Most boards hear the promise: AI in customer service unlocks speed, data, happier customers. But here’s the thing when you supercharge customer interaction with automation, the rest of your business rarely gets the same upgrade. The operational fallout? That’s what most leaders miss.
You see, companies design operations to balance on a knife-edge. As one industry expert puts it, “Operations as companies are finely balanced.” Budgets are stripped to essentials, with every department permanently expected to do more with less. The myth? That you can automate the customer contact front without a ripple effect elsewhere in the business.
Deploy AI chat, returns, instant help and suddenly, every customer can reach you, 24/7. That’s progress on the surface. But when customers request refunds, escalation, or bespoke help, it’s no longer bots; it’s “send a person”, “dispatch a replacement”, or “escalate to a specialist”. AI hasn’t solved the problem, it’s moved it down the line at higher speed and volume.
Christian Terwiesch gets to the heart of it: “Let’s acknowledge it’s pretty damn hard. It’s tedious, very labor intensive, and has to be provided on demand and in synchronization with the needs of the customer. It’s nothing you can do on an assembly line.” Automation doesn’t erase underlying limitations; it exposes them.
Every superior customer experience comes at a price. More hands, more machines, more logistics or a conscious decision to accept wait times, missed calls, or “computer says no”. The inescapable truth? “There’s a cost-quality trade-off.” You can have frictionless digital journeys, but delivering on promises at scale always means new costs or operational stress.
Boards often see only the tech ROI pitch. What’s hidden is the surge: AI unlocks demand you didn’t budget for. The rest of the company can’t magically absorb the workload. Without strategic resource planning across people, process, inventory—you risk operational whiplash. Shareholder priorities often trump system slack, leading to gaps that get bigger with every new layer of automation.
Banking and highly scrutinised industries plan capacity for regulation and public scrutiny; most sectors don’t. Councils or everyday retailers, for example, simply can’t (or won’t) scale. The result is a systemic consequence: bottlenecks, frustration, and value leakage.
The same Wharton article lays it bare: “Companies have been trying to use AI to push out the trade-off curve and provide better service at a lower cost. But solving for lower costs, as opposed to better customer service, is a problem, according to Terwiesch.” In other words, efficiency for its own sake rarely gives the outcomes boards actually want or customers remember.
AI’s a multiplier. If your back end is weak, AI won’t fix it, it will expose it faster and more visibly than ever.
If you want AI to future-proof your customer service not just disguise today’s limits get honest. Every board-level decision on automation should trigger a total chain capacity audit, candid scenario planning, and a fresh look at systemic risks.
AI is a gift for proactive boards… but only if you’re ready for the demand and complexity it uncovers.
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