How to Validate the ROI of an AI Project in Under Two Weeks

Measuring the return on investment of an artificial intelligence project often seems complex. Many Quebec companies are investing in AI... but struggle to demonstrate concrete gains. Projects take a long time, results are slow to come in, and enthusiasm eventually runs out of steam.However, it is possible to find out quickly, in just a few days, whether an AI use case can really generate a ROI for your organization.

At Globalia, we've developed a simple, pragmatic method that's perfectly suited to local businesses, an AI analysis that measures your ROI in less than 10 days.

Why measuring AI ROI is so difficult (especially in Quebec)

In Quebec, the majority of AI projects never deliver the expected results.
Not because the technology isn't good, but because projects lack structure, don't start with clear objectives, don't define a baseline to measure before/after, and stretch on for months before even proving value.

We see companies testing conversational agents, trying out automation or AI tools... but without a clear framework. The result? A lot of noise, little ROI.

These are not technological failures. They're failures of method.

The "myth" of long-term AI ROI: why wait 6-12 months no longer makes sense

It has often been said that AI requires months of analysis, development and integration before demonstrating its value. This paradigm belongs to the past.

Today, successful companies are those that measure ROI in days, not quarters, from a targeted prototype built on a clear use case and validated by real data. Gone are the days of "black box" AI projects, too long, too expensive and too theoretical.

The truth is that rapid proof of value has become a strategic imperative :

• to reduce risk before committing further ;

• to demonstrate tangible gains to internal teams;

• to unlock funding or management buy-in more quickly;

• to avoid devoting resources to initiatives that will never scale up.

Quebec organizations don't have the luxury of "testing for testing's sake". They need to know quickly whether an AI initiative really creates value, and if so, how much, where, and how fast.

That's exactly the philosophy behind our method: prove quickly, on the ground, where AI generates ROI... before even thinking about full deployment.

Our method measures your AI ROI in less than 2 weeks

At the heart of our approach is a short, pragmatic and measurable sprint that leads companies to a working prototype... and a credibly estimated ROI.

Here's how the method is structured.

1. Alignment with objectives and success indicators (KPIs)

It all starts with clarity.

We identify :

• your operational irritants,
• your business objectives,
• the performance indicators that really count (time, costs, errors, volume...).

This is where we establish the measurable starting point for before-and-after comparisons.

Without a measurable starting point, no ROI is possible.

2. Explore opportunities in your operations

Together with your teams, we explore the areas where AI can :

• automate,
• accelerate,
• support,
• optimize.

We look at your critical processes such as customer service, finance, HR, IT, production, sales, etc. It's a guided, rapid and highly structured exploration.

3. Map each idea in the AI opportunity matrix

Not all AI ideas are created equal.

We position them in our matrix according to :

• potential impact,
• task frequency,
• technical complexity,
• dependencies,
• data risks (Law 25),
• internal adoption.

This helps rule out bad ideas... and brings out the right AI use cases.

4. Prioritize according to ROI, feasibility and momentum

An idea may be brilliant, but impossible to execute right now. Or very feasible, but with little value.

So we prioritize according to:

• Potential ROI,
• technical feasibility,
• data status,
• internal capacity,
• possible speed of execution.

This is where we identify the use case to be prototyped in the AI sprint.

5. Build your AI executive roadmap

At the end of the sprint, you'll have :

• An AI opportunity map,
• Quick wins identified in less than 48 hours
• a quantified ROI estimate,
• your 3 best AI use cases,
• a simple, realistic roadmap aligned with your business priorities.
• Possibility of a functional prototype (depending on the package chosen)

It's a strategic document... but understandable. No unnecessary jargon. Just concrete.

The most important AI indicators for Quebec companies

Measuring ROI is based on simple indicators, adapted to local realities.

The main ones are:

• hours saved per employee,
• reduction in processing time,
• reduction in errors or rework,
• cost per transaction / file,
• speed of service,
• reduced mental workload (important in a context of shortage),
• customer satisfaction.

Quebec companies don't need theoretical models. They need concrete gains.

Concrete example of an AI analysis revealing a clear ROI in 2 weeks

Let's take a typical scenario in a service company.
An employee spends 4 hours a day :

• processing repetitive requests,
• copy/paste data,
• preparing documents.

AI analysis → prototype: → partial automation that reduces the task to 45 minutes.

Measured gain
3h15 saved per day
≈ 16 h per week
≈ 64 h per month

At $40/hr → $2,560/month saved.
ROI: ~4 weeks.

Management understands immediately. And teams naturally adopt-it's simple, useful and fast.

Ongoing ROI, the starting point + Globalia follow-up

Once implemented, we track gains on an ongoing basis:

• volume processed
• actual lead times
• hours saved
• error rate
• operating cost

The complete loop:

1. Starting point
2. Prototype
3. ROI measurement
4. Actual follow-up
5. Adjustments

A simple, yet extraordinarily effective approach.

In short, AI doesn't have to be complex, it has to be measurable.

AI is not a technological gamble, it's a lever for operational efficiency. In less than two weeks, a Quebec company can :

• validate an AI use case,
• obtain a working prototype,
• measure its ROI,
• decide whether or not to go ahead.

It's a realistic, pragmatic approach, perfectly adapted to Quebec companies.

Ready to measure your AI ROI in less than 2 weeks?

Globalia's AI Discovery Session enables you to identify your best opportunities, estimate your ROI and start an AI Sprint that delivers measurable results, quickly and without unnecessary complexity.

FAQ - Measuring the ROI of AI

1. Is it really possible to measure AI ROI in 2 weeks?

Yes. With the right method (baseline + prototype + metrics), it's not only possible, it's essential.

2. How much does an AI Sprint cost?

Around $5,000, depending on the context. This is deliberately affordable to test before investing more.

3. What is an AI baseline?

It's your reference point, current time, costs, errors, volume, etc. Without a baseline, there's no ROI. Without a baseline, no ROI is reliable.

4. What types of task give the best ROI?

Repetitive, heavy, frequent tasks: request processing, data entry, forms, customer service, HR, IT.

5. How many people should be involved?

Very few, an operational manager and one or two key people.

6. What is the typical ROI of a sprint?

From 5 to 40 hours saved per month per employee, depending on the case.

7. What happens after the sprint?

You receive a prototype, the estimated ROI and an executive AI roadmap.

8. Where do I start?

With an AI Discovery Session, the easiest, quickest and most effective way to get started.