Who’s Flying the Plane?
Why AI Deployments Succeed or Fail in Contact Centers
A Practical Readiness Framework
Opening
You would never accept this design in aviation. So why are we accepting it in enterprise systems?
We talk about AI as a co-pilot, but in practice it is often deployed without clearly defining who is in command, how decisions are governed, or where accountability sits. In any environment where reliability and trust matter, that would not pass review. The same standard should apply here.
The real issue with enterprise AI
Most AI programs in contact centers do not struggle because the technology is weak. They struggle because AI is introduced into operating environments that were never designed to support it.
Tools are deployed quickly. Expectations are high. Results are mixed. Adoption fades. Leaders move on to the next initiative.
This pattern is not driven by a lack of innovation. It is driven by a lack of readiness.
AI reflects the maturity of the system it enters. It does not correct it.
Research from Forrester shows that only 32 percent of CX organizations have the data literacy required to use AI responsibly. That gap alone explains why so many deployments stall after launch.
What “co-pilot” actually implies
A co-pilot supports the person flying the plane. They assist with monitoring, provide recommendations, and help manage workload. They do not own the destination or the outcome.
When AI is positioned as a co-pilot, three conditions must already exist:
When any of those are missing, the problem is not AI. It is system design.
The three conditions required for AI to work
1. Clear command authority and operating model
Before introducing AI, organizations need to answer basic questions:
In many contact centers, these questions are not clearly answered. Agents are accountable for outcomes, but authority is vague. That creates hesitation. Under pressure, people default to what feels safest, which often means bypassing the tool.
AI works when the operating model is explicit. Decision rights, escalation paths, and accountability need to be defined before scale, not after issues surface.
AI cannot compensate for an operating model that avoids ownership.
2. Reliable data and usable context
AI depends entirely on the quality of the information it receives. If customer data is fragmented, if knowledge is outdated, or if journey context does not persist across channels, AI output will be incomplete or misleading.
This is not a model issue. It is a foundation issue.
Research from McKinsey consistently shows that weak data foundations are one of the primary reasons AI initiatives fail to scale, regardless of model sophistication.
In contact centers, this shows up as:
AI does not know when its inputs are wrong. Humans do. That only works if the underlying data can be trusted enough to support real decisions.
3. Are your pilots ready for their co-pilot? Human capability and AIQ
AI readiness is not just a systems question. It is also a people question.
Even with a clear operating model and reliable data, AI will underperform if the humans using it are not prepared to work alongside it. This is where AI Quotient, or AIQ, matters.
AIQ refers to a person’s ability to use AI effectively across real tasks. That includes understanding what AI is good at, where it tends to fail, how to frame inputs, and how to validate outputs under pressure. Research cited by MIT shows this capability is distinct from general intelligence or technical skill and can be developed over time.
In practical terms, AIQ shows up in small but meaningful ways:
Low AIQ looks different. Tools are used superficially or avoided entirely. Generic responses slip through. Agents either overtrust the system or bypass it.
This is not a training failure in isolation. It is often a signal that AI was introduced before people were ready to use it safely and effectively.
The key point is this. You can have a capable co-pilot and still struggle if the pilot has not been trained to work with it.
AIQ does not replace the need for a strong operating model or clean data. It depends on them. People build confidence when systems behave predictably and when responsibility is clear.
Organizations that succeed treat AIQ development as part of readiness, not something to fix after rollout. Capability is built through real scenarios, peer learning, and clear guidance on when AI should be trusted and when it should not.
Why many deployments stall
A common pattern is to deploy AI first and address governance later. Tools are layered onto existing workflows. Metrics focus on usage instead of outcomes. Responsibility is implied rather than defined.
Research from Upwork Research Institute shows that many employees experience AI as additional work rather than relief. That reaction is rational when systems are bolted on instead of integrated.
When trust erodes, adoption stops. Not gradually. Quickly.
A note on agentic AI and autonomy
Agentic AI will continue to evolve. Over time, systems may take on more responsibility. That does not eliminate the need for governance.
Even in aviation, increased automation led to more rules, clearer procedures, and stronger oversight. Autonomy has never meant absence of control.
As AI takes on more responsibility, the operating model, data standards, and accountability structures need to become stronger, not weaker.
The question is not whether AI could someday handle more of the workload. The question is whether organizations are designing systems that are worthy of that trust.
What readiness actually looks like
Organizations that see consistent results from AI tend to do a few things well:
AI is not the strategy. It is an enabler. Its impact depends on the environment it is placed in.
Closing
If this were aviation, the plane would be grounded. In enterprise AI, we often label the same design as progress.
AI will continue to improve. That is not the limiting factor.
The limiting factor is whether organizations are willing to design systems with the same discipline they apply anywhere reliability, trust, and outcomes matter.
The most important question is not whether AI can do the work.
It is who is responsible when it does.
Sources
Forrester
MIT
McKinsey
Upwork Research Institute
Garry Kasparov
