Podcast Summary: The Agile Brand with Greg Kihlström®

Episode #806: NiCE Cognigy VP of Marketing Alan Ranger on Agentic Customer Service
Date: February 2, 2026
Guest: Alan Ranger, VP Marketing at Nice Cognigy
Host: Greg Kihlström

Episode Overview

This episode spotlights the transformative role of agentic AI in customer service, exploring how large brands are moving beyond basic chatbots to proactive, task-completing automation. Greg and Alan dive into the necessity of agentic customer service, how it augments—not replaces—human agents, and what organizational leaders need to consider when implementing next-generation AI. The conversation is rich with real-world examples, strategies for data and measurement, future-forward predictions, and actionable insights for CX leaders.

Key Discussion Points & Insights

The Strategic Shift to Agentic AI

[02:01-03:49]

• Traditional AI and chatbots have made interactions more human-like, but often lack real task-completion capability.
• Agentic AI brings “agency”—the ability to independently perform tasks end-to-end, integrated into enterprise systems.
• This is now a necessity due to scalability challenges, customer expectations, and the need for automation to go beyond surface-level engagement.
• Example: Lufthansa’s transition from deterministic to agentic AI showed a leap in customer experience and operational reasoning.

Alan Ranger [02:39]:
"The whole sort of meaning behind agentic is that these AIs have agency to actually do something... It's all about completing tasks and doing it properly."

AI as a Force Multiplier, Not a Replacement

[03:49-06:49]

• Agentic AI aims to automate repetitive, low-value tasks never meant for humans (e.g., password resets, account lookups).
• Human advisors are elevated to brand ambassadors handling nuanced, high-value conversations.
• AI can collaborate with, support, and even prompt human agents, functioning both as customer and agent-facing support.
• Talent shortages and pandemic-influenced attrition mean automation fills gaps, especially in surge situations (e.g., flight cancellations).

Alan Ranger [04:26]:
"It's just about automating the tasks that humans never should have done... You should really think about using the human advisors as being your brand ambassadors and doing the work of highest value."

Practical Differences: Agentic AI vs. Traditional Chatbots

[07:36-10:10]

• Deterministic bots are effective for simple, predefined flows but fail when customers deviate from the script.
• Agentic AI leverages large language models to deeply understand context and intent, handling complex, multi-part interactions and passing tasks to the right expert (AI or human) as needed.

Alan Ranger [07:36]:
"The deterministic ones only built to follow the flow and can only do that. We just have to. I don't understand which is the most frustrating thing in the world, talking to an automation... it shouldn't now with the agentic stuff because it's using the large language model. It understands absolutely everything that's being said to it..."

• Real-world success: A major global coachline launched agentic AI and quickly saw improved user experience over their deterministic predecessor.

The Crucial Data Component

[11:06-12:33]

• Most AI failures stem from bad or outdated data, not model flaws.
• Agentic AI requires access to current, accurate, enterprise-approved data, not just generalized web knowledge.
• Organizations need processes for continuous data updates to prevent high-profile errors (e.g., erroneous refunds due to policy changes no one told the AI about).

Alan Ranger [11:06]:
"95% of AI projects fail... pretty much because the data underneath isn't correct... when you build the AI agent, it's grounded only on the data you give it. You can't allow it to use its knowledge of the rest of the world to have the conversation. It has to be completely anchored and grounded on the data."

Rethinking Metrics and Measurement

[15:22-17:58]

• Traditional contact center metrics (e.g., Average Handle Time) are less relevant; agentic AI offers infinite scalability and zero “hold time.”
• Shift toward measuring outcomes: % of tasks resolved end-to-end, customer satisfaction (CSAT), and customer lifetime value.
• Agentic AI unlocks outbound engagement (sales, retention) that deterministic bots can’t manage, driving up repeat business and loyalty.

Alan Ranger [15:44]:
"What we're seeing more and more is people are measuring by outcome, so they're actually throwing away all of the traditional measurements... the classic one is average handling time. It really doesn't matter anymore... what we're seeing is a lot of people actually measuring it on percentage of tasks completed, fully automated, end to end..."

From Customer Service to Lifetime Customer Value

[19:03-22:48]

• Beyond solving problems, agentic AI enables brands to upsell, cross-sell, proactively engage, and build persistent 1:1 relationships (e.g., via messaging triggered by a QR code on a CPG product).
• Outbound use cases: Automated AI places calls, manages scheduling, and only connects human advisors for high-probability resolutions, driving efficiency and results in banking and subscription businesses.

Alan Ranger [21:08]:
"It's the marketeer's dream, it's a personalized one to one relationship with every single one of your consumers. And I think that's really going to be where the future is going to take us..."

The Next Three to Five Years in Customer Service

[22:48-25:24]

• Expect “self-building” automations: AI monitors interactions, identifies top intents, and constructs new workflows with minimal human intervention.
• Generative, conversational websites will dynamically adapt content and UI to the customer’s real-time dialogue.
• The era of fixed “phone trees” and frustrating chat loops is fading; customer journey becomes hyper-personalized and efficient.

Alan Ranger [23:59]:
"We'll be able to work out which are the most popular intents and then automatically build an AI automation using the agentic capabilities to actually resolve those issues...a much better customer service. Get rid of all the hold times."

Balancing Automation and the Human Touch

[25:24-27:37]

• Human oversight remains essential: regulated industries (finance, healthcare) require humans-in-loop, and background review of AI interpretations.
• Agentic AI supports “designed empathy” (e.g., tone, politeness, cultural fit); can even detect sarcasm or sentiment, enhancing customer experience at scale.
• Technology enables the “human touch” without sacrificing efficiency.

Alan Ranger [25:54]:
"There will always be human oversight and there will always be the capability to be transformed to a human advisor. Sometimes there may be a human in the loop without even actually having direct contact with the consumer..."

Rapid Adoption, Staying Agile, and Organizational Learning

[28:17-29:41]

• Alan predicts increasing focus on which tasks still need humans and ongoing industry resistance to full automation, but adoption rates are accelerating.
• Staying agile means in-person engagement: participating in industry events, listening to customers and peers, staying close to market needs, and rapidly iterating.

Alan Ranger [29:00]:
"I spend as much time as I can either presenting on stage or just joining in the forums and communities of the people whose issues we're solving...becoming a trusted peer and part of the communities that everybody's in. And yeah, that keeps me excited every day when I wake up."

Memorable Quotes & Timestamps

• "It's just about automating the tasks that humans never should have done."
  — Alan Ranger [04:26]

• "We just have to. I don't understand which is the most frustrating thing in the world, talking to an automation... it shouldn't now with the agentic stuff because it's using the large language model."
  — Alan Ranger [07:36]

• "95% of AI projects fail... pretty much because the data underneath isn't correct."
  — Alan Ranger [11:06]

• "Average handling time... really doesn't matter anymore."
  — Alan Ranger [15:44]

• "It's the marketeer's dream, it's a personalized one to one relationship with every single one of your consumers."
  — Alan Ranger [21:08]

• "There will always be human oversight... Sometimes there may be a human in the loop without even actually having direct contact with the consumer."
  — Alan Ranger [25:54]

Notable Moments & Examples

• Real-world switch from deterministic to agentic AI for global transportation brand led to faster deployment and improved customer satisfaction. [07:36-10:10]
• Outbound AI caller in banking moved conversion rates from 20% to 80%. [19:03-21:08]
• AI-driven retention for a subscription fashion brand quickly outperformed human agents. [21:08-22:48]
• CPG brands leveraging QR codes and messaging for direct customer engagement, shifting from retail-only to direct relationships. [21:55-22:48]

Timestamps for Important Segments

• [02:01] – Defining Agentic AI and the business imperative
• [04:26] – AI as a force multiplier for human agents
• [07:36] – Practical customer journey contrast: deterministic vs. agentic AI
• [11:06] – Data readiness and pitfalls in AI deployment
• [15:44] – Rethinking measurement, focusing on outcomes
• [19:03] – Beyond service: upsell, retention, and new CX touchpoints
• [23:59] – Future predictions: self-building automations and conversational websites
• [25:54] – Humanity at scale: maintaining a human touch in AI-powered CX
• [28:17] – Upcoming trends and organizational agility strategies

Conclusion

This episode makes clear that agentic AI is revolutionizing customer service and CX at scale, delivering utility that far surpasses traditional chatbots. Success depends on strategic alignment, robust data foundations, sustained human oversight, and metrics that emphasize true customer outcomes. Future-forward brands should see agentic AI as both a force multiplier and a relationship builder, not just an efficiency tool.

For more on Alan Ranger or Nice Cognigy, see the links in the show notes.