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 very best 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 range and experience of cross-functional teams and in addition 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 will 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, a whole lot of radio satellites. There was some data in there, nevertheless it was a whole 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 essential for me.

After 14 years within the military doing quite a few different signals jobs, every part from working on a base and supporting IT and telecommunication services out in the sector, organising 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 taking a look at 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 a whole 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 at all times 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 slightly bit more. I did a whole lot of reading, researched among the vendors and the platforms that were providing these tools. I actually became excited 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 simply would recommend?

I follow a whole 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 a whole lot of thought leadership to it. I actually follow among the mainstream ones like HBR and Forbes. A contact of mine named Andreas Welch who works at SAP, he releases a whole lot of content around AI and AI adoption, so I have been following him. I feel 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 clarify a monetization framework for data. I try to only 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 how. I feel 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 learn how to 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 a whole lot of information and have a structured approach to have the ability to know what’s essential, what’s not essential, where do you desire to focus has been a superb skillset.

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

You were vp 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 in addition 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 a whole 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 technique of bringing their data together, constructing AI models, deploying them and dealing through the change management side as well in order that they might be operationalized and used, really helping those organizations solve problems through constructing applied AI solutions.

The role was vp 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 vp of customer solutions role was accountable for the delivery function, resource management for projects and energetic account management, a whole lot of the client facing features of that work fell into my team.

So far as projects are concerned, there was quite a bit, I might say in a method, 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 in all 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 know whether a hearth is more likely to occur in a specific area. After they were uncertain, their scheduling practice was to schedule whatever resources they’d 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 can draw down resources or simply reduce the extent or focus them at certain times of the day. That may save a whole lot of money but probably not introduce a whole lot of material risk of missing a hearth, thousands and thousands of dollars of savings potential. That work has still carried on. Even today, they’re now taking a look at extending the time window out slightly bit, making the zones smaller and more granular to higher optimize resources. But taking a look at how the fireplace season we have had thus far here in Alberta, any intelligence which you can provide upfront about where the risks are and having the ability to optimize resources or at the least reallocate resources to the correct 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 might be mechanically approved, which of them would require a human review, and even which team a claims needs to be forwarded to for getting the correct level of review. That variety of work’s also really essential and might save organizations a whole lot of effort and a whole lot of money in how they do their business,

You’re currently the director of knowledge 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 every part 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 is a bit 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 is going to take, what those projects appear like, how they’ll prepare. An enormous concentrate on adoption as well, especially with the advanced analytics systems which can be recent and that sometimes include a negative connotation from a workforce, so really working with them on learn how to best implement these solutions in addition to things just like the processes they are going to need, the structures they are going to need. That is an enormous a part of the role. Internally, leading the engagement and leading the project teams, helping get the correct priorities for the project team and guide the work in addition to synchronization of various teams which can 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 quite a few different industries in Alberta. Actually, energy has a whole 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 quite a bit in supply chain. As large organizations do mergers and acquisitions, their data’s in all places. 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 right away to prepare data, structure it in a way that it will 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 seek out probably the most relevant variety of safety transient or safety inspection that needs to be occurring at a specific site. In financial services, a whole lot of work on personalizing the experience for a banking customer, providing the very best possible advice and finding tailored solutions for those who are in numerous financial scenarios is a very essential focus and we have seen a whole lot of work there. After which insurance. As I discussed before, a whole lot of this triaging and claims processing. Yet one more 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 are going to 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 a whole lot of interest as well.

Talking about generative AI, how do you see this technology transforming the longer term?

I’m very excited for it. I see the potential. I also think it is important to have the correct controls in place for generative AI, I actually do think there’s a whole lot of use cases there where this might 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 provision chain, that was leveraging a few of those techniques even before ChatGPT was publicly announced. So far as where I see this going, one in all the opposite cool trends I’m seeing is increasingly of this technology is being embedded into mainstream business applications right away. 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 is going to force businesses to take into consideration how and after they adopt them, how they control them, how they’ll monitor for quality assurance on the products that they are producing. When it’s an entire standalone separate capability, it’s slightly 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 right away on the responsible use of this technology, that it does help organizations put an emphasis on responsible AI, putting the correct processes, the correct governance in place to actually be certain that their AI solutions are being effectively built, the chance is being managed throughout all the life cycle, that there is follow-on checks and that you realize, can trust the outputs of them. I’m hoping that this hype right away 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 essential to you.

Absolutely. I feel it needs to be for quite a few reasons. A lot of the those who are constructing these systems could have pride within the work that they are doing and so they don’t need their systems to have that, so there’s going to be an internal must have this to maintain your workforce engaged and joyful 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 in case you do not have the correct controls in place, having an actual concentrate on that is going to be critical for many organizations. After which reputational risk after all for organizations. In case you get that incorrect, that would have an enormous, huge impact on your online business.

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

At once, the forms of problems which can 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 a superb cross-functional team with different perspectives and skill sets is actually essential, to have the ability to have those who are strong in a single area really harnessing their strength. So far as the range piece is available in, One other really big driver of getting a various team is that most often, the tip user of those systems will probably be a various group of individuals, and never having those perspectives brought into your team whenever you’re constructing them really sets you up for making mistakes down the road or missing things, Things that I may not take into consideration that another person may and they bring about 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 unique perspective is where you get the very best dialogue, you get really good questions coming from those who are seeing something from a unique lens. It forces conversation about learn how to best approach something. It makes you switch over a few of those stones you would possibly not have turned over if that person wasn’t there, having a various group of individuals taking a look at an issue really allows you to get the very best possible end result and best solution.

What do you’re thinking that will probably be the following big breakthrough in AI?

In that generative AI lens, I feel 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 should be really, really essential, and which may open the door to among the other use cases as people grow to be more aware of what it will possibly do, what its limitations are, how it will 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 it will solve. We’ve got this problem. This may be really cool to unravel and will 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 a whole lot of movement on the law making level and regulatory level, but what I’m hoping is that individual businesses also determine for themselves or get advice on how they must be excited about it and what are among the internal controls that they needs to be setting up now.

Laws and regulations take a protracted time. Businesses can drive a whole lot of change by taking over a few of those controls internally and considering through that. There may be precedent for this, obviously with audits and things like that, something that KPMG is actually strong in. But excited about what those controls is perhaps, how we’d control it, how will we test outputs? How will we be certain 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 correct forms of questions and I’m hoping among the hype, again, right away is a breakthrough on how we take into consideration this and the way we construct the correct structures, processes, and teams on the responsible AI side.