Is Human Imitation the Right Goal for AI in Customer Experience?

# CX

# CSS

# CS

Escaping the ‘Turing Trap’ by focusing on capability expansion.

Stanford’s Erik Brynjolfsson warns of the “𝘛𝘶𝘳𝘪𝘯𝘨 𝘛𝘳𝘢𝘱”, our tendency to reward AI for mimicking humans rather than augmenting them. He argues that AI’s true potential lies in 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗲𝘅𝗽𝗮𝗻𝘀𝗶𝗼𝗻—helping people achieve what was previously beyond reach. That idea hits home for those of us in Customer Service, Support, and Success (𝗖𝗦𝗦), where empathy, context, expertise, creativity, and trusted relationships define value. Here’s what that looks like in practice: 1️⃣ Customer Service ▪ Labor Substitution: Chatbots deployed mainly to replace agents and reduce costs. Fast and scalable, but often limited when context or empathy is required. ▪ Value Augmentation: AI chatbots that collaborate with humans—resolving routine requests instantly, gathering context, summarizing prior interactions, and handing off to humans seamlessly when deeper expertise or emotional nuance is needed. 2️⃣ Support Operations ▪ Substitution: Traditional automation that auto-routes cases based on static rules or keywords — efficient, but often blind to intent, sentiment, or business impact. ▪ Augmentation: AI that blends predictive intelligence, sentiment analysis, and real-time telemetry to understand context, prioritize impact, and recommend next actions. It learns from every interaction, helping teams prevent escalations before they happen. 3️⃣ Customer Success Management ▪ Substitution: Automated health scores and templated check-ins — lagging signals that identify risk only after it occurs. ▪ Augmentation: AI-driven success intelligence that unifies product, sentiment, and journey data to predict outcomes and surface next-best actions, enabling CSMs to engage proactively and lead strategic, value-focused conversations. 4️⃣ Customer Engagement ▪ Substitution: One-click AI emails and auto-generated outreach. Fast but formulaic and impersonal. ▪ Augmentation: Generative AI that personalizes engagement at scale, helping CSMs turn data and intent into authentic, insight-led stories that strengthen relationships and renewals. 𝗠𝗮𝗸𝗶𝗻𝗴 𝗔𝗜 𝗪𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗣𝗲𝗼𝗽𝗹𝗲 𝗶𝗻 𝗖𝗦𝗦 🔹 Metrics: Measure joint human+AI outcomes; TTV, NRR, CSAT, and value realization, not just cost or deflection. 🔹 Diffusion: Equip every support engineer and CSM with augmentation tools and reinvest efficiency gains into proactive engagement and learning. 🔹 Guardrails: Build trust through oversight, data integrity, and transparent AI interactions that foster confidence and control. AI can substitute labor or augment capability. Only one path expands opportunity, trust, and long-term value. “We should build systems — combinations of humans and machines, that do things no human and no machine could do alone.” — Erik Brynjolfsson How is AI being used in your organization — to replace or to amplify?

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Is Human Imitation the Right Goal for AI in Customer Experience?

Escaping the ‘Turing Trap’ by focusing on capability expansion.

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