• With email support, customers learned that certain wording generated quicker responses.
• With web chat, customers discovered they could multitask and disappear in the middle of conversations.
• With omnichannel, customers started channel hopping, expecting the company to remember everything from the previous interaction.
• With knowledge bases, customers often became more informed than frontline agents.
Now, with AI, customers are already learning how to prompt, manipulate, challenge, and test the system.
Some customers are finding ways to get better answers than designers anticipated. Others are discovering weaknesses we never imagined.
Agents have a habit of doing the same thing, finding shortcuts, workarounds, and entirely new ways to use systems once the realities of the work set in.
Every implementation plan contains assumptions about user behavior. Then users arrive. They ask different questions, take unexpected paths, find shortcuts, expose gaps in knowledge and process, and teach us what we failed to anticipate.
In many ways, the customer becomes the final designer of the solution.
That is why technology implementation is never a finish line. It is, instead, the beginning of a learning cycle.
The organizations that succeed are not necessarily the ones that launch first. They are the ones that listen, adapt, and learn fastest after launch.
The customer always gets a vote. History suggests they usually get the last one too.
THE CRM LESSON I NEVER FORGOT
Years ago, a client asked me to review a CRM request for proposal( RFP). CRM was still relatively new to the industry, and the organization had done what many do when a new technology appears.
The client researched every available feature and function they could find and included all of them in the RFP.
As we reviewed the document together, I began asking a simple question: " How will this feature improve the contact center?"
The response was almost always the same. " Why wouldn ' t we want it if it ' s available?" So, I changed the question: " Assume this feature adds hundreds of thousands of dollars to the project. How will you recover that investment?"
The room became very quiet. What followed was one of the most valuable conversations the organization ever had.
• Instead of asking what technology could do, we started asking what business problems needed solving.
• Instead of asking what was available, we started asking what created value.
NOW, WITH AI, CUSTOMERS ARE ALREADY LEARNING HOW TO PROMPT, MANIPULATE, CHALLENGE, AND TEST THE SYSTEM.
We discussed implementation costs, support requirements, customization needs, upgrade cycles, and governance responsibilities.
We also discussed something almost nobody was talking about at the time.
Knowledge.
• Where would it live?
• Who would maintain it?
• How would agents know which information to trust?
The technology itself was never the problem. The assumption that every available feature automatically created value was.
Looking back, I see the same conversations happening today around AI. The technology is different. The thinking is remarkably similar.
ANOTHER GROUNDHOG DAY MISTAKE
One lesson I am learning firsthand from organizations implementing AI today is that the technology itself is rarely the biggest challenge. The challenge is everything surrounding it.
Organizations routinely underestimate the resources required to design, develop, launch, monitor, and continuously improve these solutions.
The software may be purchased in a matter of weeks. But building an effective solution often takes months of operational effort. Sometimes much longer.
Many organizations are learning how to use AI at the same time they are trying to implement it. They are building the airplane while learning to fly it.
22 CONTACT CENTER PIPELINE