S1- All right, everyone, we are live with the last session of the day. It is a conversation I'm having with Tony Pollock, who heads up digital transformation in which is an Ontario appeals tribunal for worker's comp, and he is here to talk about he joined, I guess three years ago and to talk about what he's doing to prepare for the digital transformation that they're going through. I'll hand it over to Tony, who can start by giving us a bit of background about himself.

S2- Hi. Thank you. Mona, it's a pleasure to be here today. A bit about myself. I've had a number of years in in information and technology and in the public sector, believe it or not. I started out working on the help desk and slowly worked my way up through the different different functional areas within it. I spent time in technical support. I spent time as a network and server administrator and moved over to IT procurement, contract management, IT policy standards and strategy and then ended up just prior to moving into leadership, ended up in development and managing a development team. I constantly challenged myself so that I can get a better understanding of the department how it worked as a whole. And that helped propel me into a role of leadership and where I currently am the head of it for, as you mentioned, the Workplace Safety and Insurance Appeals Tribunal. If I look back at my career and especially what I'm doing now, I spend a lot of time on digital transformation modernization building successful teams and using technology to bring value to the organization that I work for.

S3- Thank you.

S1- So. Tell us a bit about what city does what is its role. In in its I'm assuming it's a tribunal.

S2- Yeah. So we we're separate from the workplace Safety Insurance Board where all claims for employment employment claims go to. We are the second and final level of appeal for any of these decisions. So they all these decisions are all this all these cases come to us from the WSIB and we make that final decision on on that specific to that appeal.

S3- And I guess.

S1- We can start with if you can go through and take us through when you arrived at CHC three years ago, what did it look like? What was the tech stack and walk us through from then until now? And what you were trying to figure out about what needed to change there?

S2- Yeah. Thank you. All. It was a bit of a journey, so I'll start even before then. I'll start to explain what the environment was like pre Covid. So the organisation was very paper driven and very manual and technology was used as in a support function for the organisation. A good way to to kind of you know, when I started there and I looked at it, they were a good, comfortable couple steps behind when it came to technology. And then when Covid hit, it was very disruptive as it was for everybody else. And there was a lot of focus in the organisation around business continuity and being able to move the hearings to virtual hearings. And there was a lot of focus spent on that. Unfortunately, the end result was that what we call technical debt grew in that organization to the point that the majority of the hardware and software was past its end of serviceable life and needed to be upgraded and replaced. So is this technical debt was starting to to hold them back because the the hardware and the software was old, there were business continuity problems and issues. We had outages that were affecting that were affecting the you know, the hearings and the business of the organization. And so we came up in 2022, we came with our first strategy. It was really simple. It was you know, three words stabilize, modernize and transform. And the focus really was on stabilize was to get things in order so that the business continuity would improve. The modernize phase was around upgrading all of that end of life hardware and software, and then transform would be looking forward on how we can use technology better within the organization. Yeah. On the on the screen here on the slide is a is a quick one pager of the the strategy that we have that kind of talks about some of the work that we've done or some of the directions that we've taken. Earlier this year we did add two new phases Elevate and Thrive to our strategy because we have been moving forward successfully with the with the first three. But you can see in the in the top left there what some of the things that we kind of focused on when it came to stabilization, you know, we had to rightsize our workforce as well. We had there's a number of vacancies. When I started there, we knew that we were going to modernize our infrastructure. So we didn't want to replace like for like, but we wanted to to look at where did we want to be infrastructure wise in 2025. And let's hire the resources that we need for that. As we make that transition, our data centre was in an inopportune location. It didn't have, you know, what a data centre really needed to do so is a large focus on on moving our data centre out to a true tier three facility. And then of course, replacing all that end of life tech. And then again not replacing like for like but but looking at what's the technology that we're going to need, you know, for our vision for 2025 and start making those changes as we went along we had to optimize some of our procurement practices. We really focused a lot on on business continuity. And most importantly, we had to prepare our organization for change. And I know you and I were were talking earlier about driving here. But if I could use that as an analogy you know, change was very important. You want to, you know, you want to be able to do this, right? Because you're going to introduce introducing a lot of change in an organization in a short period of time. And to me it was like kind of if I could use an analogy of driving here and you're driving on the highway, you've got to be alert with what's happening around you. Sometimes you got to speed up. Sometimes you got to slow down. You got to see what other people are doing. You have to be aware of everything. You know, so we really focused heavily on our change management. We were able to onboard a very effective change leader that helped us to to walk this through. And then we started focusing on the modernization. We were basically a Microsoft shop, so we decided to migrate to Microsoft 365. You know, get access to Azure and all the power tools that are that built in that and start leveraging our cloud computing. A lot of interesting conversations in the organization about what kind of data we wanted to move to the cloud and what kind of data, you know, the highly confidential information that we wanted to retain on premise. So we'd be building or started building out a hybrid cloud environment for the organization? Our application development we do have a small application development unit that focuses on our case management tool, we needed to modernize those application development frameworks and techniques. We also had to create true hybrid. Because we were hybrid, we had to create true hybrid workspaces and that, you know, being able to create not just for hearings but also for meetings where you may have all or some participants in person, all or some participants remote, but they all have to be able to, you know, to function in that environment correctly. And then of course, cybersecurity is when you start making changes like this, you really need to be on point with your cybersecurity tools, techniques and processes. With respect to transformation, we wanted to really focus on maturing our data reporting capabilities. Started investigating you know, artificial intelligence because, you know, when ChatGPT came out it really democratized AI for a lot of our a lot of staff focusing then also on data governance and things like that. And you can see on this on this slide, the really the focus in the first three stages was on technology and it needed to be because we needed to really rightsize that. But as we start moving into the later phases, we're starting to change our focus away from just the technology but now starting to focus on our lines of business. And how can we use technology to make their jobs easier because they're the ones that provide those that customer service to, you know, to our clients. And then in the end they're focusing on on on the thrive thing. And it's really a dual a dual goal here. How do we ensure long term technology sustainability, but also how do we use technology now to help promote the reputation of the organization, not just in a support function now, but to use technology to make our organization better? So this is the you know, we've been working at it for three years. We've added a couple more stages on this, and we continue to push forward with modernizing the tech stack at the organization, but also working very closely with our business units and understanding the goals and priorities of our organization so that we can use technology to help them achieve those goals.

S1- So you were talking about like by the time you get to elevate, you're working with people now, right? So given the fact that we've spent a lot of today talking about the fear that people have of what AI is going to do to their jobs, and here you have public sector employees, right? Dealing with a mandate around really digital transformation and eventually some sort of AI adoption. Right. Can you talk to us about that process and how You and your team is really handling the change management and the emotional aspect of this process.

S2- Thank you. I'll talk a bit about before I answer that question about what we're doing around AI. Go to the next slide. You know, as everybody's aware, when ChatGPT came out, it really made a significant change. And we realized that in our organization and we realized that we needed to get in front of this because there was a lot of risk with these tools as effective and as exciting as they were. There was also a lot of risk involved. So we did a number of things. The first is we we published some internal guidelines on how to safely and effectively use AI tools like ChatGPT. And then we began conversations about what did what did we want our ambition with AI to be? Where did we want to take AI? What what were we comfortable with? And it was that these conversations that we decided that we wanted as an organization to be AI enabled, and that's to use AI tools and technologies to enhance capabilities and do better quality service for our organization. We also released the governance framework published and released it for ourselves. And this is really the guide rails around how we would implement AI in our organization. We we leaned heavily on on the provinces province of Ontario's responsible use guidelines that talk about using AI to benefit people. It must be justifiable, reliable, safe, secure, human rights affirming, transparent and responsible use of AI. And we leverage on that heavily in our framework. It's also tied into our risk management framework and processes within the organization. So we actively manage the risk of the AI solutions that we bring in. And then also we wanted to make sure that it was aligned with our organizational strategic goals. So, you know, we weren't going to do AI for the sake of AI. We were going to focus on those pain points and those priorities within the organization. One of them being that we needed to maintain the independence of Adjudicative decision making. We didn't want the AI to negatively impact that that ability of our of our staff to make those decisions. And then finally, everything needed to be show value. We are a public sector. We got to be careful on how we spend, you know, the funds that are available to us. So with every AI initiative that we consider, we always look at that return on investment and ensure that what we do pursue has the potential to have positive value. Another thing that we did is we created an AI strategy. And this is more of our how to Two on how we were going to implement it. We we we focused on our vision in our AI strategy to be purposeful and deliverable implementations of AI solutions and tools to make the better. And better to us meant more efficient, more accurate, more reliable, better quality services and data driven decision making. Another thing that we and I will get to answering, another thing that we we undertook was we brought a third party in to conduct a, a readiness and AI opportunities assessment of our organization. We wanted them to come in and understand what our business was, what our priorities were, what our pain points were, and then provide us with the their or their advice on what our top 10 AI opportunities would are. But also what is our readiness? How ready are we for AI? And this was key and critical for our organization. Some of the top three readiness challenges that they identified for us. One was data. We had a lot of data unstructured all over the place. And so we needed to get a handle on that. Since then we've instantiated a business intelligence unit and they're looking after data governance and we're starting to move forward on that. Another one was around staff literacy around AI. There's a lot of mixed feelings, a lot of misunderstanding of what AI is. And so we are going to be starting rolling out very soon, an AI literacy program for the organization to help level set our staff understanding of what AI is, what it isn't, what its pros are, and also what its risks are. And then finally the challenge around accepting and this is trying to get to to answer your question is how do we build that acceptance of AI tools and AI solutions in our organization where there are a lot of concerns about it. And so we're really spend a lot of time focusing on on talking to our staff, answering questions, making, you know, opportunities available for them to talk to us, doing a survey right now to get some feedback on what they think about when it comes to the actual opportunities. The top three that were identified for us was to roll out because we are have a Microsoft ecosystem was to roll out Microsoft Copilot, the full product version. We're currently in the process of doing that. We've started with a champions group. We find that from a change management perspective to be a good approach forward, and we're asking our our change champions to champion the solution and to also provide us feedback on what they're finding, the efficiencies or what the challenges are with this. The second one is specific to our developers we'll be rolling out a copilot for GitHub to help them with their development. They've got a lot of work backlog and that should help with with some of that. And our third one that we're starting to pilot next year is going to be around using AI to make our case materials to optimize those case materials for our adjudicators. And then what you see on the on the screen as well is our a maturity roadmap. That's part of our strategy as well. This leverages heavily on a Gartner AI maturity roadmap. So credit where credit is due here. But you can see from this maturity roadmap the kinds of things that we're focusing on. I know the text is a little small so I apologize for that. But you know, there's seven main work streams that we're focusing on between now and through into 2027 here one is around strategy and we've developed our strategy and we've done quite a bit of work there. The value I've talked about you know, value positive. And so this Workstream talks about finding, assessing and validating that value. The organization how ready is the organization for AI from from a resourcing and knowledge perspective. And then people in culture. Right. So this is about acceptance within the organization of AI and AI tools. You know, and it's to me it's it's you know, AI is a lot like what's happened in the past. In the 80s we had microcomputers and there was, you know, they started rolling out to to throughout organizations and there were a lot of concerns about whether or not that will take my job. And we know now in hindsight that that didn't happen, that actually created more opportunity. But as we saw the same thing happen in the in the 90s with the internet and in the 2000 cloud computing smartphones all of these technology changes that made our organization change them in a certain way and made them better. And I envision the same will happen with with AI. It will change our organizations. And, you know, we need to be reticent and we need to be alert to what our staff and what our people are thinking about it and the concerns that they have and dealing with those current concerns. Governance is is another workstream on that. We spent a lot of time around developing governance processes. Engineering is something that our organization is small. We're going to have challenges with with engineering. So I think a lot of our tools will be leveraging tools that exist in the marketplace and then data. And so this is the you know, as we mature as an organization with respect to AI, these are sort of those target points or those action items that we want to focus on over the next few years. I hope I answered your question.

S3- Absolutely.

S3- So.

S1- You know, you talked about ensuring that AI is used to support judicial decision making and not the way to do decision making. There's a lot of chatter online about cases where lawyers are filing materials into court that have hallucinations or judges are not realizing that the materials have hallucinations in them. And they're they're creating decisions using GPT or whatever it is they're using that are completely false. Right. How do you make sure that there are checks and balances in place for the for the decision makers when they're using these AI tools? That these mistakes don't happen.

S2- Thank you. That's a that's a good question. I mean, if you step back and you look at it, you know, the conversations that we've had internally is it really is a bit of a slippery slope. You know, at what point does using a tool or using any technology potentially influence a decision? So yes, if you ask an AI tool to make a decision, that's a very obvious example. But if you look at the other end, if you use AI to do research, how what is the potential that what it returns back on research? If it's not correct, how might that impact the decision. Right. So these are all and and you can kind of as you go through do we use AI to summarize. And what's the impact of that summarization on a potential future case decision. The way to deal with it really is around awareness and education, right? And also when we make our decisions about which tools we use, we always look at that. What is the potential impact of this tool on a on a on a on this on the decision making process and how do we educate and make our adjudicators aware that this risk exists and to be aware of it? So I think to answer your question, a lot of it goes back to awareness and education for our staff and for our adjudicators and the decisions that we make around AI tools, that we make those decisions very consciously understanding what those impacts may be.

S1- And as you know, other other administrative operators get involved in the process. Right. Like you it's it's it's a fully functional institution. It's not just the adjudicators. You've got admin staff and other roles. How do you pick are you going through a process and talk to us? I guess if there is a process on how to train them to work with the output of these tools, like what is the process for that?

S2- So with our first rollout is on Microsoft 365, and we're coupling that with proper adoption training. So what we don't want to do is say here's a new tool. Knock yourself out. We actually want to show our staff how to get value out of that tool. And it's through this process through this adoption training that we have an opportunity then to reinforce what what the benefits are, what the risks are to show them how to use that tool, how to use it effectively in their work. A lot of our focus really is on sort of that back office administrative staff that support the adjudicative process, and how can we use these tools to make our staff more efficient and better at that role? But a lot of it ties into, as I mentioned, adoption training, but also our literacy training and our ongoing education and awareness programs that we have will be putting in place for our staff.

S1- Do you think at some point as the adoption process matures and you've got a good workflow around using those AI tools that that there would be a need for almost like a check of verification process for when somebody is using a tool that the outcome has been verified before it goes into the downstream process.

S2- Yes. I think you're you're referring to, you know, human in the loop kind of thinking and yes, that's what we want to do the the way that we're introducing and that we're explaining these tools to our staff is it's it's not a replacement for what you do, but it's an enhancement. What what the you know, AI and machine learning, it's really good at some things, but it's not so good at other things. And we need to make that distinction and show our staff where's the value, the human value in the process versus the machine value in the process. How do we distinguish between those two? And how do we optimize both. So we're not going to let you know our AI and our machine algorithms run amok. We're going to monitor them. There'll always be a human within the loop on any sort of even simple decision making and back office things. We'll train our staff how to how to do that and how to validate that the information that comes back.

S1- What's the reaction been from staff when you've talked about this, and has it changed over time from when you first brought up the fact that you were going to look at AI tools versus now.

S2- It's it's always a mixed reaction. You know, there's a lot of hype. There's a lot of misinformation about what AI is and what its capabilities are. And people are naturally worried. We hear on the news, you know, certain big tech companies, you know, with their large layoffs and oh yeah, we're going to replace this number of staff with AI. So those concerns always exist. You know, we as an organization have made a conscious decision that we're not going to replace directly replace the staff member with an AI tool, but we're going to use it to enhance enhance their capabilities. And we keep you know, we keep talking to our staff about this, we keep reinforcing it. And I think there's starting to get a little bit of a sense of comfort on what we're doing. We're very transparent about what we do. You know, and we're very transparent with our staff when it comes to staff acceptance, you know, as probably any organization, some staff are very excited about AI and very excited about technology and they can't wait to get their hands, you know, on the latest tools. And yet there's other stuff that are more leery that aren't really you know, that are maybe taking a step back or need a little extra time with any of these tools that we release. It's fully optional for any of our staff member. We're not forcing it on anybody. So for example, copilot, each one of our staff will have an opportunity to say, yes, I want it or no, I don't. And they can change their mind at any point in time and we'll accommodate that. So we'll take it easy. We'll we'll spend time with our staff to show it how these tools work, what those benefits are in their roles and their jobs. And we'll let them decide on what their comfort level is. As we move forward.

S1- So as a leader, I have to ask then if you are giving your staff the the option not just now going forward on whether they want to use a tool that could improve productivity, how do you then measure productivity across multiple people? Right. Because some people are much more productive than others.

S2- Yes. That's the beauty of the of of of the issue here is, is how do you validate the efficiency or the gains that you received from from AI. So I mean, as we roll it out and with our champions group, we are asking them to track usage and to and to evaluate on what they think that efficiency gain is will have, you know, as we proceed through our, you know, our maturity, we'll have constant touch points. We're going to use surveys to elicit you know, information from our staff. We'll have we still need to as you can see in our in our roadmap, find the you know, come up with the process to validate to review, to constantly review and validate the efficiency and the gains from the from the AI tools that we're using. But it is a challenge. It's not easy. You know, but having that repeat those repeated touch points and those repeated evaluations might at least help us identify which of those tools are no longer providing the value that that we had expected. And maybe there's other tools that might be a better option for that.

S1- But what about if you you've done the validation process and I know we're going deeper into this, but I want to go down this rabbit hole for a bit. Right. Pilot is done. 95% of the team agrees that, oh, this is the tool we're going to use in this process. And 5% are like, no, I want to stay with the old process, but there's a productivity difference. How do you handle it as a leader?

S2- More tough questions. I think a lot of it is around education. You know, having those conversations with the staff to understand, you know, what the challenges are. Maybe it's in around tool usage having additional you know, education focused on that. You know, the the There is a strong alignment in the organization to be as good as we can be. And you know we can leverage on that leverage that with our staff. But having that constant evaluation and dealing with those challenges regardless of AI, it happens with any sort of tech that we roll out is understanding what those what those concerns are and what those limitations are and addressing them as best as we can.

S1- So for anybody that's looking to do a digital transformation, not just in public sector, but, you know, across an enterprise, do you have any words of advice on how to get started, what pitfalls to watch for? Maybe cover things that went wrong in the last three years as you were trying to go through this process and what you did to recover from it?

S2- Yeah, I think first and foremost is having a vision that's that's important is being able to as an organization to to think forward into the future a bit and say, where do we want to be with technology the best? We understand it now having that vision and drawing up the plans on how we're going to get from where we are now to where we want to be. Change management is key. Without good change management, you're going to struggle, right? So having a good change leader and having good change management practices are in a lot of cases you know, a good differentiator between whether you're going to be succeed or not. When it comes to things like pitfalls, things are so fluid in the field of technology, things change. You know recently we've had the issue with the tariffs and the impacts within the organization, within, you know, government and around limitations around what you can procure. But from an organization perspective you need to be agile, right? You need to be able to twist quickly. And I think in our strategy a good example was, you know, in 2023 when AI came out. So when we first had our strategy we really weren't thinking about AI. But then AI came out and we had to pivot quickly, you know, how do we adapt it into our current strategy? How do we adapt it and adapt the strategy to be able to accommodate that? So the strategy you need to have needs to be also adaptive and to be able to react to that. And then I think finally having the right resources and this is really a you know, a big challenge is how do you get the right resources in place to be able to deliver and execute on the plans that you've come up with?

S1- Very interesting because from a from a business framework perspective, it's goal right people in the right seats and then you start building out what you want Any lessons you've learned along the way. Things that people can watch out for sort of in the closing remarks about okay, this is where we stumbled. Watch out for this.

S2- I think I think if I go back to my earlier analogy of driving on a highway you really need to be aware of what's happening within the organization and what those concerns are. And most IT departments have a lot of tools they can leverage. I mean, in our case, we could see the tickets climbing, right? We've we've instantiated a change and suddenly we notice an increase. And that to us is a trigger. It's a flag that says something's not going as well as we had hoped. Sometimes it's just a a natural reaction to the change. But we also always have to be careful that you're aware that what's happening on the ground is different than maybe what's happening in the strategy in the boardroom. So being fully aware of that and being able to react to that again when it comes to, you know, I say again, good change management will get you a really, really long. You'll do a really good job if you have good change management practices in place. Okay.

S1- Any last minute advice before we wrap up?

S2- I don't know if any any advice, but, you know, change technology change is the norm now. I know we're talking, you know, about AI now, but eventually AI will become mainstream and there'll be something new that comes up. I if I had a magic black ball, I might be able to guess at it. But, you know, it will change. Technology always changes. And you know, as a as a technology leader and as an organization, you need to be ready and need to be nimble, nimble and agile and ready to, you know, to pivot. Be careful that you're not jumping in both feet into the deep end right away. You need to evaluate, you know, what's the value of this new technology and what's the benefit for the organization. And be really specific and directed on on how you're going to implement that in a way that's going to enhance the organization and not deter it in some way. Great.

S1- Thank you so much for talking to us today. Really, really appreciate it. This brings us to the end of the program and the last of our webinars. So we will be signing off now unless anybody in the audience has questions at this point. But thank you very much for quite a few of you who've stayed with us through the entire day. And it was a long day. So thank you very much. And we'll be in touch with recordings of all of the sessions early next week. Thank you very much, everyone. Bye. Welcome.