I watched a business make one decision that looked harmless — and it quietly set their entire operation on fire.
I worked with a healthcare organisation that had a reception team drowning in calls. Everyone was stressed. Customers were frustrated. Leadership was desperate for relief. So they did what most teams do under pressure: they outsourced the calls, thinking “that’ll fix it.”
Except it didn’t fix anything. The problem got worse.
Demand doubled. Complaints spiked. And they were stuck in a 12-month contract they literally had to pay their way out of.
Here’s what no one saw coming
The outsourced team didn’t understand the healthcare environment, the policies, or the specific needs of the patients they were speaking to. The organisation hadn’t checked for this — they’d done no due diligence on the vendor’s domain knowledge. They’d just seen “we handle calls” and assumed that was enough.
The outsourced team couldn’t access patient records. They couldn’t filter enquiries. They couldn’t redirect appropriately. So every single call turned into a formal request that came straight back to the internal team to handle.
200 calls a day became 400. And the original team was now cleaning up someone else’s work on top of their own.
The lesson that applies equally to AI
The same logic applies to AI adoption, and I see this mistake made with AI tools just as often as with outsourcing.
Businesses hand off a broken or misunderstood process to an AI tool, assuming the technology will sort it out. The AI does what it’s told — efficiently and at scale. But if what it’s been given is broken, it scales the breakage.
This is why AI strategy has to start with process understanding. Before you hand anything off — to a company or to an AI — you need to understand what you’re actually handing over and whether it’s ready to be delegated.
You can’t hand off a problem you haven’t understood
This is the principle I come back to most often with clients considering outsourcing or AI implementation: if you pass on the mess, you’re just paying someone else to grow it.
The healthcare organisation’s calls weren’t really the problem. The calls were a symptom. The real issue was upstream — a service delivery failure that was generating failure demand at scale. Outsourcing the calls without addressing the cause just added a layer of inefficiency between the problem and the people who could fix it.
The framework: understand it first, hand it off second
Before outsourcing any function — or deploying any AI tool — work through these questions:
- Do we fully understand what we’re trying to hand off?
- Have we mapped the process end to end?
- Do we know what information, context, or access the person or system will need to handle it properly?
- Have we identified which parts of this process require domain expertise or judgment?
- Are we solving a real problem or just moving it somewhere less visible?
If you can’t answer those questions confidently, you’re not ready to hand off. The work at this stage is the same foundational work that has to happen before any automation: understand, simplify, then delegate or automate.
A practical next step
If you’re considering outsourcing a function or deploying AI in your business, the Business AI Health Check is a useful starting point. It takes three minutes and helps you identify where the real issues in your operation are — before you invest in any solution.
Or if you’d prefer to talk it through, book a free strategy call. We’ll help you understand what you actually have before deciding what to do with it.
Fix it before you hand it off. Outsourcing and AI don’t fix problems — they scale whatever you give them.