Agentic AI in operations: where autonomous systems create measurable value — and where they don't
A cross-industry review of 140 deployments separates durable gains from pilot theatre.
MIT Sloan Management Review (sample) · Practitioner research · 15 Oct · 14 min full read
Reviewing 140 enterprise deployments, the authors find agentic AI delivered durable gains mainly in narrow, well-instrumented workflows: procurement exceptions, demand-signal triage and supplier communications. Broad 'autonomous operations' ambitions underperformed. The strongest predictor of value was not model capability but clean process data and clear escalation rules to humans.
Why You Should Read This
Demonstration connectionsYour operating plan lists 'AI-enabled supply chain' as a 2027 priority. This gives you an evidence-based shortlist of where to start and a warning against broad mandates.
- Where should the first AI-enabled supply chain pilot sit?
- What data foundations are needed first?
- Start with procurement exception handling as a first agent pilot.
- Audit process-data quality before vendor selection.
- Define escalation thresholds before deployment, not after.
Connections are illustrative sample data. Uploaded documents have not been analysed.
Key findings
- 0162% of durable value came from three workflow types.
- 02Clean process data predicted success more than model choice.
- 03Explicit human escalation rules cut failure rates by roughly half.