How insurers are transforming roadside assistance with AI in contact centers

In the insurance sector, roadside assistance is one of the most critical touchpoints in terms of customer experience and operating costs. For operations and customer experience leaders, managing internal contact centers has become increasingly complex: unpredictable call peaks, overloaded agents, difficult-to-maintain SLAs, and mounting pressure to control costs without compromising quality.

The adoption of generative artificial intelligence and CCaaS (Contact Center as a Service) solutions enables a strategic rethink of this operating model. The goal is not just automation, but an intelligent redesign of the synergy between AI and human agents, boosting productivity, ensuring service continuity, and delivering truly omnichannel experiences.

New paradigms in roadside assistance: balancing operational efficiency and customer experience

Roadside assistance has evolved from a simple operational service into a strategic lever for differentiation and loyalty. This is where much of a brand’s perception is shaped: in moments of emergency, customers evaluate speed, effectiveness, and empathy.

Providing timely, high-quality service often means increasing pressure on operating costs and contact center resources. In this context, generative AI technologies represent a paradigm shift. According to a McKinsey report, companies integrating intelligent automation into contact centers can reduce operating costs by 20–40% while simultaneously improving customer satisfaction and experience.

This evolution is particularly relevant in roadside assistance, where every second of delay can harm both customer experience and brand trust.

Why roadside assistance is a critical touchpoint

No other moment leaves customers more vulnerable or less tolerant of service friction than a roadside breakdown. Roadside assistance is therefore a high-impact, high-risk touchpoint: during urgent and stressful conditions, an ineffective response can destroy years of relationship value in minutes.

For organizations, this is not just a customer experience issue, it’s a matter of business continuity, cost per customer, and active retention. Response speed directly impacts key metrics such as Net Promoter Score (NPS), retention rate, and complaint ratio, creating a ripple effect on churn, caring costs, and negative word of mouth.

Roadside assistance is not merely a service to be optimized; it is a reputational and financial asset that must be managed with tools capable of scaling quality without multiplying costs.

The tough balance: quality service vs. rising operating costs

Providing 24/7 rapid service is expensive, and cost-to-serve often suffers more than customer experience. Insurers must balance strict SLAs, flexible staffing, and rising customer expectations in an unpredictable volume environment with shrinking margins.

CCaaS solutions enhanced with generative AI, like Smile.CX, provide a more scalable, adaptive model that optimizes cost-per-contact versus perceived quality, without compromising service during critical moments. This marks a shift from reactive management to proactive control of costs and performance.

Contact centers under pressure: today’s operational challenges

Roadside assistance requires a responsive, scalable, and sustainable operating model. Yet many insurers still rely on outdated processes: sequential, fragmented, and heavily manual. These systems struggle with dynamic workflows and sudden demand spikes, resulting in unstable performance, unpredictable customer experience, and operating costs rising faster than controllability.

Agent overload during call peaks: the bottleneck risk

During high-impact events (bad weather, holidays, multiple breakdowns), call volumes can surge 100–200% within minutes. Traditional contact centers quickly hit capacity: each agent typically handles only one conversation at a time, with hourly productivity rigidly limited by human capability.

This model is not only inefficient, it’s inherently fragile. It risks SLA breaches, unresolved escalations, and rising unit costs per call. Organizationally, it triggers stress, burnout, and turnover risks, undermining service continuity at the worst possible moments.

For management, this means losing control of key KPIs and struggling with workforce planning. Every unplanned peak becomes a crisis instead of an absorbable event, and the lack of operational flexibility turns into systemic inefficiency.

Critical KPIs:

• ASA (Average Speed of Answer)
• SLAs Met (%)
• Calls handled per agent per hour

Long phone wait times: negative impact on NPS and retention

A livello strategico, questo si traduce in una riduzione dell’NPS, in una diminuzione della loyalty e in una crescita dei costi di caring (richieste di follow-up, reclami, customer churn). Per i manager, ignorare questo parametro significa sottovalutare un costo implicito che impatta direttamente il CLV e l’efficienza complessiva della macchina operativa.

Average wait time is one of the strongest signals of operational inefficiency and demand-response imbalance, especially in emergencies. Beyond 60 seconds, the probability of a negative customer experience rises exponentially.

Strategically, this translates to lower NPS, reduced loyalty, and higher caring costs (follow-ups, complaints, churn). Ignoring this metric means underestimating an implicit cost that directly impacts Customer Lifetime Value (CLV) and overall operational efficiency.

Critical KPIs:

• ASA (Average Speed of Answer)
• CSAT / NPS
• Customer churn rate

Manual insurance data verification: slow and costly processes

Many contact centers still manually handle low-value tasks like license plate checks, policy verification, coverage limits, and eligibility. This approach inflates Average Handling Time (AHT), increases error risk, and makes every interaction more costly.

For companies, the issue is economic: every extra minute of handling is an added cost and when multiplied across thousands of calls, it becomes structural inefficiency. Assigning repetitive work to agents also reduces productivity and limits their ability to focus on strategic or relationship-driven tasks.

In a context where every AHT percentage point impacts operating costs and scalability, failing to automate means sacrificing competitiveness.

Critical KPIs:

• AHT (Average Handling Time)
• Cost per Call
• % Process error rate / Rework 

Generative AI response: a new model for intelligent call management

Adopting generative AI in insurance contact centers does more than automate responses, it redesigns how demand and capacity meet. This is a shift from linear handling to intelligent workload distribution, where AI and agents work in synergy to maximize efficiency, continuity, and perceived quality.

Delegate repetitive tasks to AI, free agents for high-value work

The power of generative AI lies not in replacing humans but in lightening their load. Intelligent automation can handle initial call phases (data collection, policy verification, vehicle location) reducing agent cognitive strain and minimizing idle time.

Result: Agents focus on critical tasks, improve relationship quality, and boost productivity. Every minute saved on repetitive work becomes a minute reinvested in efficiency and CX, impacting KPIs like AHT and FCR (First Call Resolution).

Parallelized workflows: one agent can handle multiple customers at once

With the support of virtual assistants and conversational orchestration systems, agents are no longer limited to handling just one customer at a time. Next-generation CCaaS platforms enable parallel service models, where a single agent can coordinate multiple conversations simultaneously. This operating mode is implemented in a structured, intelligent way, through interfaces designed for multitasking and smart notifications.

The result is greater efficiency without sacrificing quality: routine tasks are handled by the system, while the agent can focus on the customer relationship and the issues that truly require human attention. Everything flows seamlessly, scalable, efficient, and easy to monitor.

See how an agent manages multiple customers with generative AI.

Reduced Average Handling Time (AHT) through intelligent automation

With generative AI, average handling time decreases not just because of speed, but because of cognitive efficiency: agents receive real-time suggestions, dynamic summaries, and proactive automations that remove unnecessary steps. The benefit is not only qualitative but structural: less time per call means more calls handled in the same timeframe, with the same resources.

Reducing AHT frees operational capacity during peak periods, allowing staff to be used more effectively. As a result, each FTE works more efficiently, the organization manages pressure better, and costs remain sustainably under control.

Case Study: how an insurance company transformed roadside assistance with Smile.CX

Implementing Generative Artificial Intelligence within a contact center is no longer an abstract innovation exercise. It’s a practical lever to redesign processes, streamline interactions, and dramatically improve operational efficiency.

This is exactly what a leading insurance group did. Managing a large amount of roadside assistance cases every year, the company adopted Smile.CX to optimize call handling in its contact center. The result was a tangible, measurable, and scalable operational transformation. The change brought by Smile.CX was not just technological. It was structural.

• Handling time reduction: before adopting the platform, each roadside assistance request required over 9 minutes of processing fully managed by a human agent. This valuable time was often consumed by low-value tasks such as collecting customer information, verifying insurance coverage, or locating the vehicle. With Smile.CX, these tasks have been automated: artificial intelligence now handles the repetitive, low-value parts of the interaction, gathering and validating the necessary information in real time. The result? A 52% reduction in case handling time: today each assistance request is resolved in just 4.5 minutes, freeing agents from repetitive operations and allowing them to focus their intervention more effectively.
• Productivity increase: this streamlining has had a direct impact on overall team productivity: hourly capacity per agent rose by 23%, from 3.1 to 3.8 interactions per hour. At the same time, the contact center reduced FTE resources by 36% to handle the same volumes, without compromising service quality, indeed, improving it. Thanks to greater efficiency, the company has significantly increased its SLA coverage: from 68% to 78%, with a clear improvement in responsiveness even during peak hours or in critical situations such as roadside emergencies.
• CX improvement: the average customer satisfaction rating rose from 6.6 to 7.2 (+9%), signaling a tangible improvement in perceived service quality. Customers receive faster responses, interact with agents who are already informed about their case, and perceive the service as more efficient, empathetic, and well-coordinated.
• Increased volume capacity: the ability to handle parallel conversations, supported by AI, made the operating model even more scalable. Each agent can now manage an average of 1.2 simultaneous interactions, compared to just one sequential conversation under the previous model. This enabled the contact center to absorb a 20% increase in contact volumes without additional hiring and without compromising quality.

This transformation demonstrates that adopting Generative Artificial Intelligence and CCaaS platforms like Smile.CX is not merely a technological investment, but a strategic lever that enables insurance companies to effectively meet the challenges of modern roadside assistance. Optimizing processes, reducing costs, and improving the customer experience are no longer conflicting goals, but part of an integrated, scalable operating model capable of dynamically adapting to both customer and business needs.

Contact us today for a personalized demo of Smile.CX and discover how to transform roadside assistance in your contact center, reduce costs, and enhance the customer experience.