with business context.
- A Gartner prediction suggests AI agents will augment or automate 50% of business decisions by 2027, increasing the urgency to address data bottlenecks.
- A survey of 300 data and technology executives reveals that 'data leaders' are more successful with agentic AI due to fewer legacy system limitations.
- On average, AI agents only access 45% of company data, falling to 30% or less for 'data laggards,' while 'data leaders' ensure access to over 70%.
- Only about half of organizations trust their AI agents' decisions, compared to 100% of 'data leaders,' indicating data readiness is key to trust.
- Two-thirds of 'data laggards' report legacy systems limit AI agent scaling and speed, whereas only 8% of 'data leaders' face these constraints.
- All respondents plan to use agentic AI widely within two years, making the elimination of data system constraints crucial for desired efficiencies.
- Improving access to structured and unstructured data, enhancing data governance with business context, and automating data management are top priorities for scaling AI agents.