The Race To Scale AI Without Losing The Human Touch
PLUS: Two prompts to test where AI wins speed and where humans win trust
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Date: 🗓️ August 25, 2025 ⏱️ Read Time: ~5 minutes
🤖 Welcome
AI is showing up everywhere in CX—from shopping carts to call centers to claims desks. But every rollout raises the same question: are we scaling trust along with efficiency? That tension is front and center in today’s stories.
📡 Signal in the Noise
Retailers, insurers, and service teams are leaning hard into AI for growth and efficiency. At the same time, leaders are being pushed to keep empathy, ethics, and transparency in the mix. The line between helpful automation and alienating automation is thinner than ever.
🧭 Executive Lens
It’s not about whether AI works—it clearly does. The challenge is how you deploy it without losing the human heartbeat of your customer experience. Scale is impressive, but trust is what sticks.
📌 Stories That Matter
🛒 Generative AI drives growth in e-commerce
AI is no longer a side project in retail—it’s driving $3.5 billion in growth and used by more than half of retailers. Shoppers are getting personalized recommendations and smarter journeys, and they’re responding with bigger baskets and higher conversion. If you’re not testing generative AI in your buying paths, your competitors already are.
Why this matters: AI is now a revenue lever, not an experiment.
Try this: Run a pilot in your product discovery flow and see if AI nudges up cart size.
Source: WebProNews
🛡️ AI strengthens insurance resilience and customer outcomes
Insurance isn’t the first place people think about AI, but Australia’s CSIRO is showing what’s possible. From predicting claims spikes to preparing for natural disasters, AI is helping insurers get ahead of risk instead of scrambling after it. Customers benefit too, with faster claims and more proactive support.
Why this matters: Being proactive with risk earns trust in a high-stakes industry.
Try this: Test an AI model that predicts one claims category and compare it against actual results.
Source: CSIRO
🤝 Human-first CX gets an AI guide
Craig Crisler, CEO of SupportNinja, is pushing back on the “AI everywhere” mindset. His view: AI should be the sidekick, not the star—supporting humans with transparency and clear escalation paths. Customers still want to feel heard, not handled by a machine.
Why this matters: Empathy is what keeps AI from turning into cold automation.
Try this: Build an “AI playbook” with your frontline teams that spells out when humans take over.
Source: Medium
⚡ AI Week shows how roles are being reshaped
At Cloudflare’s AI Week, the big theme was how work itself is changing. Agents are handling 10× more tickets, engineers are editing AI-written code, and salespeople are spending less time on admin and more on relationships. The jobs aren’t disappearing—they’re being rewritten.
Why this matters: AI is rewriting roles across the customer journey.
Try this: List your team’s most repetitive tasks and start shifting them to AI so people can focus on higher-value work.
Source: Cloudflare
🌍 Aligning generative AI with CX values
Dr. Gleb Tsipursky argues that AI adoption in CX must be more than a tech rollout—it has to safeguard empathy, transparency, and human connection. He frames generative AI as a tool to enhance service quality, but only when guided by clear guardrails around escalation, consent, and emotional intelligence. The real win is when AI scales efficiency without eroding the humanity customers expect.
Why this matters: CX leaders need AI strategies that drive scale and strengthen trust, not weaken it.
Try this: Host a cross-team workshop with agents, ops, and compliance to co-create rules for AI transparency, tone, and escalation.
Source: Medium
💡 Prompt of the Day
Title: Pressure-test AI against human trust
You are a CX strategist designing a controlled experiment to compare full AI service vs. AI-augmented human service.
Step 1: Select a high-volume customer inquiry (e.g., password reset, account update).
Step 2: Build two workflows:
• Flow A – AI handles the request end-to-end.
• Flow B – AI gathers context but a human delivers the final resolution.
Step 3: Route 500 interactions through each flow.
Step 4: Measure:
• Average handling time (AHT)
• CSAT
• Trust indicator (e.g., “I felt understood” survey question)
• Escalation rate
Step 5: Summarize the trade-offs and identify which model best balances speed, cost, and customer trust.
What this uncovers: Real data on whether AI should act as the frontline responder or as an assistant.
How to apply: Run this in one support queue, share findings with your ops + product leaders, and use the insights to guide AI deployment strategy.
🛠️ Try This Prompt
Title: Map empathy vs. efficiency moments in your journey
You are a CX leader tasked with separating customer journey moments where speed wins vs. where empathy wins.
Step 1: Pick two customer flows:
• One where speed matters most (e.g., order status check, password reset).
• One where empathy matters most (e.g., service outage, billing dispute).
Step 2: Design two AI pilots:
• Flow A – AI-first handling with human fallback.
• Flow B – Human-first handling with AI support for data gathering and resolution recommendations.
Step 3: Route at least 100 interactions through each variant.
Step 4: Measure:
• CSAT
• Resolution time
• Repeat contact rate
• Agent feedback (Was the AI assist helpful or not?)
Step 5: Create a heatmap of “AI-first moments” vs. “Human-first moments” and draft new routing rules accordingly.
Immediate use: Creates a decision framework for when AI should lead vs. when humans must.
Tactical benefit: Produces evidence for rewriting your escalation and routing playbook.
How to incorporate: Share the heatmap in your next leadership meeting to realign your AI investment roadmap.
📝 CX Note to Self
Scale without empathy is just automation.
👋 See You Tomorrow
Which of today’s stories could spark your next pilot?
—Mark
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