AI Won’t Save Your Broken CX. It Will Expose It

Last year, I had to call a customer support team for something embarrassingly simple.

My issue was a minor billing error.

First came the chatbot.

It confidently told me my issue was resolved before I had even explained it properly.

Then came the IVR maze.

Press 1 for this. Press 3 for that. None of which matched my problem.

When I finally reached a human, she asked me to repeat everything. And then she transferred me to someone else, who asked me to repeat everything.

Now imagine replacing that entire broken journey with AI.

What do you get?

Faster frustration.

Recently, I read an article arguing that AI-driven layoffs may be undermining customer experience rather than improving it. That struck a chord. Last week, I wrote about something similar: how AI costs in customer experience may eventually outpace human costs if we don’t deploy it wisely.

Put these two ideas together, and here is the uncomfortable truth:

If your customer experience is broken, AI will not fix it. It will amplify it.

And in some cases, it will make it more expensive.

The Temptation of Automating First

AI today is seductive.

Boardrooms are excited. Investors are impressed. Vendors are persuasive.

It feels like a no-brainer:

Let’s automate. Reduce headcount. Deploy bots. Cut costs.

But here’s what I’ve seen repeatedly in organizations:

• They automate chaos.
• They automate unclear policies.
• They automate bad processes.
• They automate internal confusion.
• They automate broken handoffs.

And then they wonder why CSAT drops.

AI does not remove structural flaws. It operates inside them.

If your escalation paths are unclear, AI will escalate to the wrong places faster.

If your data is messy, AI will confidently deliver incorrect answers.

If your policies are inconsistent, AI will apply them consistently, and consistently badly.

6 Customer Experience Problems to Fix Before AI Automation

Over the years, across multiple client engagements, here are the patterns I’ve noticed. If you want automation to work, these must be fixed first.

1. Fix the Process, Not the Interface

Once, I worked with a contact center that wanted to deploy a conversational AI bot for refunds.

Here was the problem:

The refund process required approval from three internal teams. No documented SLA. No clear ownership.

The bot went live.

Customers were told: “Your refund is being processed.”

Internally, nothing moved for days.

The bot wasn’t the issue. The process was.

If your backend process is broken, automation becomes a beautifully designed façade covering structural decay.

Fix the workflow before you digitize it.

Map it. Simplify it. Assign ownership. Define timelines.

Then automate.

2. Clean Your Data Before You Let AI Touch It

AI feeds on data. And data inside most organizations is messy.

• Duplicate records.
• Outdated consent flags.
• Incomplete customer profiles.
• Conflicting policies.

When AI operates on dirty data, it doesn’t hesitate.

It answers confidently and wrongly.

I’ve seen bots confidently tell customers their loan was approved when it wasn’t. Or that a payment was due when it had already been made.

The issue wasn’t AI hallucination.

It was data fragmentation.

Before automation:

• Consolidate data sources.
• Ensure consent and compliance metadata are accurate.
• Audit edge cases.
• Clean historical records.

Otherwise, you’re just accelerating misinformation.

3. Clarify Ownership Internally

This one is subtle but powerful.

In many organizations, if you ask:

Who owns customer experience?

• Marketing says brand.
• Operations says service.
• Product says features.
• Compliance says risk.

AI requires clarity.

If the bot gives a wrong answer, who is accountable?

If automation denies a request incorrectly, who fixes it?

If customers escalate from bot to human, who owns the handoff?

Without defined ownership, automation becomes a blame machine.

Fix accountability first.

4. Redesign Metrics Before You Redesign Headcount

This is where my earlier thinking on AI costs comes in.

Many organizations deploy AI to cut headcount.

But they measure success by:

• Reduced agent count
• Faster average handling time
• Lower cost per contact

Here’s the danger.

If you optimize for speed and cost alone, you create shallow interactions.

Customers might get responses faster, but with less depth.

And then:

• Repeat contacts increase.
• Escalations increase.
• Churn increases.

Which increases the cost again.

AI does not automatically reduce long-term cost. It reduces visible cost in one line item.

Before automation:

• Define outcome-based metrics.
• Track first contact resolution properly.
• Track repeat call rate.
• Track customer effort.

Otherwise, your savings are temporary, and your reputation damage is permanent.

5. Train Humans for Hybrid Models

This one is counterintuitive.

Automation increases the complexity of human work.

Why?

Because AI handles the simple stuff.

Humans now handle:

• Emotional situations
• Edge cases
• Angry escalations
• Complex policy interpretations

If you reduce staff without upgrading skill levels, you are creating a mismatch.

AI does the easy 60%. Humans now handle the hardest 40%.

And they burn out.

Before automation:

• Upgrade agent training.
• Redefine job roles.
• Equip agents to work with AI tools.
• Clarify when AI must step aside.

Automation should augment humans, not corner them into crisis management roles.

6. Identify the Right Use Cases

AI works best where:

• Patterns are predictable.
• Outcomes are structured.
• Decisions are rules-driven.
• Volume is high.

For example, look at password resets, order status queries, address updates, payment reminders, and appointment confirmations. These are great use cases for your AI deployment.

AI struggles where:

• Emotional nuance is high.
• Policies are ambiguous.
• Exceptions dominate.
• Human reassurance is required.

Once, I saw a company deploy a bot for bereavement-related banking processes.

The bot was efficient.

The customers were grieving.

Efficiency is not empathy.

Choose your use cases carefully.

The Hidden Cost Curve of AI

Now let’s talk money.

At the outset, automation reduces visible costs by reducing the number of agents and lowering payroll costs.

But here’s what creeps in quietly:

• Increased tech stack licensing.
• Continuous model training.
• Prompt engineering and governance.
• Compliance audits.
• Infrastructure scaling.
• Cybersecurity.
• Oversight teams.

AI is not a one-time deployment. It is a living system.

It demands monitoring, tuning, updating, and guardrails.

If you underestimate this, your AI cost curve will catch up.

And sometimes outrun human costs.

Not because AI is bad.

But because governance wasn’t designed properly.

A Story About Slowing Down

A few years ago, I was trekking in the hills. We were eager. Young. Competitive.

We started fast.

Within an hour, half the group was exhausted.

Our guide smiled and said, “The mountain rewards rhythm, not speed.”

Customer experience is the same.

If you rush into automation without rhythm, without fixing foundational issues, you will collapse halfway.

AI is powerful.

But power without preparation is expensive.

The Playbook: Fix, Then Automate

If I had to summarize what organizations should do before deploying automation, here is my playbook:

• Simplify and document core processes.
• Clean and centralize customer data.
• Define clear accountability for CX outcomes.
• Align metrics with outcomes, not just cost.
• Upgrade human capability for hybrid models.
• Start with high-volume, low-emotion use cases.
• Build governance into AI from day one.

Automation should be the final layer, not the first reaction.

The Real Question Leaders Should Ask

Replace

How much can we cut?

With

What friction are we eliminating?

Replace

How many agents can we replace?

With

How many customers can we serve better?

Replace

How fast can we deploy AI?

With

What must we fix before AI sees this?

Because AI is not magic.

It is multiplication.

• It multiplies clarity.
• It multiplies confusion.
• It multiplies efficiency.
• It multiplies dysfunction.

You decide which one.

We are in a phase where AI feels inevitable.

And it is.

But inevitability does not equal readiness.

If you deploy automation on broken foundations, you won’t save money.

• You will accelerate disappointment.
• You will frustrate customers faster.
• You will lose trust faster.
• You will burn out employees faster.

Eventually, you will spend more time fixing what you automated too quickly.

The organizations that win won’t be the ones who automate first.

They will be the ones who fix first.

Then automate with intention.

AI will not save your broken customer experience.

It will expose it.

And exposure, in today’s world, is more expensive than inefficiency.

Choose wisely.