THE EFFICIENCY PARADOX

When Success Creates New Problems

Some things are easier to deny than to fix. When intelligent systems work well, they create surplus capacity. And when that happens, the default corporate response is as predictable as it is destructive, cut costs.

That's exactly where businesses break their own systems.

The Problem is Human, Not Technical

The system automates customer support workflows. Response times drop. Tickets get resolved faster. The efficiency metrics look fantastic. But the dashboard doesn't show the remaining support staff now see AI as a job threat, not a growth engine.

They stop leaning into the system. They resist new use cases. They quietly work around the technology that was supposed to move the business forward. The system slowly grinds down because the people who power it no longer trust it.

This isn't a workflow problem. It's a cultural fracture.

When efficiency leads to cuts, employees don't get smarter about AI; they get smarter about protecting themselves from it. Trust breaks fast. Morale fades. The system stalls because the people driving it resist it.

The Efficiency Paradox in Action

Picture this: AI improves your finance team's productivity by 40%. Fantastic, right? Here's what happens:

Month 1: The team celebrates faster month-end closes.

Month 3: Leadership asks, "Do we really need the same headcount?"

Month 6: Two finance roles get eliminated "because of AI efficiency gains."

Month 9: The remaining team starts avoiding the AI tools.

Month 12: Productivity drops below pre-AI levels because no one trusts the system.

You've automated your way to a worse outcome. The solution seems easy. They are now surplus. Let them go! Right?

The Human Cost of Automated Efficiency: Real-World Examples

The pursuit of automated efficiency, especially in customer service, is often a classic example of the efficiency paradox. The initial gains look great on a balance sheet, but the hidden human cost can quickly turn them into a long-term liability.

A powerful example of this is the Swedish fintech company Klarna. In its drive to cut costs, Klarna heavily invested in AI-powered chatbots and reportedly laid off 700 employees. The company's CEO publicly boasted that their AI assistant could perform the work of 700 full-time human employees. This move, however, had unintended consequences. Customers began to complain about a decline in service quality, and the company faced public backlash. Klarna eventually had to reverse its decision, acknowledging that human interaction was still essential for handling complex issues and maintaining customer satisfaction. This case demonstrates that simply replacing people with AI, without a strategy for the remaining workforce, can lead to a disastrous drop in service quality and a damaged brand reputation.

Another example comes from the tech giant IBM. In an effort to streamline its human resources department, IBM introduced an AI-powered system called AskHR to handle many routine queries and administrative tasks. The automation was intended to free up HR staff for more strategic work. However, the system struggled with tasks that required empathy and subjective judgment. While it was efficient at handling simple requests, it couldn't manage complex, sensitive employee issues. IBM was eventually forced to rehire some of the HR staff it had laid off, recognizing that the AI system could not replicate the human touch necessary for many of its most critical functions.

Denham Sadler (2025) Companies backtrack after going all in on AI.

https://ia.acs.org.au/article/2025/companies-backtrack-after-going-all-in-on-ai.html

These cases highlight the core flaw in the "automate and cut" strategy. When companies prioritize short-term cost savings, they fail to see that a disengaged and fearful workforce will resist new technologies. The remaining employees, fearing for their own jobs, quietly work around the system, withhold critical feedback, and ultimately cause productivity to stall. These examples show that the efficiency paradox is not just a theoretical problem; it's a very real business risk with measurable financial and reputational consequences.

The Real Solution: Plan for Surplus Before It Arrives

Smart businesses approach efficiency differently. They plan for surplus capacity before it happens. They decide early where freed people will go and what growth initiatives will absorb their time.

Managing the Paradox:

• Be Honest About Trade-Offs Upfront: Hiding the human cost breaks trust. Being clear about where roles will change builds credibility, even when the conversation is tough.

• Invest in Real Upskilling: Not rebranded training sessions that lead nowhere. Actual capability-building that gives people ownership over the next round of system improvements.

• Phase the Automation: Rushing full automation in one wave triggers cultural rejection. Gradual deployment gives people time to find their new roles inside the system.

• Share the Benefits: When intelligent systems free up capacity, the gains shouldn't bypass the people who made it work. Salary increases, shorter work weeks, and  meaningful bonuses. Show that efficiency serves everyone, not just the balance sheet.

Why This Matters for PE-Backed Companies?

When your eye is on exit multiples, not quarterly metrics, you can afford to invest in your people. Redeploy capacity instead of cutting it, and share the benefits without flinching.

But if you're trapped in short-term cost-cutting mode, every decision collapses into efficiency theater. The system gets faster, but the business gets weaker.

The companies that pass this test build intelligent systems that keep growing. The ones that fail build systems that stop working because the people inside them stop pushing. Those early gains soon turn to a financial liability when the transition to native AI fails prematurely.