How to choose your first corporate AI project.

(and why most companies get it wrong the first time)

Artificial intelligence fascinates as much as it worries. It promises spectacular gains, intelligent automation and more informed decisions. Yet, in reality, a large proportion of AI initiatives fail to deliver the expected results.

The problem is almost never technological. It's strategic.

Many companies embark on AI as they would launch a new trend, with enthusiasm, but without any real focus. They seek to impress, to "do AI", rather than to solve a precise problem. The first project then becomes a symbol... rather than a lever of value.

And that's where it all comes in.

The first AI project is not a demonstration project

When an organization decides to invest in artificial intelligence, it's tempting to aim for the big picture. One imagines complete platforms, sophisticated predictive systems, complex automation. On paper, it all seems logical. In reality, these projects quickly become cumbersome, costly and difficult to deliver.

The mission of an initial AI project is not to transform the company. It has a much simpler, but much more strategic mission: to demonstrate that AI can create real value, here and now.

This is a fundamental difference. A project that seeks to prove a vision often fails to produce measurable results. A project that seeks to solve a concrete problem creates trust, buy-in and a solid foundation for what's to come.

Start with reality, not technology

Successful AI projects all have one thing in common: they start from the company's operational reality. Not a tool. Not an algorithm. From a well-known irritant.

It could be time wasted on repetitive tasks, decisions made with too little information, missed sales due to lack of personalization, or inefficient user paths. Where there is clear friction, there is often a relevant AI opportunity.

Conversely, when a project starts with the question "what could we do with AI?", it almost always ends up missing its target.

AI must serve performance, not complexity

In e-commerce, for example, artificial intelligence is often associated with spectacular experiences. Yet the most effective projects are often the most discreet. A better-calibrated recommendation engine, a better understanding of abandoned shopping baskets or more responsive customer service can have a direct impact on revenues without weighing down the technological ecosystem.

In marketing, the same principle applies. AI doesn't have to produce stand-alone campaigns to be useful. When it helps to better segment, prioritize efforts or predict performance, it becomes an extremely powerful decision-making tool.

On the website and app side, AI works best when it enhances the experience without making it more complex. Relevant personalization or better reading of user behavior can transform engagement, without the user being aware of the technology behind it.

The ROI question arrives sooner than you think

One of the most common pitfalls is to put off the question of ROI until later. We tell ourselves that AI is a long-term investment, that we must first "test", "learn", "explore".

This is true... up to a point.

A first AI project must be measurable, even if imperfectly. Without clear indicators, it becomes impossible to know whether the initiative is working, to improve it or to justify it to decision-makers. AI is no exception to the fundamental rules of management: what can't be measured can't be controlled.

Why so many first projects fail

AI failures are rarely spectacular. They are silent. A project that drags on. A tool that isn't used. Results too vague to be exploitable. And, little by little, AI becomes "something we've tried".

In most cases, failure stems from a mismatch between ambition and reality. Too broad, too abstract, too disconnected from operations. Conversely, successful projects are often modest in scope, but very clear in their objectives.

The importance of an external viewpoint

Choosing your first IA project requires some distance. Internally, it is sometimes difficult to distinguish what is really a priority from what is merely visible or politically attractive.

External support can often help you ask the right questions, refocus decisions on value and avoid classic pitfalls. Not to do things for the teams, but to secure structuring choices from the outset.

In conclusion

Artificial intelligence is neither a passing fad nor a miracle solution. It is a powerful lever, provided it is used with discernment.

Your first AI project need not be ambitious.

• It has to be relevant.
• It must be measurable.
• And above all, it must serve a clear purpose.

The organizations that succeed in AI are not those that move the fastest.
They're the ones who start in the right place.

FAQ

What is a successful first AI project for a Canadian company?

A successful first AI project is simple but strategic. It must solve a concrete problem, be measurable and have a clear impact on operations or revenues. The size of the project is less important than its ability to deliver value quickly.

Why do so many AI projects fail in Quebec and Canada?

The majority fail not because of the technology, but because of poor scoping. Organizations often choose a project that is too ambitious or spectacular, with insufficient data and no clear KPIs. Starting small and strategic is the key to success.

What types of AI projects are suitable for Canadian SMEs?

For SMEs in Quebec and Canada, the most effective AI projects are often linked to :

• Improving the customer experience (e.g. recommendations or chatbots)
• Optimizing repetitive internal processes
• Data-driven decision-making (e.g. customer segmentation, forecasting)

These projects are concrete, measurable and directly useful.

Is AI accessible to Canadian companies without in-house data scientists?

Yes. Many companies in Canada succeed with their first AI project with external support, which helps prioritize projects, structure data and measure results. AI isn't just for big technical teams.

How do you measure the success of an AI project in Quebec or Canada?

Success is measured by clear indicators linked to business objectives: increased sales, reduced processing time, improved customer engagement, or better retention. Without KPIs defined from the outset, it's impossible to assess the real impact.

Should you embark on an AI project if your company is in the early stages of its digital transformation?

Yes, but with caution. Even in a context of limited digital transformation, it is possible to start with a targeted, quick-to-test project that creates tangible value and serves as a foundation for future AI adoption.