Look, I love AI. I ship with it daily—it handles my boilerplate, catches my typos, and keeps me focused on the problems that actually need my brain. But watching companies treat AI like a magic wand instead of what it actually is? That's how you end up as a cautionary tale on Twitter. Enter the Commonwealth Bank of Australia, starring in August's biggest "oops" moment. ## The Story That Made Me Cringe **August 2025**: CBA decides to cut 45 customer service jobs because their shiny new AI voice bot was supposedly crushing it. Management probably saw some chart showing "calls down 15%" and thought they'd cracked the code. **A few days later**: Reality hit. The bot wasn't reducing workload—it was **increasing** it. Call volumes went up. Managers got pulled onto phones. Overtime became the band-aid for backed-up queues. **The aftermath**: CBA backtracked, apologized, and offered those 45 people their jobs back. Classic "we made an error" press release damage control. As a developer watching from the sidelines, this felt painfully familiar. It's the same pattern I see everywhere AI gets deployed without anyone asking the hard questions. ## This Wasn't an AI Failure—It Was a Systems Failure Here's what really went wrong, and why it sounds like every rushed deployment I've ever witnessed: ### 1. They Measured the Wrong Thing Someone looked at raw call volume and called it success. But what about repeat calls? Escalation rates? Customer satisfaction? It's like measuring website performance by counting page loads while ignoring that half of them are timing out. ### 2. No Real Testing Under Load I guarantee their bot worked great in the demo environment. Probably handled the happy path beautifully. But real customer service? That's edge cases, angry people, and complex problems that don't fit neat categories. You can't A/B test with 10 calls and then scale to thousands. ### 3. Zero Rollback Strategy The moment things went sideways, they should have had a kill switch. Instead? Overtime shifts and managers playing phone operator. That's not a contingency plan—that's panic mode. ### 4. They Forgot Humans Aren't Just Overhead Customer service isn't just answering questions. It's de-escalation, empathy, reading between the lines. All the stuff that makes AI great at FAQs but terrible at "my card got stolen and I'm stranded in another country." ## The Hidden Costs Nobody Talks About Even when you "save" money on headcount, the bill always comes due: - **Operational chaos**: Overtime costs, management time, stressed teams - **Reputation damage**: "We fired people for robots then hired them back" isn't great PR - **Technical debt**: Quick fixes, patched systems, and band-aid solutions - **Trust erosion**: Employees wondering if they're next, customers questioning your judgment I've seen this play out in smaller companies too. You cut costs in one area, and suddenly you're bleeding money everywhere else. ## How to Not Be CBA: A Developer's Approach Treat AI deployment like you'd treat any critical system change. Because that's what it is. ### Start Small and Contained - Route **less than 10%** of traffic to the bot initially - Run parallel systems—humans handling the same calls for comparison - Have real humans monitoring every interaction, not just metrics ### Build Actual Safety Rails - **Hard blocklists** for topics that must escalate (complaints, fraud, anything emotional) - **Automatic rollback triggers** tied to real KPIs, not vanity metrics - **Clear escalation paths** with no dead ends ### Measure What Actually Matters Don't just count handled calls. Track: - First-contact resolution rates - Customer satisfaction scores - How often people call back about the same issue - Time to resolution for escalated cases ### Test the Disaster Recovery - Practice turning the bot off completely - Make sure humans can absorb the load quickly - Run this drill regularly, not just once ### Keep Humans in the Loop by Design - Live monitoring dashboards with human override capabilities - AI suggests actions, humans approve anything beyond simple FAQs - Regular human review of AI decisions ## What I'd Build If I Were Starting Over If I were architecting this from scratch (and I've thought about it for my own projects): **Phase 1**: Bot handles only the most basic FAQs, everything else goes to humans immediately. No exceptions. **Phase 2**: Gradually expand scope based on success rates, but only after proving Phase 1 works flawlessly for weeks. **Phase 3**: AI assists human agents instead of replacing them. Real-time suggestions, auto-filled responses, sentiment analysis—but humans stay in control. **Always**: Comprehensive logging, A/B testing, and the ability to roll everything back with a single button press. ## The Real Lesson Here This isn't really about AI being bad or good. It's about treating powerful tools with the respect they deserve. In my own apps, I use AI for code generation, but I review every line. I let it handle routine tasks, but I stay involved in the decision-making. I trust it for the boring stuff, but I keep my hands on the wheel. CBA's mistake wasn't using AI—it was assuming AI could handle complexity without human oversight. They optimized for a spreadsheet instead of the actual problem they were trying to solve. ## If You're Building This Stuff Whether you're implementing chatbots, AI-powered features, or automated systems: - [ ] **Start with 5% traffic**, not 50% - [ ] **Measure outcomes**, not just outputs - [ ] **Build rollback into the architecture**, not as an afterthought - [ ] **Test with real edge cases**, not just happy paths - [ ] **Keep humans involved** in anything that affects real people - [ ] **Practice your disaster recovery** before you need it > **Bottom line**: AI is incredible when you use it right. But "right" means treating it like the powerful, unpredictable tool it is—not like a magic solution to complex human problems. The future belongs to teams that make AI work **with** people, not instead of them. CBA learned that lesson the expensive way. Don't be CBA.