Rajesh Gangadharan argues that agentic AI will hit an organizational operating-model wall
Why faster answers do not automatically create faster decisions
Faster answers expose slower organizations
Rajesh Gangadharan argues that the main obstacle facing Agentic AI will not be model performance, but the ability of organizations to absorb and act on faster answers.
Agents can compress analysis from weeks to seconds. However, that acceleration creates limited value when decisions still depend on weekly reviews, multiple approval layers and fragmented ownership.
Five barriers to enterprise adoption
- Decision processes may be too slow to absorb real-time analysis.
- Poor data quality reflects unresolved ownership and governance issues.
- Broader access to information can challenge existing organizational power structures.
- Large-scale autonomy is risky without bounded use cases and measurable outcomes.
- Agents initially create additional work in evaluation, auditability, permissions and role redesign.
Build an agent-capable operating model
Gangadharan recommends beginning with narrow, high-signal use cases that have clear boundaries and measurable outcomes. These capabilities should be progressively integrated into the organization’s operating rhythm before broader autonomy is considered.
The objective is therefore not simply to deploy agents. It is to build an operating model capable of governing, interpreting and executing AI-supported decisions.
The original article is available on LinkedIn.