Alexandre Guilbault

The best AI model in the world is useless if nobody changes how they work.

I'm often saying the best model in the world is useless if nobody use it or nobody change their process out of that. So sometimes I feel with new technologies coming in, people are so impressed by the technology itself that they forget the principle that has always been there and is still there, that there's no meaningful change without technology, people and process.

Stop running AI proofs of concept. Run proofs of value instead.

POCs are really popular in technology, but a proof of concept finishes with technology concepts. It's not necessarily creating value. So we prefer proof of value. And a proof of value, you might not even have the technology concept completely finalized.

In AI, skip the PhD test. Hire curiosity, because skills expire in six months.

If you don't learn every single day in six months, you're a dinosaur. So even if you hire the best person in the world, but he doesn't have the curiosity, resourcefulness, humility to continue to learn and question others around and read, they will be out of the game within six months and a year.

Four out of five data science projects fail. Finance teams that fund one at a time will quit AI.

But some studies have proven that 80% of the data science project in companies fails to deliver value. So that's four projects out of five that will fail. Finance thinks about funding one project and expects a return on one project. They are more likely not to see the return, and they will stop funding AI.

100,000 employees thinking about AI beat a small team of experts.

We really think that having 100,000 people thinking about the best way to use those technologies is much better than having a small team of experts in the domain trying to figure out how those technologies can really transform business students.

Automating a task saves time. It does not change a business.

they think a bit like if they have a hammer and they were looking for nails to it. And sometimes it's a little bit too siloed or too narrowed when they think about what could be automated in their task. And automation is something AI can do for sure, but automation will just save you some time. It won't drive a big disruption.

Fail in two days, not six months: how TELUS runs risky AI projects.

We're trying to ensure that whatever we tackle, if we are to fail, we prefer to fail within a day or two rather than failing after six months. Because there's a huge opportunity cost.

A $10M AI opportunity with an 80% success rate is really an $8M one.

If we have an opportunity of 10 million, but our success rate is of 80%, then it's no longer 10 million. It's 8 million at this opportunity. So something else might become higher and that changes dynamically through time.

We tried to deliver the AI and the change management ourselves. It created resistance.

And we figured out that there's more resistance to change whenever we come with everything. People like to think of us as an IT support team, a bunch of geeks that are really good with technology, but don't understand the business process.

A TELUS AI leader tells his team: it's good if you fail.

Actually, we're encouraging them to fail. It's good if they fail. They shouldn't be too hard-headed and try to always be successful or else they're not innovating.

Hiring one lone data scientist per department is how you lose them within a year.

So I think really AI talent attracts talent. So centralizing will help any business to build this team. Or else if they hire just one or two, they will probably lose those people after six months, a year, and they will start from scratch.

Next, AI moves from counselor to peer, and everyone may get several.

Agentic AI will be asking a bot to do work. So AI will move from being a counselor to a peer. That will be like everybody will add their peer as an AI, per se. Maybe multiple peers.

You can't innovate with AI if you manage the way you always did.

If we structure too much a team, we're killing innovation. So we need to be flexible. We need to reinvent ourselves in management in order to reinvest ourselves in technology.