Customer service leaders do not need another abstract vision for AI.

Customer service leaders do not need another abstract vision for AI. They need a practical path from pilot to production—one that proves value quickly, reduces delivery risk and creates the operating foundation for scale. That is where Publicis Sapient helps organizations move beyond experimentation.

A strong example comes from Phillips 66, where Publicis Sapient and the client built three Agentforce proofs of concept in just three weeks. Those proofs of concept focused on high-value service moments: invoice inquiry, case management and case escalation. Customers could review invoice details, retrieve information about line items, open cases, check case status and receive updates through a blend of AI-assisted and human support. Cases could be routed automatically to the right queue, while sentiment analysis helped tailor interactions for a more personalized experience. The result was a clear demonstration of speed, execution and what an AI-enabled service model could become.

But a three-week pilot is only the beginning. The bigger question for most enterprises is what it takes to turn a promising proof of concept into a production-ready customer service transformation. In our experience, that journey requires more than fast prototyping. It requires the right use cases, connected systems, thoughtfully designed human handoffs and a product mindset that keeps improving the service experience after launch.

Start with high-volume, high-friction service use cases

The fastest route to value is not to automate everything at once. It is to identify the interactions that create the most volume, the most repeat effort or the most avoidable friction for customers and service teams. Phillips 66 showed this clearly by focusing on recurring service scenarios around invoices and case handling. These are the kinds of workflows where AI can quickly demonstrate business relevance because the pain points are visible, measurable and meaningful.

That same principle appears across Publicis Sapient’s Salesforce work. For Pandora, routine service questions such as order status and jewelry care became a strong starting point for an AI-powered service agent. For a premium protective products brand, the challenge was not only customer demand but also the friction caused by representatives switching across too many systems to resolve a single case. In each example, the transformation began by targeting the moments where speed, clarity and consistency mattered most.

Prototype fast to validate value and build momentum

Speed matters early because it reduces uncertainty. A rapid proof of concept gives leaders and frontline teams something tangible to react to, helping them validate use cases, refine workflows and build confidence before larger investments are made.

This is a repeatable strength for Publicis Sapient. At Phillips 66, three Agentforce proofs of concept were built in three weeks. In another Salesforce engagement for a global technology brand’s new business unit, Publicis Sapient stood up a new Sales Cloud instance within three weeks to centralize opportunity management and support adoption. In energy trading, Publicis Sapient used rapid prototyping and a six-week proof of concept to shape solution design, prove integration feasibility and secure buy-in from key teams before moving into broader delivery.

The lesson is clear: rapid prototyping is not about creating a flashy demo. It is about accelerating learning. It helps organizations understand what customers actually need, what employees will adopt and what technical realities must be solved before scale.

Build the digital foundation by integrating core systems

Pilots can succeed with limited scope. Production cannot. Once an organization decides to scale, the service model has to connect to the systems that hold the context customers and agents need: case data, order information, invoice details, knowledge, commerce data and other enterprise records.

This is often where the real transformation begins. For the premium protective products brand, Publicis Sapient replaced a legacy platform with Salesforce Service Cloud and delivered more than 15 MuleSoft-to-Service Cloud integrations. Representatives could stay in one system rather than logging into multiple tools to resolve a case, and regional processes were consolidated into a single global instance with one source of truth. That created not just a better agent experience, but a stronger platform for future change.

Pandora’s Agentforce journey reflects the same pattern. Its customer service agent connects with Service Cloud, Commerce Cloud and order management data to provide real-time updates, while a personal shopper agent draws on connected commerce, experience and customer data to create more tailored interactions. In agriculture, Publicis Sapient integrated Salesforce with AWS, EBS and third-party systems to deliver real-time information and modernize both service and sales operations.

For organizations moving from pilot to production, this integration work is what turns isolated intelligence into dependable service capability.

Design human-in-the-loop escalation from the start

Production-ready service transformation is not about replacing people. It is about designing the right relationship between AI and human expertise.

Phillips 66 offers an important model here. In its case management escalation proof of concept, customers could receive updates from both an agent and a live customer service representative depending on the nature of the question. Representatives were supported by agents to maintain healthier dialogue with customers, rather than being left to restart the interaction from scratch.

This principle also underpins broader Publicis Sapient thinking on AI-led service. The most effective service experiences let AI handle routine inquiries, gather context, summarize intent and route cases intelligently—then pass customers to human experts when empathy, judgment or exception handling is required. Pandora’s work reinforces this balance: routine queries can be deflected autonomously, freeing specialists to focus on higher-value interactions while maintaining a more human, brand-aligned experience.

Establish governance and a product mindset for rollout

The difference between a promising pilot and a scalable service model is operating discipline. Once AI enters real customer journeys, organizations need governance, release management, adoption planning and clear ownership for continuous improvement.

Publicis Sapient brings a product-led approach to that challenge. Rather than treating service transformation as a one-time implementation, we help clients build for ongoing iteration. That means aligning strategy, experience, engineering and data around measurable outcomes. It means training users, tracking adoption and creating the flexibility to improve workflows over time.

This approach has delivered lasting value across Salesforce environments. A global technology brand achieved 100% adoption of its new Sales Cloud solution. Pandora improved release velocity by moving to a common codebase and more agile development model, cutting migration time in half and reducing release cycles from months to weeks. A multinational insurance company gained a scalable cloud-based platform that could support expansion across markets while improving agility and performance. These are different use cases, but they share the same underlying truth: scale requires a strong digital foundation and a model for continuous change.

From proof of concept to service transformation

Agentforce can deliver fast wins. Phillips 66 proved that in three weeks. But real enterprise value comes from what happens next: selecting the right service use cases, validating them quickly, integrating the systems behind them, designing human-in-the-loop operations and governing the rollout with a product mindset.

That is the journey Publicis Sapient supports. We help organizations move from isolated pilots to connected, scalable customer service models built for real-world complexity. And because we combine strategy, product thinking, experience design, engineering and data and AI expertise, we help clients do more than launch new capabilities. We help them create the conditions for customer service to become faster, smarter and more resilient over time.