Genesys Casual

Dear community,

Came recently across this read: The Never Normal: When Change Becomes the New Normal and interested in your point of view and experience with continuous change and continuous improvement practices in your organisations. And specifically on how you manage to continue experimenting with AI while also achieving a consistent adoption of new and improved AI enabled workflows? Is this something being discussed in your teams? And how does it work in practice, what are the new routines or systems you have put in place to support the process of changing while walking together?

Thanks,

Valentina 

#General #AITransformation #ChangeReadiness #AdvisoryServices

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Valentina Postelnicu
VP Advisory Services
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Hi Valentina,

Great topic.

Yes, this is something we have been discussing as a team. One practice we have been using is having each team member share real situations where AI has helped in their daily work.

I think this is important because it moves the conversation away from theory and brings it into practical examples: what worked, what saved time, what still needed human review, and what could become part of a standard workflow.

In my experience, AI adoption becomes more consistent when it is organized by workflow, not used only as a generic tool.

One approach that has worked well for me is creating specific AI agents or prompt configurations for different activities, such as meeting summaries, functional documentation, test scenarios, client-facing emails, discovery questions, flow reviews, and technical explanations.

Each prompt or agent has a clear context, expected output, tone, restrictions, and validation criteria. This helps make the AI output more consistent and easier to review.

In practice, AI saves time by reducing the "blank page" effort, organizing scattered information, improving documentation structure, and helping identify points that need validation before development or go-live.

But I still see human review as essential. AI supports the workflow, but the business context, technical validation, and final decisions need to remain with the team.

For me, the key is continuous improvement: share practical use cases, define the task, create a reusable prompt or agent, test the output, adjust, validate with the team, and then make it part of the routine.

Many thanks for sharing your experience, Fabiola! Great way of approaching it and I fully agree with the practical examples and the focus on workflows. I also resonate with the idea of a library of small AI agents or prompts until the most used or most useful ones are filtered and make into the routine. 

The thing that I still find complicated is the idea that the new routine is only valid for maybe few weeks until something new is coming and how to continue including innovation in a predictable and structured way without disrupting the routine while it still needs to deliver results. But maybe is just me being easily distracted :)

Hi Valentina,

Really interesting topic, especially because AI adoption often seems less about the technology itself and more about helping teams adapt to continuous change without overwhelming people.

One thing I've noticed when working with Genesys AI capabilities is that smaller iterative improvements tend to gain much better adoption than trying to transform everything at once. Features like Agent Copilot, workflow automation, or AI insights become much more successful when teams can clearly see how they reduce effort or improve the customer/agent experience in day-to-day work.

I also think experimentation works best when teams are comfortable treating AI as an evolving capability rather than a finished product. In practice, that usually means:

• continuous feedback loops
• testing with smaller groups first
• measuring outcomes early
• and adjusting workflows incrementally instead of aiming for "perfect" immediately

The pace of change is definitely increasing, so building a culture that is comfortable learning and adapting continuously seems just as important now as the technology itself.