Why most AI automation projects fail because companies automate the wrong processes first | AI Insights | Lumman.ai

There’s a special kind of delusion that shows up in boardrooms when executives talk about AI.

They say things like, “AI is just a tool—like Excel.” Then they go home and Google “Will AI replace executives?” at 2 a.m.

That nervous contradiction isn’t just funny—it’s telling.

It explains why so many AI automation projects quietly fail. And not because the technology doesn’t work. It’s because companies are automating the wrong things first. Not just the wrong tasks—but the wrong kind of problems altogether.

Let’s talk about why this keeps happening.

The Fantasy of the AI Quick Win

Most AI initiatives start with a hunt for low-hanging fruit.

The logic: Identify a repetitive, high-volume, labor-intensive process. Drop in a chatbot. Or an LLM. Or RPA. Watch the efficiency savings roll in.

Think invoice processing. Employee onboarding. Tier-1 support tickets.

It looks great on a slide. You can even calculate an ROI. But here’s the catch—those processes were almost never strategically important to begin with. They’re just... annoying.

Automating them may save a few bucks, but it doesn’t help the business compete, differentiate, or make smarter decisions. It’s busywork automation. Polishing the doorknobs while the foundation cracks.

Meanwhile, the places where AI could actually create leverage—the judgment calls, the tradeoffs, the 3 a.m. decisions in supply chain or pricing strategy—those stay untouched.

Why?

Because they’re ambiguous. Political. Sometimes sacred.

And, frankly, because they make people uncomfortable.

AI Is Just a Tool (Until It Does Your Job Better Than You)

Executives say “AI is just a tool” with the same energy someone uses to say, “I’m not worried about the layoffs” while updating their LinkedIn profile under the table.

It's not the line that betrays them—it’s the tone.

Because deep down, many leaders are sensing something they don’t want to say out loud: If this thing can spot trends, prioritize risks, and generate strategy decks at superhuman speed… what am I bringing to the table?

Historically, white-collar decision-making has gotten a free pass from automation. We automated factory labor. Then customer service. Then data entry. But the suits upstairs? They were safe. Untouchable, even.

Not anymore.

Modern AI doesn’t just push pixels—it makes decisions. It synthesizes 300-page reports in three minutes. It generates copy that converts. It makes probabilistic inferences under messy constraints—the kind of work that used to be called “executive judgment.”

Suddenly, “AI tools” start to feel like competitors.

So when execs champion automation, they carefully select targets: back-office friction, support queues, reconciliation headaches. Never the decision logic that lives in their own heads.

Instead of aiming AI at the C-suite’s cognitive load, they aim it away from themselves. And toward someone else’s job.

Automating the Wrong Thing, Beautifully

Let’s say you ignore all that and follow conventional wisdom. You automate the noisy work first. The administrative grind. Tier-1 customer support. Lead routing.

Congrats. You’ve achieved what a colleague of mine calls “faster mediocrity.”

If the workflow was broken before AI, it’s still broken after AI—just more confidently so.

Take customer support. The easy play is to automate the first-line FAQs. But what if the root problem isn’t the volume—it’s the fact that your product is confusing people in the first place?

Now you’ve got a clever bot fielding rage about bad UX.

Or take invoice processing. The sexy demo is scanning PDFs and auto-matching line items. But if your procurement system is still a maze of manual approvals and bonus Excel macros, all you’ve done is create a faster bottleneck.

AI doesn’t fix broken processes. It just accelerates them.

Garbage in, garbage at scale.

The Real Bottleneck? Nobody Understands the Process

Here’s the uncomfortable truth: Most leaders don’t actually know how work gets done in their own companies.

They know how it’s supposed to happen. They know what the org chart says. But the real workflow lives in tribal knowledge, Slack threads, undocumented branches of logic, and whatever Carol from finance remembers from 2014.

Then a consultant shows up:

“What process do you want to automate?”

“Uh... this one. It takes too long.”

“Why does it take too long?”

“Not sure. It just always has. Something about vendor forms.”

If you build AI on top of that level of fuzziness, you’re not streamlining—you’re institutionalizing dysfunction.

It’s like paving a cow path. Sure, the road’s smoother now. But it still goes in the wrong direction.

Before you automate, document. Not just what people do, but how they decide. What are the judgment calls? What heuristics are in play? Where’s the real friction?

Forget AI. Half of Fortune 500 companies need a sober process audit and a whiteboard before they need another model.

Boring Is Beautiful

Ironically, some of the most successful AI stories are also the least glamorous.

Reconciling mismatched SKUs between two legacy ERP systems.
Identifying duplicate customer records across regional CRMs.
Auto-flagging non-compliant contract clauses buried in NDAs.

You don’t hear about these at tech conferences. No one gets a keynote for automating document QA.

But they work. They create trust. And once companies prove that the plumbing works, they’re ready for more ambitious moves.

The lesson: Start with boring clarity, not flashy complexity.

The best candidates for automation have a few traits in common:

High volume
Clear rules (or improvable training data)
Low political landmines
Actual value when done better—not just when done cheaper

That last one is key.

If automating a task saves you $100K but doesn’t make customers happier, employees faster, or products better—you’ve just built a spreadsheet win. Not a business win.

Don’t Fix the Workflow—Fix the Judgment

The quiet revolution isn’t automating workflows.

It’s automating judgment.

While companies obsess over form-fillers and approval routing, the real opportunities are hiding in decision points. Messy questions like:

Should we prioritize Account A or Account B for expansion?
What’s the optimal reorder quantity under these cost curves?
Which deal carries the most regulatory risk?

These are the calls that eat up hours of meetings, group chats, and fuzzy Excel models.

They’re also where good companies become great.

Every business has them. But they’re rarely mapped. They're hard to articulate. And frankly, they’re politically sensitive. The moment you put an algorithm in the loop, someone asks, “Wait—what were we basing those decisions on before?”

Which is exactly why you should do it.

Because you’ll either discover:

Good judgment can be scaled with AI, or
You were faking it the whole time

Either way: progress.

Most Companies Don’t Know Their Edge

One final point to wrestle with.

The companies that win with AI don’t just pick the right processes to automate. They use AI to reinforce whatever makes them different.

They automate not just for efficiency—but for advantage.

Netflix didn’t use AI to shrink call center costs. They used it to build personalized viewing.
Amazon didn’t automate back-office HR just to cut headcount. They used AI to drive predictive inventory and dynamic pricing.
Spotify didn’t launch Discover Weekly to simplify music curation. They used it to create a reason to come back every Monday.

In each case, AI reinforced the thing that gave them leverage—something tied to growth, experience, loyalty, or speed.

If you don’t know what your lever is, AI won’t find it for you.

It’ll just help you do the wrong thing faster. And probably at scale.

So Here’s the Playbook, Simplified

Stop treating AI like IT.
Efficiency matters, but insight compounds. Automate for differentiation.
Audit before you automate.
If you don’t understand the current logic, don’t try to speed it up. You’ll only institutionalize the mess.
Start with the plumbing, aim for the brain.
Build trust through boring wins—but steer toward judgment-heavy decisions where intelligence adds leverage.
Don’t protect executives—involve them.
The smartest leaders ask: “What parts of my job should be offloaded to AI?” The rest spend time pretending they're irreplaceable.

Companies don’t fail at AI because the tech isn't ready.

They fail because they optimize for what’s legible, not for what actually matters. They chase cost cuts instead of leverage. And they mistake automation for strategy.

The fix isn’t more compute power.

It’s more honesty.

About how your business really works. About where intelligence matters. And about where your value truly comes from—human or machine.

Until then?

You’re just building better wheels for a shopping cart that’s still missing a steering wheel.