AI automation fails when you skip process intelligence
Most enterprise AI automation projects start with the wrong question. Leaders see a manual workflow, assume the goal is to run it faster, and start shopping for tools. The deeper problem is that they rarely understand the workflow they actually have.
Writing at InformationWeek, Anjali Garg draws on large-scale marketplace operations experience to make a pointed case: AI automates the process you have, not the process you wish you had. That includes gaps, exceptions, informal decisions, and stale data that never show up in a process diagram. Automate without seeing those realities and you get a fragile workflow moving faster, not a better one.
What process documentation misses
Documented workflows describe the happy path. Production operations live in the exceptions. A workflow mapped as a clean approval sequence may actually involve missing-field reviews, duplicate checks, manual validation, policy interpretation, risk-based routing, and follow-ups. Those hidden steps often protect the business from compliance exposure, fraud, and bad data.
Garg argues that useful pre-automation signals include queue aging, cycle time, exception frequency, rework patterns, missing-field rates, escalation reasons, and override points. These are design inputs, not noise to be cleaned up later.
Not every manual step should be automated
The distinction Garg draws between automate, assist, and escalate is the most operator-useful framework in the piece.
- Automate: low-risk, rule-based, repeatable tasks with clear inputs and outputs
- Assist: summarization, anomaly detection, recommendation drafting, prioritization
- Escalate: ambiguous cases, compliance exposure, irreversible actions, policy exceptions
This matters especially for AI agents. Once an agent can retrieve information, reason across sources, and trigger workflow actions, the organization needs to be explicit about what authority it has. Preparing a recommendation is not the same as executing it.
Feedback loops keep automation honest
Garg’s third point is about durability. AI automation should not end at deployment. Where the system hesitates, where users override it, where recommendations get rejected, where downstream teams correct earlier automation: these are signals worth tracking. Overrides clustering around one issue type may mean new rules or better training data. Rising downstream rework may mean the automation is moving work forward before it is ready.
The framing here is worth internalizing for any operator running agentic workflows: the goal is not to prove AI works once. It is to build a system that keeps learning from operational reality.
I’m founder of BizStack at Cagri Sarigoz LLC and a passionate advocate for entrepreneurs.
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