Trust and accuracy
When IDC asked organizations about agentic AI, a top challenge that customers pointed to is accuracy. Much of that accuracy problem starts upstream, with legacy content systems that do not preserve and extract the structure or meaning agents need.
Token costs
This risk is already showing up. Sixty-seven percent of organizations say their total AI agent spend over the past 12 months ran over the original budget or forecast, and 73% now call excessive AI spending a major risk to their plans over the next 12 months
Finding the use cases that deliver ROI
Adoption has outpaced return. For every AI project in production delivering value, there is one that isn't. Only half of enterprises' AI projects in production deliver measurable outcomes.
Improved trust and accuracy for agentic AI
Modern retrieval methods return more relevant, contextually accurate results because the content arrives at the agent already governed and prepared for AI, not raw and unstructured.
Less manual burden on content-heavy processes
Much of the work that used to require a person to open, read, and route a document can move to an agent once the content underneath it is prepared for AI.
Clearer, faster proof of ROI and a path to expand
Full observability into outcomes and costs gives IT and the business the data they need to prove ROI per use case, and governance and audit built into every action make the next use case easier to justify than the last.
Modernization instead of "like for like" migration
Moving content into a new repository relocates the problem. Investing in a context foundation gives the organization something to build on.
Lower token costs
When content is AI ready upstream, the model has less to do downstream. Pair that with cost-aware model orchestration and durable execution, and the token bill drops accordingly.
Future-proofing
A platform built for interoperability integrates faster today and adapts faster tomorrow, as new agent frameworks continue to arrive.