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 

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The Never Normal: When Change Becomes the New Normal The Never Normal: When Change Becomes the New Normal Reflections on leadership in times of constant change "This period is not going to disappear." I heard this sentence in June during the Change ... View this on Medium >

 

#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.

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Fabíola Freitas
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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 :)

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Valentina Postelnicu
VP Advisory Services
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Original Message:
Sent: 05-20-2026 10:43
From: Fabíola Freitas
Subject: AI Transformation and leading through continuous change

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.

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Fabíola Freitas
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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.

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Phaneendra
Technical Solutions Consultant
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Hi Phaneendra,

You're absolutely right, AI transformation is far less about technology and far more about helping teams adapt to continuous change without overwhelming people. Striking that balance between sustained, ongoing change and the kind of experimentation that becomes routine over time is essential. And yes, it all starts with culture and mindset. I especially loved how you framed it: treating AI as an evolving capability rather than a finished product. That perspective is spot‑on. Thank you as well for sharing such practical examples. This is exactly where many organisations struggle - turning intent into action, building the initial momentum, and then sustaining it beyond the first few iterations.

I'm also glad you highlighted Copilot, workflow automation, and AI insights, because each of these can be broken down into small, manageable steps that make adoption far easier. Take Copilot alone: you can start quickly with summarisation, then layer in knowledge surfacing, followed by wrap‑up codes, customised outcomes, next‑best‑action, and so on. When starting from workflows, automation becomes a natural next step for processes that require an action to close a customer request. And AI insights will continue to reveal new opportunities for both automation and personalisation.

These are fascinating times, and I really appreciate your thoughtful contribution to the discussion.

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Valentina Postelnicu
VP Advisory Services
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Original Message:
Sent: 05-22-2026 19:19
From: Phaneendra Avatapalli
Subject: AI Transformation and leading through continuous change

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.

------------------------------
Phaneendra
Technical Solutions Consultant
------------------------------

Hi all,

Really enjoying this discussion 🤩. One thought I would add is that AI transformation may require us to manage two rhythms at the same time: a stable delivery rhythm and an experimentation rhythm.

Teams need routines they can trust, especially when they are expected to deliver consistently. But with AI, those routines also need to be flexible enough to evolve quickly. So perhaps the goal is not to create a "final" way of working, but to create a repeatable way of updating the way of working.

For me, that means treating AI-enabled workflows almost like versioned products. A prompt, agent, workflow, or Copilot use case should have a clear purpose, owner, validation criteria, and review cycle. Then the team knows what is currently recommended, what is being tested, and what has been retired or replaced.

I also think it is important to distinguish between experimentation and adoption. Experimentation creates ideas; adoption creates value. The bridge between the two is usually a simple operating rhythm: identify a workflow friction, test an AI-supported approach, validate it with the team, measure whether it improves quality or effort, and then decide whether it becomes part of the standard routine.

That could also help with the challenge of continuous change. Instead of every new AI capability feeling like a disruption, it becomes part of a structured improvement loop. The routine is allowed to evolve, but the process for evolving it stays predictable.

And finally, I think we should be very explicit about what does not change: human accountability, quality standards, customer outcomes, ethical boundaries, and business context. AI can help us move faster, but trust comes from knowing where human judgement remains essential.

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Rikke Knudsen
Principal CX Advisory Consultant
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Spot on Rikke! Fully agree. I specifically like the bridge between experimentation and adoption done through a simple operating rhythm. Good food for thought! 

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Valentina Postelnicu
VP Advisory Services
------------------------------

Original Message:
Sent: 05-28-2026 09:57
From: Rikke Knudsen
Subject: AI Transformation and leading through continuous change

Hi all,

Really enjoying this discussion 🤩. One thought I would add is that AI transformation may require us to manage two rhythms at the same time: a stable delivery rhythm and an experimentation rhythm.

Teams need routines they can trust, especially when they are expected to deliver consistently. But with AI, those routines also need to be flexible enough to evolve quickly. So perhaps the goal is not to create a "final" way of working, but to create a repeatable way of updating the way of working.

For me, that means treating AI-enabled workflows almost like versioned products. A prompt, agent, workflow, or Copilot use case should have a clear purpose, owner, validation criteria, and review cycle. Then the team knows what is currently recommended, what is being tested, and what has been retired or replaced.

I also think it is important to distinguish between experimentation and adoption. Experimentation creates ideas; adoption creates value. The bridge between the two is usually a simple operating rhythm: identify a workflow friction, test an AI-supported approach, validate it with the team, measure whether it improves quality or effort, and then decide whether it becomes part of the standard routine.

That could also help with the challenge of continuous change. Instead of every new AI capability feeling like a disruption, it becomes part of a structured improvement loop. The routine is allowed to evolve, but the process for evolving it stays predictable.

And finally, I think we should be very explicit about what does not change: human accountability, quality standards, customer outcomes, ethical boundaries, and business context. AI can help us move faster, but trust comes from knowing where human judgement remains essential.

------------------------------
Rikke Knudsen
Principal CX Advisory Consultant
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Great read, thanks for sharing @Valentina Postelnicu!

From my perspective, especially after the pandemic, we started living through a much faster rhythm of change. It feels like every time we begin to stabilize, something new appears and pushes teams to adapt again. In that sense, the "Never Normal" concept makes a lot of sense to me.

When it comes to AI, I think the biggest challenge is balancing experimentation with real adoption. It is easy to test new AI features, but much harder to make them part of consistent workflows that people actually trust and use every day.

In practice, I believe teams need to create a safe space for continuous experimentation, but also define clear routines to evaluate what is working, what is not, and what should become part of the standard process. Otherwise, AI initiatives can become just isolated tests instead of real improvements.

For me, the key is to keep walking together: involving business teams, technical teams, and end users in the process, collecting feedback constantly, and adjusting the workflows based on real usage. Continuous change is already part of our reality, so the focus should be less about "going back to normal" and more about building better ways to adapt together.

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Arthur Pereira Reinoldes
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