Genesys Casual

I was recently at a cusotmer event / conference and was walking around the room to gauge the primary reason for AI adoption..

I had suspected the usual answers:

• Increase NPS
• Increase containment 
• Improve FCR and others

But I was surprised to find out that many customers admitted that the message to use / adopt AI is coming from the leadership and they are trying to fit KPIs to AI adoption.. Wondering if thats what you all have also observed.?

Hi Jai,

I've observed something very similar over the last couple of years.

Initially, AI initiatives were often tied directly to contact center metrics such as containment, AHT, FCR, NPS, and cost reduction. Today, many organizations are starting from a different place: executive sponsorship.

In many cases, the directive is simply "we need an AI strategy" or "we need to adopt AI," and the operational teams are then tasked with identifying where it can deliver measurable value.

What I've seen separate successful programs from unsuccessful ones is whether they move quickly from an AI objective to a business objective.

The most successful deployments tend to focus on problems first:

• Reducing agent after-call work
• Improving knowledge access
• Automating repetitive back-office tasks
• Increasing self-service for high-volume intents
• Reducing onboarding time for new agents

The AI becomes the mechanism, not the goal.

Ironically, some of the highest ROI projects I've seen were not customer-facing at all. Agent Assist, summarization, knowledge recommendations, and workflow automation often delivered value faster than ambitious containment targets.

My observation is that AI adoption is increasingly leadership-driven, but long-term success still depends on connecting it back to measurable operational outcomes rather than treating adoption itself as the KPI.

Hello, @Jai Nargas

I've seen both scenarios.

Many customers start their AI journey with very clear business goals, such as improving NPS, reducing AHT, increasing containment rates, improving FCR, or helping agents be more efficient. In these cases, AI is viewed as a tool to solve a specific problem, and success is usually easier to measure.

However, I've also seen organizations where the push comes primarily from leadership. They know AI is becoming an important part of the market and want to adopt it, but they don't always have a clear use case or KPI in mind at the beginning. Sometimes the goal is simply to modernize the operation, stay competitive, or show customers and stakeholders that the company is investing in innovation.

In my experience, the most successful projects are the ones that eventually connect AI adoption to a measurable business outcome. Even if the initiative starts with a top-down mandate, it becomes much easier to demonstrate value when the team can tie it back to metrics like NPS, AHT, FCR, containment, or agent productivity.

Jai, great question - and yes, I have observed something similar.

AI adoption across personal, consumer, and business environments is starting to converge. People are experiencing AI in their everyday lives, while at the same time business leaders are pushing organizations to understand how AI can create value at scale. Because of that, the conversation is no longer only about traditional contact center KPIs like NPS, containment, FCR, AHT, or cost reduction.

What I am seeing is that many organizations are being asked by leadership to "do something with AI," and then teams are working backward to identify the right KPIs and use cases. That is not necessarily a bad thing, but it does require a thoughtful approach. AI should not be adopted simply because it is the current mandate; it needs to be tied to real business outcomes, employee experience, customer experience, and operational effectiveness.

From the Genesys perspective, I think the opportunity is to help customers move from AI experimentation to AI orchestration - where AI is not just a tool, but part of how they improve experiences across the organization. That includes helping employees be more effective, reducing friction in customer journeys, improving decision-making, and creating better outcomes for both customers and agents.

We also need to anticipate outcomes we may not fully expect yet. With AI, the impact can be exponential when it is applied responsibly and aligned to the right use cases. The key is helping customers connect leadership ambition with practical execution, measurable value, and a clear experience strategy.