The article is filled with case studies, but one stands out as particularly relevant to our current AI automation frenzy.
Ford’s accounts payable department was gearing up for a standard efficiency project. The goal: reduce staff by 20%. Not bad for a corporate initiative. Management was feeling pretty good about the plan.
Then someone looked at Mazda.
Ford’s accounts payable department had 500 people. They were planning to cut that to 400.
Mazda’s accounts payable department had 5 people.
Not 500. Not 400. Five.
That’s not a 20% improvement. That’s a 99% improvement. And it’s the difference between automating a process and obliterating it.
Ford didn’t get to Mazda’s numbers by automating their existing process. They couldn’t. You can’t automate your way from 500 people to 5 people by making the same process faster.
As Bill Gates famously said: “Automation applied to an efficient operation will magnify the efficiency. Automation applied to an inefficient operation will magnify the inefficiency.”
Ford had to completely reimagine how accounts payable worked. They had to question every assumption. They had to throw out the old process and start from scratch.
The technology—databases, computers, networks—was the small player in this story. The big player was the willingness to obliterate the old way of doing things.
We’re living through the AI automation hype cycle. Companies are being told that AI will revolutionize their operations, eliminate inefficiencies, and cut costs dramatically.
And they will—if you do the hard work of reengineering first.
But most companies are making the same mistake Ford almost made. They’re taking their existing, inefficient processes and trying to automate them with AI.
The result? Automated inefficiency. Faster dysfunction. More expensive complexity.
Why was Mazda’s accounts payable department so small? They didn’t have the baggage of “the way we’ve always done it.”
Much of the Japanese economy had to rebuild from scratch after World War II. There were no legacy processes to protect. No entrenched departments to defend. No politics around “but we’ve always done it this way.”
They could start with a clean sheet and ask: “What’s the simplest possible way to accomplish this goal?”
That freedom led to radically simpler processes that required far fewer people.
Today’s automation vendors would have you believe their technology is pixie dust. Just sprinkle AI on your problems and watch them disappear.
It won’t work.
If your process is broken, automating it just means you’ll produce wrong answers faster. If your workflow is overcomplicated, adding AI just creates overcomplicated AI workflows.
The vendors selling you automation don’t want you to obliterate your process. They want you to automate your existing one. Why? Because that’s a technology sale. Obliteration requires consulting, change management, and hard thinking. That’s messy and expensive.
Buying software is easier than rethinking your business.
Before you automate with AI, ask:
1. If we started from zero today, how would we do this? Don’t ask “how can we make our current process faster?” Ask “do we need this process at all?”
2. What’s the simplest version that could work? Ford’s existing process had multiple checks, approvals, and verification steps. Mazda eliminated most of them by redesigning the upstream process so they weren’t needed.
3. What are we doing because ‘that’s how it’s always been done’? Legacy processes often exist because of constraints that no longer apply. The approval chain that made sense when everything was on paper might be ridiculous when everything is digital.
4. Can we eliminate this instead of accelerating it? The fastest process is the one you don’t have to do at all.
Here’s what’s crucial: Ford and Mazda both had access to the same database technology. The technology wasn’t Mazda’s advantage.
The advantage was that Mazda designed their process around what was possible with the technology, while Ford was trying to use the technology to speed up their old process.
Sound familiar?
Today, everyone has access to the same AI models. OpenAI, Anthropic, Google—they’ll sell to anyone. The technology isn’t your competitive advantage.
Your advantage is being willing to obliterate your process and redesign it around what AI makes possible.
Thirty years later, Hammer’s message is more relevant than ever.
You can’t just keep doing what you’re doing, automate it with AI, and hope to survive. Your competitors who are willing to reimagine their processes from the ground up will destroy you.
Not because they have better AI. Because they have better processes that happen to use AI.
If you’re serious about AI transformation:
Stop the automation projects. Pause any “let’s add AI to this process” initiatives.
Start with obliteration. Pick one inefficient process and ask: “If we could start over, how would we do this?”
Design for AI, don’t retrofit it. Build new processes that take advantage of what AI can do, rather than bolting AI onto old processes.
Measure end-to-end outcomes. Ford measured people in their department. Mazda measured cost per transaction and error rate. One metric encourages efficiency. The other encourages obliteration.
Be willing to throw everything out. The hardest part isn’t the technology. It’s having the courage to say “our current approach is fundamentally wrong.”
Thirty years ago, companies failed at reengineering not because they lacked technology. They failed because:
Today, companies are making the exact same mistakes with AI.
The question isn’t “how can we use AI to automate this process?”
The question is “what would this process look like if we designed it today with AI as a fundamental capability?”
One question gives you incremental improvement. The other gives you the Ford-to-Mazda leap.
Struggling to figure out where to start? I help companies identify which processes to obliterate and how to redesign them for AI-first operations. The goal isn’t just automation—it’s transformation that delivers measurable business results. Let’s talk about reimagining your processes before you automate them.
Hammer, M. (1990). “Reengineering Work: Don’t Automate, Obliterate.” Harvard Business Review, July-August 1990. https://hbr.org/1990/07/reengineering-work-dont-automate-obliterate
About the author: Richard Wellman helps companies implement AI automation that delivers measurable business value. With experience in business process reengineering and enterprise transformation,he specializes in identifying processes that should be obliterated, not automated.
]]>“If I had an hour to fix a problem I would spend 55 minutes on forming the question.”
— Often attributed to Albert Einstein (and variations to other luminaries)
Whether Einstein actually said this or not, the principle is profound. And nowhere is it more relevant than in prompt engineering.
Most people treat AI like a search engine. They type in a quick question, hit enter, and hope for useful results.
“Write me a sales email.”
“Analyze this data.”
“Create a marketing plan.”
The AI dutifully generates something. It’s grammatically correct. It looks professional. And it’s completely useless because the question was lazy.
Garbage in, garbage out—but with better grammar.
The real work of prompt engineering isn’t learning special syntax or magic words. It’s the discipline of clearly defining what you actually want.
This is hard. It requires you to:
In other words, it forces you to think clearly about the problem before demanding a solution.
That’s why spending 55 minutes on the question isn’t wasted time. It’s the work.
Bad Prompt:
Write a sales email.
Good Prompt:
Write a sales follow-up email for a B2B SaaS prospect who attended our webinar
on AI automation but didn't respond to our first outreach.
Constraints:
- Keep it under 150 words
- Don't mention pricing (that's for the call)
- Reference specific value from the webinar content
- Include a soft ask for 15-minute discovery call
Tone: Professional but conversational, not salesy
Context: This prospect is a VP of Operations at a 200-person manufacturing
company. They're exploring AI but don't have internal expertise. Our
differentiation is practical implementation vs. theoretical consulting.
Which one will give you a better result?
The second prompt took 5 minutes to write. It will save you 30 minutes of editing generic AI output into something actually useful.
Before you write a prompt, answer these five questions:
Not “a blog post” but “an 800-word blog post formatted for LinkedIn with 3 actionable takeaways and a CTA to book a consultation.”
Not “people” but “CFOs at mid-market companies who are skeptical of AI hype and need to see ROI in months, not years.”
The AI doesn’t know your industry, your company, your product, or your customer’s pain points. You have to provide that.
Length limits, tone requirements, format specifications, things to avoid, compliance requirements.
What criteria will you use to evaluate it? Define those upfront.
Lazy prompt:
Analyze this sales data.
Better prompt:
Analyze Q4 sales data to identify:
1. Which product lines underperformed vs. target
2. Whether the decline was due to volume or pricing
3. Geographic patterns in the decline
4. Correlation with sales rep tenure
Format findings as an executive summary with:
- 3 key insights (bullets, not paragraphs)
- Supporting data table
- 2 recommended actions
I need this for Thursday's board meeting, so focus on findings that
suggest concrete actions we can take in Q1.
Lazy prompt:
Write a Python script to process CSV files.
Better prompt:
Write a Python script that:
- Reads CSV files from /data/input/ directory
- Filters for rows where status = "complete"
- Calculates total revenue by product category
- Outputs summary to /data/output/summary.csv
Requirements:
- Handle missing values gracefully
- Log errors to /logs/processing.log
- Use pandas library
- Include error handling for file not found
- Add comments explaining each major step
Input CSV format: date, product_id, category, revenue, status
Output CSV format: category, total_revenue, row_count
Lazy prompt:
Help me think through this business problem.
Better prompt:
I need to decide whether to build our AI evaluation capability
in-house or partner with an existing vendor.
Context:
- We have 2 engineers with ML experience
- Current projects need eval framework within 2 months
- Budget: $50K for tools/services this quarter
- Long-term: expect 5-10 AI projects per year
Help me think through:
1. Build vs. buy tradeoffs specific to our situation
2. What capabilities we'd need in-house either way
3. Risks I might be overlooking
4. What decision criteria I should use
Challenge my assumptions. I'm biased toward building because
I like having control, but that might not be the right call here.
Time spent on prompt: 5 minutes Time saved editing mediocre output: 30 minutes Quality improvement: Significant
But the real value isn’t time savings. It’s forcing yourself to think clearly about what you actually need.
Often, when you sit down to write a detailed prompt, you realize:
That’s valuable. That’s 55 minutes well spent.
Good prompt engineers don’t memorize special techniques. They:
It’s less “prompt hacking” and more “clear thinking.”
The AI doesn’t know your business, your industry, your customers, or your goals. You have to tell it.
If you can’t describe what good output looks like, the AI can’t produce it.
“Write a report” could mean anything from 2 paragraphs to 50 pages. Be specific.
One prompt, one clear output. If you need multiple things, use multiple prompts.
First prompts rarely nail it. Refine based on what you get back.
Once you master basic prompt clarity, you can use advanced techniques:
Chain of Thought:
Before answering, think through:
1. What are the key constraints?
2. What assumptions am I making?
3. What could go wrong?
Then provide your recommendation.
Few-Shot Learning:
Here are 3 examples of good outputs:
[Example 1]
[Example 2]
[Example 3]
Now create one for this new scenario: [details]
Role-Based Prompting:
You are a CFO evaluating AI project ROI. Analyze this proposal with
a focus on financial risk, capital requirements, and payback period.
But these are techniques, not fundamentals. The fundamental is: spend time on the question.
Prompt engineering teaches a valuable skill that applies beyond AI: the ability to clearly articulate what you want.
How many meetings meander because nobody defined the desired outcome?
How many projects fail because requirements were vague?
How many emails get ignored because the ask wasn’t clear?
Prompt engineering is just clear thinking in written form.
And in an age where AI will do increasingly sophisticated tasks, your ability to clearly define the problem becomes your competitive advantage.
Anyone can type “write me a thing.”
The value is in knowing exactly what thing you need and why.
That’s the 55 minutes. That’s the work.
Take something you’d normally ask AI to do. Before you write the prompt, spend 5 minutes answering:
Then write the prompt.
Compare the quality of output to what you’d get from “just ask it something quick.”
The difference is the ROI of thinking before prompting.
Need help implementing AI that actually delivers value? I help companies build AI systems with proper evaluation frameworks and business metric alignment—which starts with asking the right questions. Let’s talk about making your AI projects work.
About the author: [Your name] helps companies implement AI automation that delivers measurable business value, starting with clearly defining what “value” means for their specific context.
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