Building a seamless support team with people + AI: 5 key learnings
In an era where customer expectations are only rising, navigating the intricacies of modern support operations requires more than just reactive solutions — it demands a strategic, data-driven approach that integrates people, processes, and technology.
Recently, Assembled hosted a conversation with two leaders at the forefront of support transformation: Josh Stern, Principal Solution Architect at Engine (formerly Hotel Engine), and Kevin Lada, Director of Solutions Engineering, Service Cloud. Together, they shared insights on the future of support operations, highlighting the critical role of AI, workforce management (WFM), and omnichannel strategy in delivering customer-first experiences at scale.
This recap explores five key learnings from our discussion, each providing a practical takeaway for reimagining support operations. From embracing AI and WFM to overcoming omnichannel challenges, these insights offer support leaders actionable strategies for thriving in today’s complex CX landscape.
1. Use AI to empower (not replace) your team
AI is no longer just a buzzword; it's a core component of efficient support operations. But, as both Josh and Kevin made clear, it’s here to work with us, not replace us. For Josh, tools like Salesforce’s Agentforce allow Engine to “meet customers where they are,” handling repetitive tasks and freeing agents for more nuanced, meaningful interactions. Kevin echoed this, stressing that AI’s value is in amplifying human efforts to deliver faster, higher-quality support. By deploying AI where it fits best, support teams can boost satisfaction and retention without losing the human touch. Leading teams can analyze their most repetitive actions or customer queries and support them with AI workflows, instead of dedicating their team’s time towards minutia.
2. Ensure AI and human team visibility for a seamless journey
In an omnichannel world, visibility across both AI and human touchpoints is essential to maintain a seamless customer journey. Kevin explained that by mapping out each step in the support experience, from initial contact through to resolution, support leaders can pinpoint where to refine processes and reduce friction. For Josh, tools like Assembled enable real-time data tracking and transparency, allowing Engine to adjust staffing and support channels based on live demand. This adaptability has moved Engine’s model from a reactive to a proactive one, making responsiveness the new standard.
“The Case Lifecycle approach that Assembled takes is one of the key differentiators that really gives you that holistic view and visibility into the entire lifecycle of the case. Regardless of who's touching it, front office, back office, and dependent on the channel that you're interfacing with. That level of granularity is a gold mine for support organizations to really be impactful.
One caution is that not every interaction benefits from AI. Some things are best handled by humans, and workforce management helps ensure that the right mix of AI and human support is applied where it adds the most value.”
Kevin Lada, Senior Service Cloud Architect at Salesforce
3. Design staffing for flexibility and real-time agility
As customer interactions become more complex, so do staffing needs. Josh described how moving away from Excel to Assembled allowed Engine to adapt to real-time fluctuations in call volume, ensuring the right agents are available across channels when they’re needed most. Kevin added that companies should think strategically about channel management, while allowing customers to be met where they want to. By proactively designing support models around customer preferences, companies can deliver more relevant and satisfying experiences — while using resources wisely.
4. Use metrics that show AI’s real impact
The right metrics make all the difference in AI’s success. While CSAT, handle time, and other traditional metrics remain fundamental, Kevin noted that new metrics around AI accuracy, escalation rates, and seamless bot-to-agent handoffs add another layer of understanding. At Engine, Josh’s team tracks customer satisfaction and AI’s impact on reducing frustration, ensuring a smooth handoff between AI and human agents. This holistic approach ensures that AI isn’t just working — it's working effectively to drive better outcomes.
“We focus heavily on AI accuracy—tracking escalation rates and customer satisfaction with AI interactions. Since we’re customer-centric, our top priority is that AI supports a great customer experience. We also monitor the transition from AI to human agents to ensure the handoff is seamless, capturing customer intent and passing it along without requiring the customer to repeat themselves.”
Josh Stern, Principal Solution Architect at Engine
5. Automate wisely to free agents for high-value work
Automation isn’t one-size-fits-all. Josh and Kevin pointed out that high-volume, repetitive tasks are prime for AI, while complex interactions require a human touch. For Josh, tasks like note-taking and simple follow-ups are the perfect fit for AI, allowing agents to dedicate more time to complex cases. Kevin added that in high-volume industries, AI-driven automation can deliver significant time savings, with human agents stepping in where empathy and creativity are needed most. By thoughtfully automating, support teams can streamline operations without sacrificing quality.
Get the full story
Building a seamless support team that combines people and AI takes strategy and precision. Josh and Kevin's insights offer clear steps for making this happen. Ready to dive deeper? Watch the full webinar to get actionable tips straight from the experts and see how Engine and Salesforce are setting new standards in support.
