Adam Asquini is a Director of Information Management & Data Analytics at KPMG in Edmonton. He’s accountable for leading data and advanced analytics projects for KPMG’s clients within the prairies. Adam is obsessed with constructing and developing high-performing teams to deliver the most effective possible outcomes for clients and to enable an attractive work experience for his teams. He has previously worked at AltaML because the Vice-President of Customer Solutions, the Government of Alberta as a Program Manager and within the Canadian Armed Forces as a Signal Officer. Having followed a non-traditional profession path into AI, Adam is an enormous believer in harnessing the variety and experience of cross-functional teams and likewise believes that anyone can join the growing AI community.

We sat down for our interview with Adam on the annual 2023 Upper Certain conference on AI that’s held in Edmonton, AB and hosted by Amii (Alberta Machine Intelligence Institute).

You could have a non-traditional profession path, could just discuss the way you got into AI?

I began my profession within the Canadian Armed Forces as a signals officer, signals officers are accountable for IT telecommunication systems that help people communicate. So really, quite a lot of radio satellites. There was some data in there, but it surely was quite a lot of the core infrastructure technologies that we were accountable for, that originally began me into technology. I’d studied chemical engineering in university of all things, right off the beginning driven by my very own curiosity and desire to learn. It began there and diving into technology upskilling and self-development were really vital for me.

After 14 years within the military doing a lot of different signals jobs, all the things from working on a base and supporting IT and telecommunication services out in the sphere, establishing headquarters and communicating frontline units, supporting domestic operations like forest fires and floods, I moved on to the Alberta Provincial government. I used to be in program management some cross-government technology initiatives. On the time, the federal government was centralizing IT, we were working with various government ministries to bring their services together and consolidate things, I did quite a lot of work there in addition to in investment management. And really, in doing that work, I began to see among the organizations leveraging data and analytics.

It really piqued my curiosity and all the time being curious and hungry to learn, I began actually pursuing a few of that through either getting involved in some projects there or simply doing self-study, things like Coursera or other training tools to learn just a little bit more. I did quite a lot of reading, researched among the vendors and the platforms that were providing these tools. I actually became fascinated by data and analytics and thru my very own natural curiosity and desire to learn more, began to get increasingly heavily involved on this over time.

Outside of Coursera, are there specific podcasts or books that you just would recommend?

I follow quite a lot of different followers on LinkedIn, but a number of that jump out to mind similar to Emerj. Dan Faggella is the person behind it. He brings quite a lot of thought leadership to it. I definitely follow among the mainstream ones like HBR and Forbes. A contact of mine named Andreas Welch who works at SAP, he releases quite a lot of content around AI and AI adoption, so I have been following him. I believe so far as podcasts, there’s been a number of that I’ve listened to after which books as well. A very good book that I’ve recently read known as Infonomics by Doug Laney. He’s former Gartner and MIT, and it’s a very good book to elucidate a monetization framework for data. I try to simply immerse myself into as many things as possible, plus plug into project work to learn more.

How has your military experience benefited you in your current role?

In a pair of the way. I believe among the awesome core skill sets that I learned through my military profession, a really structured approach to planning, which is actually good. Time management and prioritization. In a military environment, it really forces you to learn what’s an important thing and to work at a certain pace, assessing trade-offs and understanding how one can best provide you with a plan of action that is workable and that is going to get you moving forward. I find in a fast-paced technology landscape like AI where things are only moving so fast, having the ability to process quite a lot of information and have a structured approach to have the ability to grasp what’s vital, what’s not vital, where do you would like to focus has been an excellent skillset.

The opposite big one is around leadership and teamwork. You are working with a big organization. Out in the sphere, teams are being organized and reorganized on a regular basis to get the most effective group together to finish a mission, having really strong interpersonal skills, leadership skills, communication skills are all skills which might be really harped on within the training within the military, I believe they’ve really leveraged a few of those as well.

You were vice chairman of customer solutions at AltaML for over two years, what’s AltaML and what were some interesting projects you worked on?

AltaML is an applied artificial intelligence machine learning company. It’s based out of Alberta, headquarters is in Edmonton, a big office in Calgary and likewise one in Toronto. What they do is that they work with other businesses to develop software solutions and products which have AI at their core, it is a business to business. The a part of the organization I worked in was the services side, we might work with oil and gas company financial institutions. We worked across quite a lot of different industry verticals. I worked with them to define business problems that were relevant and will make an impact to be solved with AI, after which worked them through the means of bringing their data together, constructing AI models, deploying them and dealing through the change management side as well in order that they may very well be operationalized and used, really helping those organizations solve problems through constructing applied AI solutions.

The role was vice chairman of customer solutions. After I began, I used to be in a project manager role leading a number of AI engagements, I then moved up over time, and the vice chairman of customer solutions role was accountable for the delivery function, resource management for projects and energetic account management, quite a lot of the client facing elements of that work fell into my team.

So far as projects are concerned, there was so much, I might say in a technique, shape or form, as either a hands-on project manager, a coach or a high quality assurance resource, dozens of AI projects that I might’ve worked on over the 2 and a half years, one among my favorite ones was a wildfire project. I worked with the governor of Alberta. They were struggling on days where there is a moderate fire risk, to grasp whether a fireplace is more likely to occur in a specific area. Once they were uncertain, their scheduling practice was to schedule whatever resources that they had available, and that might include contracting additional resources, heavy equipment like bulldozers or airplanes, helicopters, which is after all expensive.

The aim of the AI project was to predict for a given region what the probability of a hearth could be for that region for the following day, to assist them make decisions across the optimal resource allocation for a process they called pre-suppression, which is actually the proactive scheduling and allocation of resources.

It was really cool to have the ability to see that in certain scenarios, you would draw down resources or simply reduce the extent or focus them at certain times of the day. That may save quite a lot of money but not likely introduce quite a lot of material risk of missing a fireplace, hundreds of thousands of dollars of savings potential. That work has still carried on. Even today, they’re now extending the time window out just a little bit, making the zones smaller and more granular to higher optimize resources. But how the hearth season we have had to date here in Alberta, any intelligence you could provide upfront about where the risks are and having the ability to optimize resources or at the least reallocate resources to the precise places is actually impactful work, it was really enjoyable.

I also did some work in claims processing as well. As an insurance provider would get 1000’s of claims coming in, which of them may very well be robotically approved, which of them would require a human review, and even which team a claims ought to be forwarded to for getting the precise level of review. That variety of work’s also really vital and might save organizations quite a lot of effort and quite a lot of money in how they do their business,

You’re currently the director of data management and data analytics at KPMG. What does this role entail exactly?

I work with businesses to guide them through the journey of solving these problems through, on this case, a broader set of information and analytics capabilities. We work all the things from data strategy up front and helping organizations organize data from disparate systems, bringing it together, reporting and analytics in addition to AI and ML. It’s kind of of a broader role than my previous one, but that is also really exciting to me. It fuels my passion for learning and self-development.

As a director, I’m often working with senior leaders on the client side to assist advise them through the journey, get them a way of what it will take, what those projects appear to be, how they will prepare. An enormous deal with adoption as well, especially with the advanced analytics systems which might be recent and that sometimes include a negative connotation from a workforce, so really working with them on how one can best implement these solutions in addition to things just like the processes they will need, the structures they will need. That is an enormous a part of the role. Internally, leading the engagement and leading the project teams, helping get the precise priorities for the project team and guide the work in addition to synchronization of various teams which might be working on these projects.

In a recent interview with the Calgary Herald, you spoke about how there’s been a good amount of AI adoption in Alberta. In what industries are you seeing this most in?

I’ve seen adoption across a lot of different industries in Alberta. Definitely, energy has quite a lot of it, so I’ve seen use cases where organizations are using artificial intelligence to assist optimize maintenance and safety inspections in pipelines, where should or could digs occur? Because digs are very expensive to do if there is a suspected leak. I’ve also seen so much in supply chain. As large organizations do mergers and acquisitions, their data’s all over. Sometimes, they really struggle with finding items of their material masters, so having the ability to use these language models that we’re seeing emerge without delay to arrange data, structure it in a way that it may possibly be analyzed. We have seen significant work in consolidating supply contracts by just having the ability to higher search and query and find information. That one can span across multiple industries, not necessarily just in energy but I’m seeing it applied there.

Safety is an enormous one, so using either image processing and even the language models to search out probably the most relevant variety of safety temporary or safety inspection that ought to be occurring at a specific site. In financial services, quite a lot of work on personalizing the experience for a banking customer, providing the most effective possible advice and finding tailored solutions for those that are in several financial scenarios is a very vital focus and we have seen quite a lot of work there. After which insurance. As I discussed before, quite a lot of this triaging and claims processing. Yet another I’d perhaps suggest too is forestry and natural resources land management, seeing a little bit of an uptake in using satellite imagery to detect changes to land, having the ability to manage agreements on land and using those image processing techniques to have the ability to discover things that ought to or should not be there, or things which have modified over time.

It’s really exciting and we see different organizations are at different stages of their maturity. Some are only either starting or experimenting, others are further along and fully adopting, but most organizations are recognizing that in the event that they don’t start or if they are not moving forward on this, they will be left behind and that is going to create quite a competitive drawback for them, so the interest is actually high across the board. Obviously, with generative AI capabilities it’s generating quite a lot of interest as well.

Talking about generative AI, how do you see this technology transforming the long run?

I’m very excited for it. I see the potential. I also think it is vital to have the precise controls in place for generative AI, I actually do think there’s quite a lot of use cases there where this may very well be applied to make huge productivity gains or efficiency gains for business. A few of that like within the use case I just mentioned with the availability chain, that was leveraging a few of those techniques even before ChatGPT was publicly announced. So far as where I see this going, one among the opposite cool trends I’m seeing is increasingly of this technology is being embedded into mainstream business applications without delay. Microsoft’s announced their Copilot tool that is going to be integrated along with your Microsoft Office apps, I saw in a few of their material things like writing a briefing note and just prompting the word processor with, “Are you able to make this paragraph shorter?” And it just does it for you.

As those generative AI technologies get embedded straight into mainstream business applications, it will force businesses to take into consideration how and once they adopt them, how they control them, how they will monitor for quality assurance on the products that they are producing. When it’s an entire standalone separate capability, it’s just a little bit easier to slow play it or ignore it, but seeing this being embedded into mainstream business applications and platforms is actually going to drive that discussion forward.

I’m also hoping that with this and the emphasis without delay on the responsible use of this technology, that it does help organizations put an emphasis on responsible AI, putting the precise processes, the precise governance in place to essentially make sure that that their AI solutions are being effectively built, the danger is being managed throughout all the life cycle, that there is follow-on checks and that you already know, can trust the outputs of them. I’m hoping that this hype without delay on the generative AI actually continues to drive that discussion with those capabilities forward.

Are you able to discuss how responsible AI and reducing AI bias is actually vital to you.

Absolutely. I believe it needs to be for a lot of reasons. Many of the those that are constructing these systems can have pride within the work that they are doing they usually don’t need their systems to have that, so there’s going to be an internal have to have this to maintain your workforce engaged and glad and guarded. Legally, there’s examples on the market where organizations have faced legal challenges or regulatory challenges for the bias of their AI. There is a classic case study of a corporation that was using AI in hiring. The information set was over overly biased towards men over women in order that their AI discriminated against women.

That was an AI tool by Amazon.

Things like which have already occurred and have the potential to maintain occurring should you do not have the precise controls in place, having an actual deal with that is going to be critical for many organizations. After which reputational risk after all for organizations. For those who get that unsuitable, that would have an enormous, huge impact on your corporation.

You are also an enormous believer in harnessing the variety and experience of cross-functional teams. Why is diversity so vital in your view?

Straight away, the varieties of problems which might be being solved with AI are so complex, from a business perspective, from the info that is that underlies behind it, nobody person or one role can solve all of those problems by themselves. Having an excellent cross-functional team with different perspectives and skill sets is actually vital, to have the ability to have those that are strong in a single area really harnessing their strength. So far as the variety piece is available in, One other really big driver of getting a various team is that normally, the top user of those systems might be a various group of individuals, and never having those perspectives brought into your team if you’re constructing them really sets you up for making mistakes down the road or missing things, Things that I won’t take into consideration that another person may and they convey that perspective forward. It’s easier to unravel problems and adjust for that in the event cycle than it’s after a release.

I also just consider strongly that having a special perspective is where you get the most effective dialogue, you get really good questions coming from those that are seeing something from a special lens. It forces conversation about how one can best approach something. It makes you switch over a few of those stones you may not have turned over if that person wasn’t there, having a various group of individuals an issue really lets you get the most effective possible consequence and best solution.

What do you think that might be the following big breakthrough in AI?

In that generative AI lens, I believe as we’ll see more of that technology being embedded into mainstream applications, and that is already starting, That is really going to be huge for the adoption of the technology since it’ll be right there on the systems that individuals are already using. It can be really, really vital, and which may open the door to among the other use cases as people change into more accustomed to what it may possibly do, what its limitations are, how it may possibly be optimally used, and which may just trigger people’s considering and, okay, now I even have a greater sense of the variety of problems this can solve. We’ve got this problem. This could be really cool to unravel and should open up some recent doors.

I’m also hoping that that regulatory policy is a breakthrough that is available in the near future as well. I do know that there is quite a lot of movement on the law making level and regulatory level, but what I’m hoping is that individual businesses also work out for themselves or get advice on how they must be fascinated with it and what are among the internal controls that they ought to be putting in now.

Laws and regulations take a protracted time. Businesses can drive quite a lot of change by taking up a few of those controls internally and considering through that. There’s precedent for this, obviously with audits and things like that, something that KPMG is actually strong in. But fascinated with what those controls may be, how we would control it, how will we test outputs? How will we make sure that that we’re reducing hallucinations? What are among the additional steps after the model has produced its output that we are able to take to reduce any potential harm or risk? Those are the precise varieties of questions and I’m hoping among the hype, again, without delay is a breakthrough on how we take into consideration this and the way we construct the precise structures, processes, and teams on the responsible AI side.