A PRACTICAL GUIDE TO AI ONBOARDING
BOX
For AI initiatives to move forward and succeed, it is critical that your contact center staff must be brought on board. Here is a practical guide with steps to help them on this journey.
1. Communicate early and often
Agents should never encounter a new AI tool for the first time on go-live day. Communicate ahead of deployment, explain what is coming, and keep updating as timelines develop. The rumor mill moves faster than most implementation plans. Get ahead of it.
2. Explain the why for them, not just for the business
Most AI rollouts are announced in terms of business outcomes. Agents do not work for the business case.
Explain specifically how the tool will make agents’ working days easier, what tasks it will take off their plate, and how it will support them in difficult conversations. If they cannot see what is in it for them, engagement will be low from day one.
3. Create champions
Identify engaged agents early, bring them into the product before wider rollout, and give them time to become genuine experts. These people become your peer trainers and your advocates when skepticism surfaces on the floor.
Agents trust other agents. A champion programme is one of the highest-return investments you can make in any AI deployment.
4. Give agents proper time to learn
Going live and expecting adoption to follow is one of the most consistently ignored pieces of advice in contact center technology rollouts, and one of the most expensive mistakes.
Schedule dedicated training time before go-live. Build in practice sessions where agents can make mistakes with the tool before using it live with customers. Rushing this stage does not save time. It creates problems that take far longer to fix.
5. Plan ongoing coaching and follow-up
Training is not a one-time event. Plan structured follow-up sessions, drop-in coaching, and team leader check-ins for the weeks and months after go-live.
Confidence and usage patterns shift as agents actually live with the technology, and when follow-up is not planned, performance quietly dips and nobody formally owns the problem.
6. Ask them
Build formal feedback mechanisms in from the start. Have a structured survey at key points post-launch. Provide drop-in sessions where agents can raise questions without hierarchy in the room. Institute a clear channel for flagging when something is not working.
Agents are closest to the customer and closest to the tool. Their feedback is not a niceto-have. It is how you catch problems before they become expensive ones.
7. Be honest about job impacts
If AI is going to change roles or significantly alter what agents do day to day, that needs to be communicated with honesty and care.
Agents will figure it out. They always do. If they feel misled, you will lose trust that is almost impossible to rebuild.
Fear that is acknowledged and addressed can be managed. Fear that is ignored becomes resistance, and resistance becomes failure.
TECHNOLOGY DEPLOYMENT
• Attrition increases, because people leave environments where they feel threatened or undervalued.
• Experienced agents take institutional knowledge with them.
• Recruitment and training costs accelerate from hiring and onboarding replacement staff.
Consequently, the AI investment meant to generate ROI ends up sitting on top of a disengaged workforce. The promised, hoped-for efficiency gains never arrive.
There is also a subtler risk that rarely surfaces in boardroom conversations. When staff are excluded from transformation, they become passive recipients of change rather than active participants.
As a result, they stop flagging when AI is producing wrong answers. The feedback loop that makes AI better over time ceases to exist: if it had a chance to be formed in the first place.
The organizations most focused on reducing their dependency on people through AI consistently end up discovering that getting the people part right matters more than ever.
The change management component of AI deployment is consistently underfunded and underweighted. But in mature deployments, it is arguably the most important element.
The technology is increasingly commoditized. The ability to embed it in a way that actually changes how people work is not.
That fear starts earlier than most leaders realize. Long before go-live, agents are reading the headlines, listening in on vendor presentations, and drawing their own conclusions.
In the absence of honest communication, those conclusions are rarely positive. By the time the technology lands, resistance is already baked in.
Transparency matters here. Agents who understand what AI is scoring and why, and who can see how that connects to their own development, engage with it differently than those who experience it as an opaque monitoring layer.
CONTINUED ON PAGE 33
30 CONTACT CENTER PIPELINE