How Motor Ace Turned Weeks of Analysis Into Minutes During an AI Workshop

The challenge: knowing AI exists isn't the same as knowing where it can save time

Like many established companies, Motor Ace already had experienced leaders and managers producing the reports, analyses, and decisions needed to run the business.

The problem was not that the work could not be done. It was that some of that work required significant time. Important analyses might involve gathering information, working through spreadsheets, examining patterns, validating results, and preparing insights for management. Some took hours. Others could take days or even weeks.

The practical question for Motor Ace's leadership team was: can AI help us do serious business work faster, without sacrificing the quality of the analysis?

The workshop gave them an opportunity to test that question for themselves.

The workshop: don't just watch AI. Use it on real work.

In August 2026, Jerry Ilao conducted a practical AI workshop in Cebu for Motor Ace Philippines' executive and management team, including its President, CEO, and department heads.

The session covered areas including:

• AI Essentials for Business Leaders
• Practical business applications of AI
• Prompt Engineering, including Jerry Ilao's RTC-FR prompting framework
• Data Analysis with AI
• Applying AI to actual management work

But the workshop was designed to go beyond demonstrations. Participants were encouraged to use AI on real business problems and analyses relevant to their own departments.

That created an important moment. Instead of asking "Can AI really do this?" they could test: "Can AI do work we've already done manually, and how does the result compare?"

The proof happened inside the workshop

1. A two-week analysis was recreated in minutes

One of the most striking examples involved an analysis the team had previously prepared manually. The original work had taken approximately two weeks.

During the workshop, the relevant data was provided to AI and the analysis was performed again. This time, it took only a few minutes.

Speed alone would not have been enough to make the exercise meaningful. The team already had the manually prepared analysis, so they could compare the AI output against work they knew. What surprised them was that the key conclusions were essentially the same.

The important result was therefore not simply "two weeks to minutes." It was:

Two weeks to minutes, with an existing manual analysis available as a benchmark.

That gave the participants something more valuable than a demonstration. They had tested AI against their own work.

2. HR tested AI on attrition analysis

The same principle appeared in Human Resources. Marben Tiu, Head of HR, Learning & OD at Motor Ace Philippines, used AI to analyze employee attrition data.

Attrition analysis can require HR teams to work through employee information, identify patterns, compare groups, and determine possible drivers behind turnover. During the workshop, AI was able to perform the analysis in a few minutes.

Again, the important part was not merely speed. Marben already had an existing analysis to compare it against. The findings produced with AI were consistent with the team's manually prepared results.

For an HR leader, that changes the question from "Can AI analyze HR data?" to: "Which analyses are we still spending hours doing manually that AI can help us examine in minutes?"

3. Finance saw the same time compression

The Finance team experienced a similar result. Pamela Pepito, Accounting Manager of Borromeo Motoring Group, shared in her testimonial that she used AI for a sensitivity-analysis exercise.

A task that could normally require roughly half a day was completed in about five minutes with AI during the workshop.

This does not mean financial judgment becomes unnecessary. It means AI can dramatically compress the mechanical and analytical work required to get to the point where experienced managers can review the numbers, challenge assumptions, and make decisions.

AI made the analysis faster. The manager still provides the judgment.

That distinction is central to responsible AI adoption.

The bigger lesson: speed is only valuable when you can trust the work

It is easy to demonstrate AI producing an impressive-looking report. Motor Ace's experience was more meaningful because participants had something to compare the AI against.

They already knew how long the manual work normally took, what the existing analysis looked like, and what conclusions their own teams had reached. That allowed them to test AI in a much more practical way:

• Manual analysis. Work through the data using the established process.
• AI-assisted analysis. Give AI the relevant information and ask it to examine the same business problem.
• Validation. Compare the AI output against the analysis the team already trusts.

That is when AI stops feeling theoretical. Participants can see, using their own work, where AI saves time and where human review remains important.

Immediate value, not a future promise

Many technology programs talk about benefits that may appear months or years later. Motor Ace saw something different: the value appeared while the workshop was still happening.

Managers did not have to wait for a major AI implementation, a custom-built system, a lengthy transformation program, or a future proof of concept. They were already sitting on business problems, data, reports, and analyses that AI could help them approach differently. The workshop helped them discover those opportunities immediately.

From learning AI to seeing work differently

The bigger outcome was not simply that Motor Ace leaders learned how to use another technology. They began seeing familiar work through a different lens.

A report that traditionally takes two weeks raises a new question: why should it still take two weeks? An HR analysis that requires significant manual effort raises another: what if AI prepares the first analysis and HR spends more time interpreting and acting on it? A Finance analysis that consumes half a day raises another: what higher-value work could the manager do with the time that comes back?

This is where AI training becomes relevant to business transformation. The objective is not simply "use AI." It is: re-examine how work should be done now that AI exists.

What participants said

The exercises were reinforced by public feedback from workshop participants.

"We learned a lot… especially the Golden Formula (RTC-FR), which I believe is a great starting point for me in using AI more effectively."

Her review specifically highlighted AI Essentials for Business Leaders, Prompt Engineering, Data Analysis, and practical applications of AI in business.

"Confidence in using AI has increased. Excited to use what I learned and how it will make my work faster and more efficient."

Other participants described the workshop as informative, practical, and engaging. And one review summarized the experience rather simply:

"Dapat 6 stars."

Key outcomes

• Motor Ace's President, CEO, department heads, and management team applied AI to actual business work during the workshop.
• An analysis that previously took about two weeks was reproduced with AI in minutes, with key conclusions aligning with the manually prepared analysis.
• HR used AI for attrition analysis and obtained results in minutes that were consistent with its existing manual work.
• A Finance sensitivity-analysis exercise demonstrated a reduction from roughly half a day to around five minutes.
• Participants increased their confidence in applying AI to their own roles and departments.
• The exercises demonstrated that AI can compress analytical work while keeping experienced managers responsible for validation and judgment.

What other management teams can learn from Motor Ace

The best way to understand the value of AI is not always to watch another AI demonstration. Sometimes the better test is much simpler: bring work your team already knows.

Take an analysis that consumed hours, days, or weeks. Give AI the same business problem. Then compare.

If the output is poor, you have learned something. If the output is useful but incomplete, you have learned something. And if an analysis that once took two weeks can be produced in minutes with comparable conclusions, you have learned something much more important:

Some of the assumptions behind how your organization currently works may no longer be true.

That is the kind of discovery a practical business AI workshop should create.