Harnessing GenAI in customer service
Published November 13, 2025
- Data & AI
Key takeaways
- Customer service leads AI adoption: It is among the most proven and scalable enterprise applications of GenAI
- From cost center to strategic edge: GenAI can transform customer service into a driver of brand differentiation and growth
- AI-native platforms are disrupting the market: New vendors and custom builds are redefining efficiency, security, and pricing models
In our 2025 Global AI Survey, ‘Delivering improved customer experience’ was cited as the second most common area where leaders are seeking to benefit from AI, beaten only by ‘streamlining operations’.
Today’s business leaders increasingly recognize that customer service is not just a ‘tactical function’ but a strategic lever that can drive customer retention, brand equity, and long-term profitability.
In this article we look at what the opportunity is for GenAI, in particular, to redefine customer service, provide our views on the top 5 questions leaders should ask the business before investing, and lastly provide our 3 Keys To Success.
The opportunity for GenAI in customer service
The decisions senior leaders make in the near future will set the trajectory for customer experience, cost-to-serve, and brand differentiation for years to come – so it’s critical to understand the significance of the opportunity.
Fundamentally, GenAI redefines customer service by transforming:
- Operational efficiency: AI can manage a large share of routine interactions such as status updates, policy explanations, and product guidance, while assisting humans with more complex cases. The results? Significantly faster response times, shorter handling durations, smarter workforce planning, and a lower cost-to-serve without compromising quality.
- Strategic differentiation: Customer service is often the most frequent touchpoint with a brand. GenAI can personalize responses using context and history, trigger proactive follow-ups, and maintain a consistent voice across channels, languages, and time zones. To put this in a strategic context: better experiences lead to higher satisfaction, loyalty, and customer lifetime value.
Five questions leaders should ask their teams
The answer to “when” is now! Rapidly scaling companies are already using AI in customer service to manage increased demand without proportional headcount increases.
However, organizations with legacy systems can still move quickly by selecting targeted use cases with clear ROI. Our advice would be to start with use cases where the fit is clear and the impact is easy to prove, for example improving first-contact resolution, cutting handle time, or lifting customer satisfaction.
If customer service is central to your brand identity or competitive edge, a custom or hybrid build may be worth the investment. Otherwise, our advice would be to start with a proven platform and customize only where it drives clear business value.
To buy or to build? Key considerations
‘Off-the-shelf’ platforms are ideal for fast deployment. They help reduce operational complexity by managing latency, tone, and risk controls out of the box.
New AI-native players such as Sierra AI and Decagon are enhancing automation by integrating with established platforms like Genesys, NICE, Zendesk, and Salesforce.
However, buying comes with trade-offs. Vendor-driven updates, model changes, and security constraints may not suit every organisation’s needs or risk appetite.
Building your own solution offers greater control, deeper integration, and the ability to tailor the experience to your brand – but it requires technical expertise, disciplined operations, and ongoing maintenance.
Global regulations shift fast, but the direction is clear: customers should know when AI is involved and be protected from harm.
- In the EU, the AI Act mandates disclosure when people interact with AI unless it is obvious from context.
- In the UK, Consumer Duty requires firms to avoid foreseeable harm, especially for vulnerable customers.
- In the US, the FTC has warned of enforcement where AI use is opaque or misleading, and several states have introduced similar requirements.
It’s vital to be clear with customers when AI is involved; make it easy to reach a person, keep audit trails, and explain important decisions. At the end of the day, transparency builds trust.
To build the right security and guardrails around AI, it’s important to recognize the new risks it introduces – such as data leaks, system misuse, or inaccurate and even inappropriate outputs.
Our advice is to start by putting layered protections in place:
- use content filters to catch harmful or sensitive outputs
- apply policy checks to ensure AI responses align with your standards
- set rate limits to prevent abuse
- restrict data access to only what’s necessary.
Beyond technical safeguards, we’d underline the importance of running ‘red-team’ exercises to simulate worst-case scenarios and to uncover vulnerabilities.
Performance needs to be tracked using accuracy and safety metrics, with AI being rolled out gradually – with human oversight at every stage.
Finally – keep testing continuously. AI models evolve over time, so your guardrails need to evolve with them.
This is an area leaders should insist on reviewing. Why? Because traditional per-seat pricing doesn’t reflect how AI is actually used in customer service.
Instead, push for models that are more aligned with real value, such as:
- consumption-based pricing: E.g. cost per conversation resolved
- outcome-based contracts: Tied to measurable results, with performance safeguards built in
As AI models become more efficient and cheaper to run, it’s important to revisit contracts regularly to make sure those savings are being shared – not just absorbed by vendors.
“GenAI enables organizations to reimagine customer service as a growth engine, not just a cost center.“
Three keys to making your AI customer service initiative a success
Lastly, if you want to be sure of your initiative landing successfully, our keys to success are:
- Keep your knowledge base sharp and structured: AI performs best when it has access to clear, up-to-date information. Make sure product details, policies, troubleshooting steps, and brand tone are all consolidated into one reliable, well-maintained source.
- Design smooth handovers to human agents: Customers are usually okay with a bit of friction to reach a person – but they won’t tolerate dead ends. Set clear rules for when to escalate, pass full context to your agents, and monitor how well those transitions work. It’s all about balancing speed with empathy.
- Start by supporting humans, not replacing them: Begin with AI that helps your agents work smarter, then gradually expand automation as your confidence and capabilities grow. Keep revisiting the human-AI balance to make sure it still works for your customers and your team.
GenAI customer service as a growth engine
GenAI enables organizations to reimagine customer service as a growth engine, not just a cost center. The winners will combine urgency with discipline: starting small where value is clear, building trust through transparency, ensuring safety by design, and aligning vendor incentives with performance.
Senior leaders should also remember to ask three questions of their teams: Where are we piloting now? How are we managing risk? How will we prove business impact?
By acting decisively now, businesses can cut costs, build stronger relationships, unlock new value, and future-proof their brand in an AI-driven world.
Authors
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Callum Lyons
Manager – UK, London
Wavestone
LinkedIn
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