Genesys Cloud - Main

Hi community 

A stat that stuck with me this week: according to MIT Technology Review Brasil, 73% of Brazilian companies already discuss GenAI in decision-making forums but only 7.9% have achieved full integration with their internal systems.

That gap says a lot about where most CX operations actually are.

We've moved past the question of whether AI belongs in customer experience.

 The harder question now is whether the architecture behind it can actually execute not just respond.

In my experience, the difference comes down to a few things:

• Does the AI remember what already happened, or does it treat every interaction as the first?
• When it takes an action, does it confirm the action actually worked?
• Is there governance defining how far it can go safely?

Without those, you don't really have autonomous AI you have a conversational bot with a new label.

I'm curious how others here see it:

What's the biggest blocker you've seen keeping companies stuck in the "talking about AI" phase instead of actually executing? Is it data readiness, governance, internal alignment, or something else?

Would love to hear your take

Hello Martha, 

I think you've highlighted three of the biggest factors for successful AI adoption: memory, action verification, and governance. In my experience, where many organizations get stuck isn't necessarily the technology itself, but everything around it. Data quality, privacy, and security concerns are often at the top of the list, followed closely by organizational alignment.

The organizations making the most progress are the ones that start with a focused use case, establish governance early, and treat AI adoption as both a business and technical initiative. Rather than trying to transform everything at once, they solve a specific problem, prove the value, and then expand from there. That approach tends to build confidence, drive adoption, and create a stronger foundation for broader AI initiatives over time.

I love conversations like this and would love to see what others have to say as well. 

Cheers, 

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Cameron
Online Community Manager/Moderator
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Following this conversation because I find the topic interesting and want to see the insights.

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Breno Canyggia Ferreira Marreco
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Hi Marta,

From my experience in contact centers, the biggest blocker is integration.

Many companies already have AI that can answer questions, but very few have AI that can reliably execute actions across CRM, telephony, ticketing, and other business systems.

The challenge is usually not the AI itself, but the complexity of the ecosystem around it.

That's where most projects get stuck.

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Guilherme Hernandez Hubner
Senior Support Analyst
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Great point, @Martha Blanco Velasco

For me, the biggest blocker is not only the AI model itself, but everything around it. Many companies want to adopt AI, but they do not always have clean processes, integrated systems, or clear governance to let AI actually execute something safely.

In CX, this becomes very visible. It is one thing to answer a customer with a good message, but it is another thing to check customer data, understand the previous context, update a CRM, create a case, validate if the action worked, and know when to stop and transfer to a human.

I think a lot of companies are still in the "talking about AI" phase because they are trying to apply AI before defining the business process behind it. Without good data, clear rules, system integration, and ownership, AI becomes more of a smart assistant than a real operational tool.

So, for me, the main blocker is a mix of data readiness, governance, and internal alignment. The technology is moving fast, but the operational structure behind it is not always ready at the same speed.

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Arthur Pereira Reinoldes
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