Are We the Last Human-Only Workforce?
DCX Links | February 15, 2026
Welcome to the DCX weekly roundup of customer experience insights!
We are likely the last generation of leaders managing a human-only workforce.
Not because humans are disappearing, but because AI is becoming part of the operating fabric of CX.
This shift isn’t about chatbots getting better. It’s about rethinking how work gets done. How conversations flow across time and channels. How service quality is protected while automation accelerates. How trust is built when intelligence is no longer purely human.
At the same time, the pressure to move faster is real. AI capability is compounding. Customers expect continuity, not channels. And 64% still say service quality is the single biggest factor separating one company from another.
The responsibility for CX leaders is becoming clearer:
Ship sooner.
Prioritize smarter.
Design AI as a brand-trained participant, not a bolt-on tool.
Measure loyalty impact, not just efficiency gains.
Automation should elevate humanity, not dilute it. If the experience feels colder, more fragmented, or less accountable, something foundational needs rethinking.
This moment isn’t about hype. It’s about intentional design.
Let’s dig in.
This week’s must-read links:
“We Are the Last Human-Only Workforce.”
Done Beats Perfect. And CX Teams Need This Tattooed on Their Roadmaps.
Something Big Is Happening. CX Teams Don’t Get to Sit This Out.
Gartner Ranks the Top 20 AI Use Cases in Customer Service.
DCX Stat of the Week: 64% Say Service Quality Is the #1 Differentiator.
DCX Case Study: Rituals Scales Concierge Support Across 19 Countries—With Zero Peak Backlog.
“We are the last human-only workforce.”
Halfway through the interview, Vinod Muthukrishnan says something that should make every CX leader sit up.
“We are the last human-only workforce.”
Not in a dramatic way. In a practical one.
He’s not talking about chatbots getting better. He’s talking about AI becoming an equal participant in the workforce. Not a feature. Not a bolt-on. A participant.
Here’s the shift he believes most leaders are underestimating.
Customers don’t want omnichannel anymore. They want one continuous conversation. Across voice, chat, time, and context. Start on text at 10 p.m., finish on the phone the next morning, and never repeat yourself. Once they see that level of continuity, everything else feels broken.
And you don’t get there by layering AI onto a 20-year-old stack.
Vinod is blunt. If you treat AI like a tool, you’ll bolt it on. If you treat it like a foundational rebuild, you’ll rethink routing, workforce design, insights, quality, data architecture. Everything.
He also pushes back on the language we use. No more “containment.” No more “deflection.” Customers don’t want to be deflected. They want intelligent engagement. If AI handles the routine well, human agents become more valuable, not less. The easy work disappears. The hard, emotional, high-stakes work remains. That raises the bar for empathy, reasoning, and judgment.
Then he lands on trust.
Security and CX can’t live in different conference rooms anymore. Transparency around how AI works, how data is used, how guardrails are enforced. That isn’t compliance theater. It’s table stakes.
His advice for the next 90 days is refreshingly grounded. Don’t “do AI.” Solve a real experience problem. Password resets. After-hours scheduling. Queue times. Run a small, empirical experiment. Prove value. Then expand.
Start with the customer problem. Not the technology.
That’s the throughline.
Automation done right should make the experience more human. If it doesn’t, you rebuilt the wrong thing.
🔗 Go Deeper: UC Today
Done Beats Perfect. And CX Teams Need This Tattooed on Their Roadmaps
Leon Ho’s point is simple: you don’t have a productivity problem. You have a shipping problem. The last 10% keeps expanding because perfectionism is less about quality and more about identity. “Good enough” can feel like a personal failure, so you keep polishing instead of learning.
Why it matters:
In CX, “perfect” often shows up as endless journey mapping, deck tuning, and governance loops while customers keep suffering.
Shipping creates reality. Reality creates feedback. Feedback creates better CX.
Your competitors don’t need to be better. They just need to be live.
What to take:
Ship to learn, not to impress. Treat every release like a feedback loop, not a final exam.
Define “done” before you start. If the finish line is “feels right,” you’ll never cross it.
Trade polish time for iteration cycles. Three rough versions with real customer input beat one “perfect” version built in a vacuum.
The CX To-Do: Pick one stuck initiative. Write a clear “done” definition. Ship a v1 within 2 weeks, then iterate weekly.
🔗 Go Deeper: Life Hack
Something Big Is Happening. CX Teams Don’t Get to Sit This Out.
Most people still think AI is that early chatbot that needed constant correction. Matt Shumer is describing something different. AI that can take a messy goal, do the work end to end, test it, fix it, and come back with “it’s ready.”
For CX, that’s not an AI story. It’s a leadership story.
First, the pace is about to break our planning habits. CX roadmaps love year-long pilots and slow rollouts. Model capability is moving in months. If your operating model can’t keep up, you’ll look stuck even when your intentions are good.
Second, AI will widen the resolution vs loyalty gap by default. You’ll close more tickets faster. Customers will still leave if policies are unfair, promises are sloppy, and escalations go nowhere. AI can scale great CX. It can also scale bad CX at machine speed.
Third, trust is the real battleground. Customers won’t care that you reduced handle time. They’ll care that the bill is right, the answer is consistent, and a human can step in with authority when it matters.
The move now: use AI to speed up work. Use leadership to keep the experience honest.
🔗 Go Deeper: Shumer
Gartner Ranks the Top 20 AI Use Cases in Customer Service
AI isn’t the strategy. It’s the lever. The question is where to pull.
In new research, Gartner assesses and ranks the top 20 AI-driven use cases across customer service and support. Instead of chasing headlines, this report forces a harder conversation: which AI bets actually drive value, and which are just interesting demos?
Why it matters:
Not all AI use cases are equal. Gartner ranks each one by value and feasibility, giving leaders a way to prioritize instead of experiment blindly.
The use cases span the entire customer contact life cycle, not just chatbots or GenAI summaries.
This is about operational impact. Faster resolution, smarter routing, better agent augmentation, stronger knowledge management.
What’s happening:
GenAI hype has pushed broader AI adoption back into focus.
Service leaders are under pressure to invest, but many lack a clear framework to decide where AI moves the needle.
Gartner’s framework creates a shared language for tech, ops, and CX leaders to align on ROI.
The bottom line:
If you’re leading CX, don’t ask “Where can we use AI?” Ask “Which AI use cases create measurable performance lift in our service org?” Then rank them. Just like Gartner did.
🔗 Get the Report and Use Case Assessment Tool: Genesys
DCX Stat of the Week
64 percent of customers say service quality is the single most important factor separating one company from another.
This is the quiet truth behind all the AI headlines. Customers may enjoy personalization and speed, but when it comes to choosing who to stay with, service quality still wins. Not price. Not product features. Not brand voice.
For CX leaders, this changes the budget conversation. Service is not overhead. It is your competitive edge. If automation improves speed but degrades clarity, empathy, or follow-through, you are trading differentiation for efficiency.
The real move is to upgrade service quality while you automate. That means fewer handoffs, stronger resolution, empowered agents, and cleaner policies. Measure loyalty lift, not just handle time.
If 64 percent of customers are making decisions based on service, then service is strategy.
Source: Accenture Song, Customer Service on the Brink: Course Correct Now for Future Growth
🔗 MORE STATS: Daily Stats on Substack Notes
DCX Case Study of the Week
Rituals Cosmetics scales “concierge” support across 19 countries—without peak-season backlog
CX Challenge: Holiday spikes hit ~9,000 customer cases/day, and prior “chatbot-era” tools couldn’t maintain quality or consistent brand tone across markets—forcing costly seasonal staffing to prevent delays.
Action Taken: Rituals launched an AI concierge agent (“Ray”) with clear operating procedures, rolling out from initial pilot markets to 19 countries / 15 languages. They paired it with conversation review + analytics to continuously improve performance and feed “voice of the customer” insights into product discussions.
Result: Zero ticket backlog during the Black Friday → Dec 26 peak period for the first time ever—avoiding the historical need to hire ~300 temporary agents.
Lesson for CX Pros: Treat AI like a brand-trained concierge, not a generic bot: start conservative, QA hard, then scale multilingual coverage once quality is proven.
Quote: “With Ray… we had zero customer relations backlog for the first time ever.” — Rommy Verschelling, Head of Customer Relations
Further Reading: Decagon’s Rituals case study
Thank you!
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Excellent insights, perfectionism can be such a trap