A faster response time can hide a slower-growing problem. That is the warning at the centre of a new framework from Sadek El Assaad, aimed at UAE founders rushing to bring AI into customer operations without first fixing who owns what happens when things go wrong.
El Assaad points to a startup that introduced AI into part of its customer operations workflow. Response times fell, the backlog shrank, and on paper the automation looked like a clear win. But underneath, difficult cases were reaching the wrong people faster than before. The organisation had never been clear about who owned a customer issue once it moved beyond the routine. Automating the workflow did not fix that ownership problem. It just meant more exceptions moved through unclear handoffs at higher speed.
Defining the cost of correction
El Assaad calls this the cost of correction. "By the cost of correction, I mean the time, money and management attention required to identify, reverse and resolve work that automation gets wrong, sends down the wrong path or leaves unresolved," he says.
Most business cases for AI, he notes, focus on the visible gains: fewer manual tasks, shorter response times, lower processing costs, higher output per employee. What they miss is that a process can become cheaper to run while becoming more expensive to fix.
Automation cannot fix ownership
The deeper issue, according to El Assaad, is organisational rather than technical. "Automation can route responsibility, but it cannot create accountability," he says. Speeding up a workflow does not resolve ambiguity about who is responsible when a case falls outside the routine path — it simply moves that ambiguity faster.
His advice to founders is to look before they scale. "Before you scale a workflow with AI, make sure you know who owns what happens when the process stops being routine," he says. That means understanding where ownership is clear, where exceptions tend to occur, and what actually happens when the automated flow hits something it cannot resolve on its own.
Signals worth watching
El Assaad recommends founders track a specific set of signals that reveal correction cost building up behind the scenes: repeat contacts, reopened cases, manual overrides, escalations, duplicated work and senior management intervention.
The warning sign, he says, is a particular combination: if repeat contacts, reopened cases, escalations and duplicated work are rising while processing time is falling, the automation may be moving work around rather than actually removing it.
He also stresses that scalability depends on more than a quick first step. "A scalable process needs a clear escalation path, not just a faster first step," El Assaad says.
For UAE startups under pressure to show efficiency gains from AI adoption, the framework is a reminder that speed and cost metrics can mask a growing backlog of unresolved, misrouted or duplicated work. The fix, in El Assaad's view, starts before automation — with clarity over ownership, not with the technology itself.
