An exploration of how generative and agentic AI are transforming customer experiences and turning them into brand-winning services.

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Next-Gen Contact Centers: Powered by AI

<h3>Introducing the new customer service experience</h3>
Understanding and resolving problems are the basic ingredients for quality customer service. But companies struggle to deliver the kind of fast, seamless and low-effort resolutions they expect in today’s connected world. A key challenge has been platform proliferation caused by the growth of devices and channels. This has resulted in a fragmentation of experience.
But we are finally at a turning point.
Advances in generative AI (Gen AI) now mean that customer intent can be understood with unprecedented accuracy, immediately identifying the gnawing “why” behind customer contact. Indeed, AI is <a href="https://www.frog.co/designmind/empowering-frontline-retail-workers-with-ai" rel="noopener">empowering frontline workers</a>. The introduction of agentic AI takes this to another level. Gen AI shares this intent, while agentic AI transforms it into resolution action in real-time. The result is a harmonized, efficient and personalized <a href="https://www.frog.co/designmind/omni-chaos-to-omni-cohesion-crafting-a-customer-centric-multichannel-strategy">omni-channel</a> customer service experience and solution.
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<h3>An agentic advantage</h3>
Combined, Gen AI and agentic AI are redefining how to deliver customer service at scale. It’s a paradigm shift for how these service experiences are designed and executed. We can break down how this AI synergy delivers benefits in four ways:
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<ul>
<li>Conversational: Customers speak or write naturally; systems understand through intent analysis</li>
<li>Intent-driven: Every customer interaction can then be precisely routed for resolution</li>
<li>Autonomous: Actions are taken autonomously, semi-autonomously or via a live agent</li>
<li>Scalable: Consistent, high-quality service at speed, and at a fraction of the cost of more traditional customer service solutions</li>
</ul>
To illustrate, imagine a customer connects to an AI-powered digital agent with a request to change their service plan. Instantly, this chatbot makes an intent analysis, measuring their current service plan with all the available alternatives on offer. This information is immediately shared with an AI agent, which autonomously creates a response allowing the customer to see–in real-time–their current service plan compared to all others, measuring things like cost, what each offer contains and notable differences.
Within seconds, the customer request is transformed into an actionable solution. In cases where a customer is still not satisfied, there is always the alternative to escalate to a human agent, who can then take the information shared by AI systems and engage with the customer on a higher level. This means that human agents have time to focus on more complex cases, allowing for greater human connectivity with a focus on the resolution.
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<h3>Directing mixed market signals</h3>
Despite these benefits, companies are still doing customer service transformations without taking AI impact into consideration. The Capgemini Research Institute found that only <a href="https://www.capgemini.com/wp-content/uploads/2025/03/20th-March-Customer-service-transformation-CRI_Final.pdf" target="_blank" rel="noopener">49% of organizations</a> consider themselves ready for AI-powered customer service. They went on to find that cultural misalignment, poor inter-departmental coordination and fragmented IT services were some of the main obstacles.
With this in mind, we crafted our AI-led customer service solution to better deliver predictable business outcomes while fundamentally reshaping customer service at every level. As a foundation, we built a four-pillar system that looks to integrate Gen AI and agentic AI benefits while designing an experience with outcomes in mind.

Gen AI can understand customer intent on a more accurate level than ever before. Intent analysis is how an understanding of what customer service interactions are about is established. This is because traditional disposition codes and call routing structures don’t often represent reality. This pillar prioritizes and drives transformational action, and our intent framework integrates across channels with sentiment analysis to gradually build intent clusters, which become the basis for understanding how customer interactions flow end-to-end.

Pillar 1: Intent analysis

A long-standing goal of any customer service operation is deflection strategy–preventing a customer escalation to an agent by providing an immediate and available resolution. Our AI deflection strategy looks to prevent, remediate and provide self-service opportunities. This takes the form of predictive AI, which looks at historical patterns and trends to anticipate future issues. It also leverages AI to scan for issues, fixing them before customers notice and alerting customer service teams. Additionally, AI-powered digital assistants can provide information and resolutions long before an agent needs to intervene.
This also provides benefits to agents. Newfound human-AI chemistry can increase competency and knowledge. Digital assistants can be powerful allies for agents seeking to better deliver a resolution at greater speeds, thereby increasing productivity and efficiency.

Pillar 2: AI deflection strategy framework

At frog, we see frog-end transformation as an opportunity to re-route customer service from a maze of menus to a direct and authentic connection between people and brands. However, this requires design.
The brand needs to live its values with customer understanding and expertise while providing live agents where they will make the most impact for customers. This involves designing channel switches where agents can bring a unique touch to make customers feel special. Systems and enterprise architectures must be designed to ensure a consistent experience and cross-organizational visibility, as customers will move across products, services and engagement channels.

Pillar 3: Intentional experiences

AI-led transformation re-prioritizes and shapes new ways to think about legacy customer technology and process transformation. With the ability to deflect interactions to a contact center’s digital front-end, the types of calls reaching live agents are fewer but more complex. Average handling times may increase, and agents will need additional resources to resolve these issues. This puts pressure on making front end digital transformation a priority because it will minimize, contain and focus future operational needs.
For our research into how these AI systems would make an impact, we looked at a theoretical 1,000-agent contact center based in the US and what results they could expect after an AI transformation:
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<ul>
<li>$40m-$60m in savings over a 5-year period</li>
<li>10-20% improvement in NPS</li>
<li>20-30% savings from efficiency enabled by routing, self-service and prevention</li>
<li>40%+ self-service containment rate</li>
<li>1-30 years payback period in investment</li>
</ul>
These research outcomes give an intriguing glimpse into how Gen AI and agentic AI will introduce a completely new paradigm into the world of customer service and uplift these experiences for both customers and companies.

Pillar 4: Service transformation

<h3>Thoughtful strategy, focused execution</h3>
There is no denying that AI will reshape customer service, and at frog, we recognize and respect the challenges of implementing this change. Our vision sees program strategy becoming a common initial project, where stakeholders should be aligned with a business case established, while options around time to value, cost, efficiency and risk are balanced.
Once this strategy is established, AI-led transformation can begin. AI can be implemented first for avoidance and then for self-service, automation and agent augmentation. This can open doors to contact-center-as-a-service options. Any transformation should include change management, with continuous product strategy and stakeholder engagement. After implementing a solution, companies should troubleshoot and fine-tune to maximize efficiency and ensure continuous improvement. All this should be performed in parallel with providing adoption and training support for agents.
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<h3>A new paradigm awaits</h3>
The synergy between Gen AI and agentic AI, combined with human expertise, is part of a broader shift towards human-AI chemistry. It will bring customer service towards an even deeper connecting role between brands, customers and technology. While putting together our roadmap for end-to-end AI transformation, we realized AI would become that missing piece between understanding and resolving customer needs, all within a seamless and connected experience that today’s customers expect. This is a turning point, with AI playing an integral rather than supplementary role, unlocking deeper levels of interaction between agents and customers. But capturing the many benefits requires taking that first, bold step into the future.

Duncan Steels

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