From blueprint to bot: Designing resilient AI-powered services
Summary
Our goal was to introduce an AI-powered voice agent to help customers of a B2B building materials supplier manage their orders. But making it work meant looking beyond the tech and working through layers of complexity like siloed systems, long-established processes, and both staff and customers used to traditional, human-led interactions. This case study shares how service design helped us make that shift by: 1. Helping front-line staff see AI as a collaborator 2. Preparing customers for a new kind of interaction at different stages of their customer journey by involving sales and customer service agents 3. A data driven approach to aligning internal processes, systems and workflows
Key Insights
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82% of companies worldwide are using or exploring AI due to competitive and investor pressure.
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AI failures are unpredictable because AI models hallucinate, so designing for resilience is essential.
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C-suite leaders often expect flawless, scalable AI impact but must see AI rollout as a learning pilot.
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Service design can untangle organizational complexity by aligning strategy, orchestration, and implementation layers.
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Orchestration teams translate high-level AI strategy into actionable workflows to anticipate downstream effects.
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Product teams must design fallback mechanisms for predictable failures and iterate based on real-world AI errors.
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Involving frontline staff as co-creators reduces AI anxiety and improves AI adoption and fallback design.
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Continuous feedback loops across organizational layers enable timely learning and more resilient AI services.
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Framing AI deployment as an experiment or pilot aligns stakeholders to expect failures and focus on learning.
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Performance metrics should go beyond containment rates to reflect customer experience and resilience over time.
Notable Quotes
"With AI, we don’t want to save a few minutes here and there. We want something that has a large impact and can scale really fast."
"AI models hallucinate when they fail, they fail unpredictably."
"Every feature release with AI is really an experiment, not a finished feature."
"If you only design for the ideal experience, you might panic and hire back human help when things don’t go as planned."
"Service designers can help the organization untangle complexity across layers so they collaborate effectively."
"Frontline staff should feel ownership, not fear, around AI implementation."
"Involving frontline staff as co-creators helps ensure AI is a collaborator rather than a threat."
"We must build our service vision not just for the ideal, but for how to handle things when they go wrong."
"It’s important to establish feedback loops so each organizational layer learns from the others continuously."
"Framing AI introduction as a pilot helps stakeholders understand it’s a learning experience to build resilience."
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