Automating Broken Processes Is the Fastest Way to Waste AI Spend

Your business bought an AI tool. The team started using it. Work is moving faster.

But somehow, nothing actually improved.

Decisions are still unclear. Errors still happen. You're still in the weeds answering the same questions. The business feels just as reactive as before—only now it's reacting faster.

This isn't an AI problem. It's a process problem.

And automating broken processes doesn't create efficiency—it amplifies dysfunction at machine speed.

What Happens When You Automate Before You Fix

Here's a pattern I've observed in 25 years across different industries:

Businesses invest in technology. They implement tools. They train teams. And then they wonder why results don't improve.

The answer is usually the same: They automated existing workflows without questioning whether those workflows made sense in the first place.

They automated:

• The same approval bottlenecks

• The same unclear handoffs

• The same workarounds that exist because nobody fixed the root problem

• The same "this is how we've always done it" steps that never created value

AI gets layered onto systems that weren't designed for clarity or efficiency. So instead of improving outcomes, automation accelerates confusion, misalignment, and waste.

It looks modern. It feels productive. But it rarely creates the leverage businesses expect.

Why Automating Broken Processes Makes Things Worse

AI is designed to do one thing exceptionally well: execute consistently at scale.

If your process is clear and effective, AI makes it faster and more reliable. If your process is messy and broken, AI executes the mess consistently—which means the dysfunction becomes systematic instead of occasional.

That's the danger of automating broken processes. You're not just wasting the cost of the AI tool. You're multiplying the cost of problems that already existed—and making them harder to fix later.

Here are examples I see across businesses:

AI communication tools layered onto undefined response rules. The system generates replies automatically, but nobody documented what commitments the business actually makes to customers. So AI creates inconsistent promises that teams have to fix manually—at higher volume than before automation existed.

AI financial tools layered onto inconsistent categorization. Automation processes transactions, but the categories were never standardized across the business. Month-end close still requires hours of manual work to correct what the AI miscategorized—defeating the entire purpose of automation.

AI scheduling tools layered onto unclear decision rights. The system books meetings efficiently, but nobody defined who has authority to make decisions in those meetings. Calendar volume increases while actual progress stays flat because meetings don't produce outcomes.

AI content tools layered onto undefined messaging. Automation generates marketing materials, but the business never clarified its positioning, tone, or value proposition. So AI produces volume without coherence—and the brand message becomes diluted instead of amplified.

In every case, the technology works exactly as designed. The problem is that what it's automating was already broken.

The Question Most Businesses Skip

Here's what businesses should ask before implementing any automation:

Does this process actually need to exist?

Not "Can we automate it?" but "Should we be doing this at all?"

Many business processes exist not because they create value, but because they compensate for something else that's missing:

• Unclear ownership (so processes add approval steps to create accountability)

• Undefined standards (so processes add review steps to catch inconsistency)

• Historical workarounds (so processes add steps that once solved a problem that no longer exists)

When you automate those processes, you're permanently encoding inefficiency. You're building technology around problems instead of eliminating the problems.

And here's what makes this expensive: Once automation is implemented, changing it becomes significantly harder than changing manual processes. You've created technical debt on top of operational debt.

Why This Pattern Keeps Repeating

Businesses keep making this mistake for a predictable reason: Nobody inside the organization can see the full picture.

When you're managing daily operations, every process feels necessary. Every step seems to serve a purpose. Every approval appears justified.

That's because you're inside the system. You know the history. You understand the exceptions. You remember why each workaround was created.

But what feels normal to you looks inefficient to anyone evaluating it objectively.

Here's what I learned from large-scale operational work: Organizations that successfully implement technology don't start with the technology. They start by questioning whether their processes make sense—and they bring in external perspective to challenge assumptions that have become invisible.

That's not because internal teams aren't capable. It's because seeing your own blind spots is nearly impossible when you're inside the complexity every day.

What Has to Change Before Automation Works

Businesses that get real value from AI don't start by selecting tools. They start by examining structure.

They ask:

• Who actually owns this outcome?

• What decision does this process support?

• Which steps change the result, and which steps just exist because they always have?

• What would break if we eliminated 80% of this process?

Those questions expose where complexity serves a purpose and where it just creates friction.

Only after that clarity exists does automation make sense—because then you're automating something that works, not something that barely functions.

Working inside one of Berkshire Hathaway's flagship companies taught me that disciplined organizations automate last, not first. They fix processes until they're simple and clear. Then they automate the simplified version.

That sequence matters. Simplify first, then automate. Not the other way around.

The Real Risk Isn't Falling Behind on AI

The biggest risk heading into the next wave of AI adoption isn't that your business will fall behind competitors who implement faster.

It's that you'll modernize the surface while leaving fundamental problems untouched.

You'll have:

• Faster execution of the same unclear processes

• More consistent delivery of the same flawed outcomes

• Higher technology costs with the same operational results

That's not transformation. That's expensive distraction.

And the longer automation runs on broken processes, the harder it becomes to fix—because now you have technology dependencies, data inconsistencies, and team members who've learned to work around the automated dysfunction.

What Your Business Needs Before AI Enters

If you're considering automation, test your readiness by answering these questions:

Can you clearly explain what decision this process supports? If not, you don't understand the process well enough to automate it. AI can't clarify unclear purpose—it will just execute unclear purpose consistently.

If you eliminated 80% of the steps, what would actually break? If the answer is "not much," then most of the process is waste. Automating waste doesn't improve efficiency—it just makes waste permanent.

Who owns the outcome of this process? If ownership is unclear or diffused across multiple people, automation won't help. It will just create a system that nobody feels responsible for maintaining.

What does "done correctly" mean for this process? If that definition is vague or if different people give different answers, automation will execute inconsistently—because the underlying standard doesn't exist.

These aren't academic questions. They're the diagnostic that separates successful automation from expensive mistakes.

And most businesses can't answer them honestly without external help—not because they lack intelligence, but because they're too close to the operations to see objectively.

Frequently Asked Questions

How do I know if my processes are ready for automation?

If you can clearly document the process, explain why each step exists, and identify who owns the outcome—your process might be ready. If those elements are unclear, automation will execute the confusion consistently. The AI Readiness Assessment helps identify where your operations actually stand.

Can't AI just fix broken processes automatically?

No. AI executes instructions—it doesn't create clarity or question whether those instructions make sense. If your underlying process is broken, AI will automate the broken version. That makes dysfunction systematic instead of occasional, which is more expensive and harder to fix later.

What if we've already automated and it's not working?

That usually means the process wasn't ready. The solution isn't better AI—it's process redesign. You'll likely need to step back, fix the underlying workflow, and then re-implement automation on top of a structure that actually works. A Process Health Check can identify what needs restructuring.

Won't redesigning processes delay our AI implementation?

Redesigning takes weeks. Fixing automated dysfunction takes months or years—and costs significantly more. The question isn't whether you have time to fix processes first. It's whether you can afford to automate broken processes and deal with the compounding problems later.

How is this different from just avoiding AI entirely?

AI creates enormous value—when processes are ready for it. The goal isn't to avoid automation. It's to prepare properly. Businesses that fix processes first and then add AI see immediate returns. Businesses that automate first and fix later see immediate costs and long delays before value appears.

Ready to see if your processes are AI-ready?

Get the AI Readiness Assessment and find out:

✓ Whether your operations can benefit from automation or if you're automating chaos

✓ Which processes need redesign before automation enters

✓ Where AI will create leverage vs. where it will amplify problems

✓ What needs to be fixed first for successful implementation

Get the AI Readiness Assessment - See where your business stands

Or book a free 30-minute Process Health Check. We'll assess whether your operations are structured for successful automation—no sales pitch, just honest diagnosis. You'll leave knowing what needs to be fixed before AI enters.

SOURCES

Insights based on 25 years of operational experience across different industries, and observations of AI implementation patterns across business operations.