If your support team relies on scripted chatbots, the ones built on rigid decision trees, pre‑defined flows, and endless “if this, then that” logic, you already know the limitations.

They’re effective at handling predictable, repeatable inquiries. But as customer expectations rise and your business expands across brands, channels, and regions, maintaining those static flows becomes a full-time job.

Eventually, even the most well-structured scripts struggle to keep pace. They work until they don’t.

Blackhawk Network (BHN) knows this firsthand. Their CX automation journey began with a scripted chatbot—one that delivered early wins by reducing ticket volume and freeing agents to focus on more strategic work.

It was a strong start. But as BHN scaled, the team saw signs that their CX automation model needed to evolve.

Rather than layering on more scripts, they took a different approach; BHN transitioned to AI customer service agents that could reason, adapt, and improve over time to support a growing portfolio of brands and increasingly complex use cases.

If your customer service team is reaching the limits of scripted automation, BHN’s story offers a clear next step. In this post, we break down BHN’s migration and highlight how their AI agent went from scripted responder to strategic teammate.

From a strong start to a smarter future with AI customer service

BHN’s scripted chatbot fulfilled its original purpose: helping the team at Tango Card (before it became part of BHN) deflect routine tickets, operate efficiently with a lean team, and build early momentum for CX automation.

But success brings scale, and scale brings complexity.

As BHN’s ecosystem expanded, so did the demands on its CX automation. Each new product, use case, or region required additional scripted logic. The team found themselves spending more time maintaining content and less time improving outcomes.

Rather than stretch the model past its limits, BHN made a strategic decision to adopt a more dynamic, scalable approach. By transitioning to AI customer service agents built with generative capabilities, they created space for both the AI and the team to grow.

The shift unlocked smarter self-service, deeper collaboration between agents and AI, and a more sustainable model for scaling customer support across the business.

What is an AI customer service agent, and how does it differ from a chatbot?

Unlike traditional chatbots, AI customer service agents aren’t limited to rigid scripts or pre-defined flows . They use large language models (LLMs) and contextual reasoning to interpret customer intent, determine the right resolution, and take action across channels, languages, and use cases.

Where chatbots follow a path, AI agents adapt. They don’t just respond, they resolve.

Laying the groundwork for scalable CX automation

Scaling with AI doesn’t start with the AI itself. It starts with building the right foundation.

Before BHN expanded automation across brands and channels, they made intentional investments to ensure their AI customer service could perform at a high level. That meant rethinking systems, teams, and the entire operating model.

What should teams consider before scaling CX automation?

• Rethinking the tech stack: BHN moved from static flows to a system that could integrate with APIs, pull from centralized knowledge sources, and deliver generative responses. This unlocked the flexibility needed to expand across brands, use cases, and channels.
• Team and role redesign: Instead of treating AI as a set-it-and-forget-it tool, BHN built a support structure around it. Content specialists, QA leads, and AI-certified agents played key roles in coaching and optimizing performance.
• Knowledge base optimization: Clean, organized, and comprehensive content was critical. BHN restructured their knowledge base to make it easily retrievable and optimized for generative AI, enabling better responses with less maintenance.
• Channel expansion readiness: With the right structure in place, BHN extended CX automation beyond chat into voice, in-product, and (soon) email. This wouldn’t have been possible without reusable actions, strong integrations, and a platform built for omnichannel customer service.

Together, these foundational moves enabled BHN to deploy an AI customer service agent that could perform like a real teammate, not just a better bot.

Expanding reach without increasing effort for truly omnichannel customer service

With that foundation in place, BHN was able to scale confidently.

Their AI customer service agent now supports multiple brands and business lines across web, chat, in-product experiences, and voice—with email support on the roadmap. Instead of managing separate flows for each channel, the team uses shared actions and structured content, all orchestrated through Ada’s platform.

The AI customer service agent is deeply embedded in their operations. It pulls from multiple knowledge sources and connects to integrated systems to:

• Greet known users and streamline authentication
• Retrieve real-time order status
• Resend digital rewards without escalation
• Route complex issues intelligently based on user context

And just like any top-performing employee, it’s supported by a system of continuous coaching.

BHN’s human agents now act as mentors to the AI—reviewing transcripts, identifying optimization opportunities, and helping the AI learn and improve. Weekly roadmap reviews bring together Product, Engineering, and Support leaders to align CX automation performance with business outcomes.

After the migration, BHN didn’t just get a “better chatbot.” They got a system that could manage real complexity and improve over time, with:

• Higher automation rates: Today, 43% of all interactions—across web, chat, voice, and in-product—are resolved end-to-end by the AI agent.
• Improved experience: With a more dynamic and context-aware agent, BHN now delivers personalized, digital-first experiences at scale, reducing friction and helping customers get answers faster.
• Operational efficiency: The AI agent now handles routine queries and actions, like resending digital rewards, freeing up human agents to focus on strategic work like optimization and cross-functional collaboration.
• Scalable omnichannel customer service: BHN’s AI agent operates consistently across web, chat, in-product portals, and voice thanks to shared logic and reusable actions that reduce overhead.

The result? A support experience that’s faster, more flexible, and always on, and one that keeps pace with customer expectations and business growth.

What other enterprises can learn from BHN: Best practices for integrating AI agents into your customer support workflow

BHN’s story offers a practical, proven path for any team looking to evolve their CX automation. A few key lessons stand out:

• Treat the AI like a teammate: Build structure, assign roles, and create space for collaboration and improvement.
• Invest in knowledge and systems early: Clean, well-organized content and strong integrations make the difference between AI that responds and AI that resolves.
• Measure what matters: Move beyond traditional metrics like containment. Track automated resolution, CSAT, and cost to serve.
• Scale smartly: Don’t duplicate effort across channels. Use reusable actions and centralized orchestration to expand reach without increasing workload.

If you’re considering a move from scripted automation to generative AI, here’s a simplified playbook inspired by BHN’s success:

• Audit your current system: Identify where scripted flows break, whether that’s maintenance effort, customer drop-off, or unsupported use cases.
• Strengthen your foundation: Ensure your knowledge base is clean, your APIs are in place, and your backend can support real-time interaction.
• Design around your team: Assign clear ownership for AI content, quality control, and improvement—governance is key to performance.
• Test across channels: Validate the AI agent in chat, voice, email, wherever your customers engage.
• Track what matters: Go beyond launch metrics. Monitor resolution, CSAT, cost to serve, and agent impact to understand true value.
• Optimize continuously: Treat AI performance like any team member. Train it, review it, and improve it.

Done right, the move to generative AI becomes more than a technology upgrade. It’s a shift in how your customer service team operates and delivers value.

What BHN’s story makes clear: Generative AI is the future of CX automation

By evolving their approach to match the needs of a growing business, BHN unlocked a new model for customer experience—one where AI customer service agents aren’t just reactive, but proactive partners in delivering value.

It’s about recognizing when to level up, and having the right strategy, structure, and partner to do it confidently.

BHN didn’t abandon their early investment in CX automation, they built on it.

With Ada’s generative platform and a mature AI operating model, BHN now runs a leaner, smarter, and more scalable support function, powered by AI customer service agents that act like true members of the team.

So here’s the question: What could your support team accomplish if your AI agent wasn’t just answering questions, but owning resolution? It might be time to find out.