The 311 Podcast

S2 E5 - From AI Venture Studio to GovLab with Nicole Janssen

• Nicole Janssen • Season 2 • Episode 5

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Nicole Janssen on Bridging AI Innovation and Government Solutions

Nicole is co-CEO of one of Canada's AI powerhouse organizations, AltaML. She's a national business leader and one of our experts in AI. 

Her firm is also behind the Alberta government's innovative AI research lab, operating as an R&D hub for the big, complex problems that the government hopes AI can help address. If you're not from Canada, and maybe even if you are, you may not know that Canada is an AI pioneer. Some of the earliest technology that is powering this new digital economy was conceived of and developed in Canadian universities.

Canada has always had a very strong bond between higher education, the public sector, and industry when it comes to innovation. AI has grown here in this collaborative ecosystem. If you're feeling left behind by the pace of change in artificial intelligence at the moment, then I think you'll find Nicole's overview and perspective helpful. 

She lays out what is happening in the AI industry and where things are going,  and she also has great advice for government organizations just trying to get a toehold.

Resource Links:

Guest

Nicole Janssen, Co-Founder & Co-CEO, AltaML - LinkedIn
AltaML:  AltaML LinkedIn
GovLab

Canadian AI Institutes


AI for Wildfire Prediction

Digital Transformation at Scale (Book) - Amazon.ca | Indigo | Kobo

This is a show about the people that make digital public service work. If you'd like to find out more, visit northern.co/311-podcast/

We're going to keep having conversations like this. If you've got ideas of guests we should speak to, send us an email to the311@northern.co.

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Paul Bellows:

This is the 311 Podcast, and I'm your host, Paul Bellows. This is a show about the people that make digital work for the public service. If you'd like to find out more, visit northern. co. Today, my guest is Nicole Janssen. Nicole is co CEO of one of Canada's AI powerhouse organizations, AltaML. She's a national business leader and one of our experts in AI. Her firm is also behind the Alberta government's innovative AI research lab, operating as an R&D hub for the big, complex problems that the government hopes AI can help address. If you're not from Canada, and maybe even if you are, you may not know that Canada is an AI pioneer. Some of the earliest technology that is powering this new digital economy was conceived of and developed in Canadian universities. Canada has always had a very strong bond between higher education, public sector, and industry when it comes to innovation. AI has grown here in this collaborative ecosystem. If you're feeling left behind by the pace of change in artificial intelligence at the moment, then I think you'll find Nicole's overview and perspective helpful. She lays out what is happening in the AI industry and where things are going, and she also has great advice for government organizations just trying to get a toehold. Here's my conversation with Nicole Janssen. Nicole, first just, first name, last name, title, just a little bit about what you do here at AltaML.

Nicole:

Nicole Janssen co founder and co CEO of AltaML. At AltaML, we have two sides to our business. On one side, we're a venture studio, and we create different ventures, and we are the technical co founder. And so we support the building of those ventures; we now have seven ventures that we have created. On the other side of the business, we do services with both public and private organizations to build custom AI solutions using their data and addressing their specific problems. And support them in that AI journey from wherever they're at. Maybe they're at the education stage, or maybe they're further along and they've got some use cases identified. We can start wherever an organization's at and help get them to value.

Paul Bellows:

So AltaML is, named after the province of Alberta, where we're sitting and speaking today. And we're here in the Canadian context and Canada has a long history with AI. A lot of the technology that is popularized today and is capturing a lot of people's imagination today was imagined and pioneered here in Canada. And you're really at the heart of what's going on in the AI community here in Canada. You're one of our experts in terms of where you sit and what you can see. Maybe a little narrative of Canada's role of, the early days of AI and where some of this technology came from right here in our backyard.

Nicole:

The interesting thing is Canada was investing in AI long before it was sexy. Back in the seventies, we were heavily investing in this technology and have been always since then. We have three of what are called the godfathers of AI that are in Canada. Located currently in Edmonton, Montreal and Toronto. And they associate with the three different AI institutes that we have, Amii, Vector and Mila, that are really at the forefront of research. I have to tell you the level of research and the talent generation we have here is it's, you can't compare it, because we've been investing in it for so long in a very Canadian way, humble, quietly, trucking along, just getting shit done. And the interesting part is most people, even myself, who I was in tech, and 2018, living in a city that has one of the AI institutes. I had no idea that the AI institute existed. I had no idea that we had one of the godfathers of AI at our University. I had no idea that people from around the world fight tooth and nail to have a spot at the University to learn about machine learning and AI. No idea. I think most Canadians are in that same camp. We are at a place where we have this absolutely amazing foundation that today, honestly, if we tried to start today, we could never get here. Because of the dollars it would take to get us here, because of the hype now. The talent that comes out of each of these universities that focuses on AI is incredible. Bar none, that is why we have an AI company here, because it is, while it's one of the hottest job titles to hire, we don't have difficulty hiring, that job title, because of the flow of super incredible talent and the relationship that we have with the different universities across Canada. We do a lot of internships, et cetera, and we have relationships with some of the best profs who will say, I know AltaML offers an exceptional experience for an internship. And so they'll send their best students to us. And we end up having just this incredible pipeline that we actually also leverage for all of our clients because those interns are working on our client projects. And so often they become our client's talent just because we've had such an effective approach to the internship program.

Paul Bellows:

I want to get into the next level of some of the work that you do here at AltaML, but I think it'd be helpful. Large language models and the party trick at scale that has appeared in our culture recently of, robots that will talk to you and answer questions and write essays, et cetera, it's engaging, it's compelling, but it's not the sum of what AI is. AI is a whole net of things. Is it fair to ask, could you give us a short primer on how do you think about the categories of AI and what is possible out there? I think it's helpful to skip past the thing that has everyone's imagination right now and remind folks there's a lot of different things that are AI when we talk about that. It's category.

Nicole:

First I have to give a little background on me. When we launched AltML in 2018, we saw an opportunity in this space. My co founder is quite technical, I am not. So in, in 2018 I knew that AI stood for Artificial Intelligence, and that was the extent of my knowledge. And often, I think that people like to hear me talk about it, because I have to talk about it, not in a technical way, but how I've come to understand it through more of a business lens. I laugh, actually, because I think of it in three categories, but one is called and referred to often as classic AI. That's hilarious to me, because, the capabilities we have within this classic version of AI are incredible and nowhere near adopted to the level that they should be. So don't feel like you're out of date if you're working on a classic project. And things that fall in there, computer vision, natural language processing, those types of things. Then you've got, the newer version that came out in 2021 with ChatGPT, the generative AI. And so you're creating, some form of content, whether it's software code, whether it's written text, videos, that's new. And so you're building something that didn't exist before, whereas the other versions are analyzing the data that exists, learning the rules of how to make best decisions in those areas. But then, now we've got the agentic starting, and this is the new thing around taking that generative piece and adding that next component to it where you're saying this is what we should do and now I'm going to have all of these agents go do it on my behalf. So those are how I bucket the three. As of 2021, everybody wanted to talk about just generative AI. As of now, everybody only wants to talk agentic AI. And what I see is, let's find the tool that solves the problem that you're trying to solve. Let's not ask the question, what can I do with AI? But rather ask the question of what business challenges am I facing? And is one of these types and versions of AI the right tool to get me to where I need to be? Because if you're just looking to build cool AI, you're wasting your time.

Paul Bellows:

Which is why you need a shop like AltaML to help you traverse the broad landscape and start to actually connect technology to business problems, right?

Nicole:

Yes.

Paul Bellows:

Yeah, so so this gets interesting I want to talk now a little bit about the business model of how you work here at AltaML because it's one of my it's one of my favorite things about what you do is how you do it. You're not a traditional software firm. You talked a little bit about you have your venture studio, and then you have your services side where you do things for people. Can you go a little bit, just some examples of on the Venture Studio side, what do those partnerships look like? How do you work alongside your customer or your partner in that context versus the relationship on the services side?

Nicole:

So in the Venture Studio, we are truly co founders with the founders. Sometimes these ideas will come from within our team, and we will put a CEO in place. But in most cases, this is a founder who's come to us with an idea, they don't have the technical background, so they have an exceptional understanding of the industry that they're trying to solve the problem in, they understand that this is a problem we're solving and they have the expertise to run this, they just don't have the technical piece. So we bring in the technical piece and become that co founder at the technical level. We also support, backend things like HR and finance, and those things that often distract a CEO at the onset and suck up a ton of time. We've got a strong team that can support in all of that. So that's not where that founder has to focus, and that, we can really just hone in on what we're trying to build and get it built and get it into market. And then on the services side we work with our clients in a way. We work in a couple ways. So one is we'll work with a private sector organization where we really help them hone in on what are the most impactful use cases in what order, and also keeping in mind that I would venture to say that 70 percent of an AI project is the change management piece. And the technical component is not as large. And we also help organizations see that maybe IT is not where an AI project should sit. It needs to partner with IT, but we actually have to truly understand the business problem, who the end users are, who will be impacted by this, so that it actually gets into operation. So we work that way. We also work, especially in the public sector, in a very collaborative way, where we say, okay, we have a model with the Government of Alberta, for example, it's called GovLab and we work within that. They allow public organizations to collaborate with them. So we've had the City of Edmonton, we've had the City of Calgary all involved in this and the IP that's built can be shared. Why would the City of Calgary pay for something, and then the City of Edmonton pay for something? This is all taxpayer dollars. Why don't we, one of them build it with us, and then the other one can leverage it. And so that's how GovLab has worked, is it's allowed that IP to be shared. And so that we're not all having to pay a bunch of extra money with our tax dollars. Instead, let's do this together.

Paul Bellows:

So the GovLab piece is what I was really excited to get to in our conversation here because I think it's a really innovative model and it's something that I think public sector folks across North America should be paying attention to as an example of how to bring innovation into government. Now innovation always, it's a word that makes anyone who's been in government long enough hears the word and they cringe. But, I like to, I'm a bit of an etymologist, so I like to go back to the root of things. And it really just comes from the root of renewal. So we talk about innovation and say, how else could we do this? And I love that you already called out, AI is so much more often business technology than traditional IT technology. It's really about do we understand the business problem. Can you give me an example of within, I want to talk a little bit about how GovLab is working. It's the next level of understanding there. But maybe the best way to get into that is what kinds of problems are you able to solve at GovLab the government would not be able to solve if they were trying to do this on their own, work independently from an organization with kind of expertise for how to solve these problems.

Nicole:

So a really important aspect of the GovLab model is that this is not traditional procurement. We don't go project by project. Instead, we look at AI as a whole across the government. And we have, we support all the departments in coming up with ideas and use cases, help them suss out what's the feasibility, what's the date, do we have the data, what's the ROI on this, all of the different pieces, and then they pitch it to the governance group, who decides which use cases are going to get funded for the next quarter. And so you're not going to get funded forever and always until it gets into operation, you've been funded to see over the next quarter. Are you worthwhile continuing after that quarter? So that allows us to cut off projects that aren't showing ROI and get rid of them. But it also allows us to hone in on the best projects and really put focus on those. Some of the projects that we've worked on, I have two favourites that I'll share with you. One is sexy, one is absolutely not sexy at all. So I'll start with the sexy one. It's around wildfires. So we can predict now, with an 80 percent accuracy, 24 hours in advance, where a wildfire will be in Alberta. That allows the resources to be effectively stationed where they need to be ready to fight that potential fire. That also allows a significant savings in overtime of, gosh, I don't know, it seems like, the whole province might go up tomorrow, I'm going to get people everywhere. This really allows the duty officers to use their experience combined with the model to say, okay, We're going to focus in these three areas, or wherever it might be, and we're going to keep our resources in those places. Because every time you move resources for firefighting, you're at risk. You're moving helicopters, you're moving all sorts of things. Those are not things that you want to be moving a lot. You want to make sure they're in the right places and ready to fight the fires that come. Now the interesting thing about that model is we've just started the next version of it where we can, we're looking at the fuel grid. How many dead trees, how many diseased trees are in these areas and can we be doing preventative maintenance? So that if we get a wildfire started in that area, it won't be quite as dramatic and damaging as it would have been. We're combining the two to be this, information system for duty officers to be making the best decisions. That's my sexy one. My not so sexy one is around safety codes. So we have a lot of safety codes in the province. Not the best reading unless you're wanting to fall asleep. But there's a lot of questions that come in around safety codes from builders, developers, etc. And they're often waiting on those questions to move forward. And so the wait time was often weeks, sometimes up to a month, waiting to find out the response to these questions because it was a lot of information to go through. And they had to have highly skilled engineers. providing these responses. We've created a model, a generative AI model, that produces that response, that can only pull data from the existing safety code, so you're not getting the, the issues of ChatGPT, where you have no idea of what it's saying is true. Instead, we're saying, you can only provide examples from this content, and it gives the source of this, which safety code it's in, what clause it is, etcetera, drafts the email. We've got a human in the loop because that's an important part of responsible AI to take a look. Is this the right response? And so now response time is two minutes instead of many weeks. And those individuals who are highly trained engineers, are now actually getting to do something in their work that's a little bit higher value and what they were actually trained to do. And so that has been a big win for that department, and we see that opportunity there to take that and expand it across really anywhere that has legislation or regulation that needs a response. Because if you think about how many things and categories we have of that in government, how many places could this be an impactful model? So not so sexy, but super, super impactful.

Paul Bellows:

Useful though.

Nicole:

Yes, very useful.

Paul Bellows:

So useful, I mean it's just, if you think of that as a paradigm of government has data, the data is complex, it needs interpretation, or search, or discovery, and you've got very high paid people, which means you can't have that many of them, because you just can't have everyone sitting benched, and when things get busy, it slows down business, and having done some commercial real estate development in the past, just through offices opening things, every day you're waiting for an answer, costs go up.

Nicole:

Yes.

Paul Bellows:

Openings are delayed, I love this, that's a brilliant example. Anyone who's ever had to get something done alongside government would appreciate government having access to those tools.

Nicole:

Yes.

Paul Bellows:

So I want to just a couple of questions one thing you said that I love it I just want to put some punctuation behind is I love you talked about procurement and how this is a different model of procurement. You talked about the ability when something's not working we can stop, which is remarkable in government procurement. Usually, you do a public RFP, you buy the thing that you need, and then you've bought it, and you have to see it through to the end, right? It's hard to stop once you've bought something and struck a contract. This is a structure in which bad ideas can fail fast. And without public visibility and people getting embarrassed, it's just, we were all sure this was a good idea. We were wrong. We can be wrong without spending a lot of time and money or having to build something that we know people will never use. That's just one of my favorite anecdotes you've pulled out here so far. Tell me a little bit more, the Government of Alberta has said we're going to put some permanent, time based funding in place, to create a certain capacity for AI work, on a quarterly basis. You say, how are we going to spend that capacity? What problems are we going to solve? And you have to continually justify the value that's getting produced, am I understanding that correctly?

Nicole:

Yes, definitely.

Paul Bellows:

And have you seen this happening anywhere else in Canada, the U. S.? Is this a novel thing happening here in Alberta, or is this something more uptake within governments?

Nicole:

I haven't seen it elsewhere, but I can tell you that every government that I talk to is excited and interested about it. We're talking with a bunch of provinces and we have talked to some states in the US as well, where there's, they see how difficult it is to do AI if you're not either buying it off the shelf, which often is not going to get you what you need. Sometimes it is, and then you should just do that. But a lot of times it's not and then they look at their existing procurement. And they think this is impossible. How, because you can't, you don't know what the end state is truly going to look like with AI until you get started. And you actually see is the data going to give us the accuracy we need? And then if it isn't, is there another way to solve this problem with a different approach? You have to have that agility. And so because that hasn't existed really before in traditional procurement, It's just so incredibly challenging. And so I would say that, I would say a year from now, we will have, you'll see a whole bunch more governments starting to deploy this model. Hopefully with us.

Paul Bellows:

They're all going to hear this podcast. Yeah, absolutely. So next question then. So it's stable funding, the government can control the spend, see this is the amount we're willing to commit. We get as much work done within that as we can. We produce models, we produce intellectual property. Who owns that, at the end of the day?

Nicole:

This is another interesting part of the model, is that the government owns the IP. But the Government of Alberta, for them, it's very important that they're a leader in the commercialization of AI because Canada really has struggled in that area. And so they have allowed us to have the ability to license that IP and build products on top of it and commercialize those as long as they get a share in the profits. Their goal is over time, and when you're building products, that doesn't happen overnight. So we don't have any fully in market yet. But once we build those products, and we have a few that we think are just about there, then their AI program will start generating revenue. And their hope is, over time, it becomes self sustaining. And in fact, they're not putting any money into AI, other than the money they're getting back from it. And so that is a really interesting approach to AI that I think is, you can't expect those returns immediately, but with the long term plan, that can be really advantageous. Then the other piece that was really important that I haven't talked about is the talent development, because with with the public sector, let's just be honest, it's not the first stop for your newest grads coming out of tech with the hottest, job title. But a startup or a scale up like us is and so that's why we have no problems attracting team members for our internship. Then they work on these projects in government and realize, wow, there is some incredible problems that government can solve in AI because the data that is here is incredibly valuable and can really solve very complex very interesting and impactful problems. So, every person that the Government of Alberta has hired in this area has now come through GovLab as an intern. And so this has become their talent pipeline. And then whoever doesn't become a Government of Alberta or an AltaML staff person goes into the ecosystem. And is someone who doesn't just have the academic background, but they have real world experience developing these solutions and getting them into operation. Which is valuable to our, to our market.

Paul Bellows:

One of the things that I think, as a practitioner in the digital software space working alongside government, one of the things that I've always found is the most fascinating is just the sheer scale of what government does and does every single day. The scale of the data, the scale of the operations, the scale of the staff, the organization, the budgets. Government is a large, behemoth, which I think often makes it a target for why does it cost so much, that'll be a separate conversation that I don't like to weigh in on, I'm not qualified. But, to say, hey, working with government, you get access to data, but also to do things that impact real people every day, like it's useful what you get to create when you do good things with government. I think that's really exciting as a talent pipeline. And again, some of the talent the government wants to hire, you need to build a relationship because what the market's offering for salaries and compensation, in start ups and, venture backed organizations, versus what government can offer in compensation, they're just different categories. But sometimes you fall in love with the work, and maybe a lower comp, but more stability, more patience of an organization, longer timescales, and the ability to have impact, I think, can be really powerful for young people. who look around and say, I would like to contribute to the betterment of the world rather than just pad my own bank account. What, how can I work? I think that's really exciting.

Nicole:

We're doing a project with Alberta Cancer Care right now that's a part of the GovLab. And we're working on how do we get individuals who've been diagnosed with cancer quicker access to oncologists? How do we remove the wait time and start reducing it? Now, obviously, eventually, we'll roll that work out, hopefully, across the healthcare system, but we're starting with cancer. Now, imagine coming into an internship and getting to work alongside that, thinking, my work is impacting the wait time of somebody with cancer accessing the system. That's incredible.

Paul Bellows:

What

Nicole:

a thing worth getting out of bed for in the morning, I love it. That's brilliant. Something that maybe people don't understand clearly is what is the skill set you're hiring for? You're talking about interns for, and again, for folks who don't really get into the weeds of what AI is and the types of technology, what kinds of skill sets are you able to bring to government?

Paul Bellows:

Are these design people, coders, math people, all of the above? Can you talk a little bit about the kind of talent that would sit within GovLab?

Nicole:

We would have software engineers we would have machine learning engineers but we also have both internships and a lot of roles in the business side. Some of the most valuable people are those who can listen to the business side and translate to the technical side. I refer to them as our translators, but they're, project delivery, they're product owners, they're those individuals who might not be the person doing the code. But they can help dive down into exactly the right problem to start solving that's going to bring value. And then communicate that to the technical folks so they can dive in right away. Because if you just hand technical folks a bunch of data and say, What cool stuff can you build with this? I'm going to be honest, a lot of times there's not a lot of value there. There's a lot of cool things! But it might not be the most valuable thing to the organization. And so those individuals are really important too. The skill set that those individuals need to have is an awareness of the capabilities of the technology and then being able to really dive in and dig deep into understanding a business problem.

Paul Bellows:

I think one of the most important questions that many technologies fail to answer is, what should we do? There's a problem. Do we understand it? Have we analyzed it? Have we gotten to the root of that problem? And then, yeah, we have a lot of technology, but those are just tools. They only ever tell us how, not what and not why. Yeah, the people who can connect, a problem space to a technology space and create a bridge, that's powerful. I'd like to think that's what I'd use. I'm glad to hear that's useful in the world. I want to talk just for one more minute, coming back to where we started. So historically, and there's so many parallels with everything Canada has ever done, which is we're really good at resources. We have all these wonderful resources and we've always relied on other people to commercialize those resources to some degree, that it's the, our great critique is, and then we ship it off and someone else builds the house out of it. Someone else upgrades our energy and does things, and it sounds like there's a bit of that happening with AI. We've pioneered a lot of this. We've invented a lot of it. We've got these deep wells of expertise here. We've gotten used to having these people here. We have these ecosystems that produce more of them. So we have all of this, but we haven't always harnessed it to produce value, wealth, solutions, whatever you wish to have at the end of the day. So as you sit where you sit, working alongside government, In a moment where I think Canada's being challenged to level up in 2025 here and get to the next level. What do you see as some of the problems that we need to solve in the business ecosystem, in the technology ecosystem, in our public service ecosystem? What do you think we need to confront as governments, provincially, municipally, federally, right across Canada right now? How do you, see that?

Nicole:

So I think that the two biggest challenges are both commercialization and adoption. On the commercialization side it becomes tough when your investors and your customers aren't here. It's very easy to raise capital in, in AI in the US. It's not so easy in Canada. It's also, to give perspective, our sales cycle. In Canada is 18 months, in the U. S. it's four. I am a proud Canadian and we will always be, a Canadian company. But, we also, do work in the U.S. because that's a big difference to a business, right? You can't ignore those facts. And how do we as as a country decide who we want to be and what we want to do, really, frankly, for everything, but specifically in AI, what are the layers that we want to be exceptional at? Because there's a massive supply chain to AI. There's the infrastructure, there's the foundational models, there's the application layer, there's all of these different pieces. Are we trying to be excellent at all of them or some of them? What's our brand going to be? And I, I think the federal government is really tired of hearing from me continually saying our brand needs to be that we're the global leader of responsible AI. And if you buy AI in Canada, you know it's ethical and built with responsible AI principles. That's a brand that aligns very well with who we are. But, that's a side. I have to tell everyone I ever talk to about that because someone will start listening. But, so that's the kind of commercialization side. But then the adoption side also relates to it because it's about the customer side. A piece of commercialization. With that 18 month sales cycle versus four months, with AI, the impacts it starts having on an organization are very quick. Once it's in operation, it begins to make an impact immediately. But the life cycle of getting to operation, isn't short with AI, it is an investment of time because you can build this really cool thing that solves this really impactful problem, but there's change that has to happen in order to use this tool. So with the wildfire tool as an example, that is a seasonal tool. We do not need that tool in the winter. So you can only really build it, and perfect it, and test it in a certain part of the year. And that first day it's ready, do you think the duty officer's sure, let the AI model decide where everything's going to be." I can tell you not a chance. And so they needed an entire fire season to work alongside the model where they made all the decisions based on their experience and then looked at what the model said. And yes, that either validated or it didn't, and then they waited to see what did happen 24 hours from then. Was the model right? Or was it wrong? Would I have made the wrong decision had I, followed the model? Or did I make the wrong decision and should have followed the model? And so after the season, then they were ready for the next season to bring it into place. When you're talking about a short season that only comes up once in a year, Think of how long that cycle was to get that to a place where it was adding value. And so we have to start, we have to get started building these things. We can't stop, which a lot of organizations do that we see in Canada, of let's educate our board, let's educate our executive teams, and then we'll think about it. And we'll spend the next 18 months getting our data in order. That's what everybody says. Which I laugh because You, how can you get your data in order, unless you know how you're going to use it? You can start AI projects with a sample of data to see if you're going to get an accuracy that's worthwhile. And then you'll realize, oh I, you know what we need? Is this one other piece of data. And if we had that collected, that would make this model incredibly powerful. You've never been collecting that. We need to start collecting that. And they want to make all these data decisions and make that, have everything perfect, by the way, data is never perfect and then start with AI. You can't do that. That does not work. And I, what I'm pushing, Canada needs to start adopting. Stop waiting. We've always been hesitant to adopt new technology. The impact and the risk of not adopting AI is so massive. In order to compete and stay productive and efficient and, frankly, to make all industries and government across Canada able to compete in this crazy world, we are going to have to get going.

Paul Bellows:

Some of the pioneers of digital government were some of the folks in the UK and they wrote a book out of it called Transformation at Scale about just how they did it. And the core mantra out of that is, you start by starting. So last thought, Nicole, this has just been a wonderful tour of what's happening here in Alberta with the GovLab and what you're doing at AltaML, a bit of the state of AI. So start by starting, I'm in a provincial government leadership, I'm a CIO, I'm a business leader, I'm the federal government, I'm in a large city that needs to tackle AI, what's an action that can be taken?

Nicole:

Call me.

Paul Bellows:

Clearly.

Nicole:

But I think it is just starting to take action towards doing. Ask yourself the question of how are we going to get something embedded, built, whatever it might be, so that it's in the hands of users. Don't focus just on the education component. Think about the end state and start working towards that. Rather than just, first we'll educate everyone. Yes, let's educate everyone all along the way. But we gotta get going on getting something in the hands of users.

Paul Bellows:

It's time for Canada to move fast.

Nicole:

Yes.

Paul Bellows:

Thanks, Nicole. I appreciate the time.

Nicole:

Thank you.

Paul Bellows:

Thanks so much for joining us for this conversation. Nicole is both a nationally and internationally recognized innovator and entrepreneur, but she's also a humble expert, clearly. That's a compelling combination. Some of the key themes she brought forward that I appreciated include Canada has been investing in AI innovation for decades, and now that the rest of the world is catching up, we still have a competitive position from which to thrive. When you build the right structures for bringing problems together with experts and talents, you can achieve great things. There isn't time to waste. Government needs to move quickly to understand this space. And as always, we start by starting. And finally, AI requires talent, patience, and new business models. Alberta's GovLab is one great model, and if you want to learn more about it, Nicole would love to take your call. I hope you enjoyed this conversation with Nicole Jansen. Please do subscribe and follow the many conversations we're going to be releasing throughout the year. I'd like to thank my colleagues who work with me on this podcast. Kathy Watton is our show producer and editor. Frederick Brummer and Ahmed Khalil created our theme music and intro. We're going to keep having conversations like this. Thanks for tuning in. If you've got ideas for guests we should speak to, Send an email to the311 at northern. co. Remember, the public service is about all of us, and when it's done right, digital can be a key ingredient for a better world. This has been the 311 podcast, and I'm your host, Paul Bellows.