Most enterprise voice AI demos succeed. That is not where deals fail. The demo proves the system can speak. Enterprise buyers assume that part is solvable. What they are trying to determine later is whether they are willing to own the consequences once the system is live. That distinction explains why many voice AI deals slow down or stall after initial excitement. Responsibility shifts.
Product teams step back. Security, legal, and operations step in. The questions change. The perspective that follows draws on Omar El-Sayed’s work at that transition point, where AI systems meet real users, real constraints, and real accountability.
When Automation Becomes Accountability
Voice agents do not just converse. They confirm information. They trigger workflows. They make commitments in real time. When those actions are automated, the organisation inherits the risk. That is the moment buyers start asking different questions. In diligence, buyers rarely ask whether the system is impressive. They ask whether it is defensible.
Security teams want to map failure modes and data flows. Legal teams want to know whether actions can be reconstructed after an incident. Operations teams want clarity on escalation and handoff. Procurement wants to understand who owns outcomes when automation is involved. What they are testing is not intelligence. It is predictability.
Designing for Scrutiny, Not Just Performance
El-Sayed’s work is shaped by repeated exposure to that scrutiny. He has been selected into selective builder communities such as the Lovable Ambassadors and has served as a judge at competitive international hackathons for Lovable and Tech: Europe, where early-stage systems are evaluated under compressed, failure-prone conditions.
Across those settings, the pattern is consistent. Teams optimise for what performs well in a demo. Buyers optimise for what survives review. “The demo gets you interested,” El-Sayed said. “Approval depends on what happens when the system is wrong, and someone has to explain why.”
That perspective changes how autonomy is designed. In the systems El-Sayed works on, risk-bearing actions are not triggered solely by inferred intent. They require explicit confirmation. When inputs are unclear or contradictory, the system does not guess. It pauses, routes, or escalates. Escalation is treated as a designed outcome, not an exception discovered after launch.
These behaviours are not left to model discretion. They are structured. Agent behaviour is defined as explicit states with known transitions, so similar situations produce consistent outcomes across calls. Tool failures, partial data, and interruptions follow predefined paths, making behaviour repeatable and inspectable rather than improvised.
The same discipline applies to evaluation. Instead of optimising for average-case performance, systems are tested against the conditions that break them: interruptions, backend timeouts, misheard entities, and users who change objectives. Evaluation focuses on edge cases with clear pass/fail criteria, not subjective transcript quality. “A lot of teams think buyers want the agent to do more,” El-Sayed said. “What they really want is to know when it will stop.”
The Real Trade-Off in Enterprise Voice AI
This mirrors how enterprise decisions are actually made. Confidence erodes quickly when vendors cannot explain system behaviour under failure without hand-waving. Conversely, clear limits and documented escalation paths often accelerate approval. There is a trade-off. Systems designed with stronger controls move more slowly and automate fewer paths upfront. Many enterprises accept this willingly. In high-trust or regulated environments, predictable behaviour is more valuable than aggressive automation.
As voice AI becomes embedded in customer operations, deals are rarely decided at the demo. They are decided later, during diligence, when organisations determine whether they are comfortable standing behind the decisions an automated system will make. El-Sayed deals with the part most vendors try to skip: proving reliability under pressure. By designing boundaries, escalation paths, and failure behaviour up front, he focuses on the questions buyers ask after enthusiasm fades, when approval depends not on how impressive a system sounds, but on how confidently an organisation can defend it once it is live.
Photo: Omar El-Sayed
