Welcome to The Innovation Today podcast where we speak with today's technology leaders about how they're innovating to stay ahead of changing industry Dynamics and reaching new levels of productivity and automation brought to you by service now your partner in digital transformation thank you so much for joining us today for another episode of The Innovation Today podcast I'm your host Jim vanover field Innovation officer at service now today we're excited to welcome Dan prior thanks for having me you are the partner Risk Technology at ey Dan why don't we start with you telling us a little bit about you and your role Shar thank thanks Jim I've uh I've been with a firm about 17 years now I've been fortunate in my career to have a a very diverse experience in terms of the sectors I've served as well as the risk disciplines that that I've had an opportunity to support across our clients and more importantly um and and why we're here today I'm very much focused on the risk technology market so helping clients optimize automate their governance risk management and compliance programs and that that's across a number number of areas that that they're facing challenges and and trying to navigate as they grow their grow their business innovate and and and look for how they're navigating these these uncertain and disruptive times yeah so we had the pleasure of talking a little bit beforehand and uh you shared that when it comes to operational resilience people get a little confused because they just think of it as the how do I recover operations and it becomes this this continuity conversation can you what what are your thoughts uh on that yeah it's a great question when the term operational resiliency is is used there there's two conversations that we see at clients one is much more focused on continuity Disaster Recovery crisis management and that that's certainly a a focus area the other aspect of it is is really resiliency in the in the broadest sense Enterprise resiliency and that gets at how are organizations foreseeing what could happen or things that they need to navigate and and really the agility of the organization and if you look externally you know there there's a lot of uh external factors and and disruption clients are dealing with whether that is geopolitical supply chain uh changing technology ecosystem particularly with with with AI uh economic right when you think about the the markets and then separately there's things that organizations are are doing that they need to be nimble and agile and and being able to respond to the risks around those and that could be transactions divestures uh changes in in operating models right we see a lot of clients do that in response to how they're going to Market or how are they're shifting their their org models to to become more of a high growth global company certainly in in in thinking about digital engagement right with their customers or even employees and and what that means and those are all things that cause the company to have to think about what does that mean in terms of resiliency availability and and how could that impact our our organization and and really the the broadest sense so how if I'm a leader how do I bring those things together in a way that they can enable processes and I can get the data that I need and and really drive the business value yeah I think there's uh a few things that were we're seeing number one understanding what are the risks that matter to the company and and those are usually the things that impact business or the business strategy really getting down to what are those different scenarios right so as an example a lot of clients are worried about their third- party ecosystem particularly if you look at Healthcare with what happened with change Healthcare and the systemic impact that had uh across the the ecosystem uh looking at resiliency and availability of Technology right as a lot of our organizations that that we work with are scaling the the data needs they have well they have to think about how do they make sure that their systems are up running and they can respond to their clients uh another example is you know if you look at consumer a lot of focus on digital engagement digital front doors well that can create risks in terms of both engaging with customers as well as it's another front door that you have to manage particularly from a from a cyber security perspective so really having a grasp over the risk that impact the business is is the really the starting point and then the second is thinking about well if you need to be agile and more resilient well what data do you need to actually foresee and gain insights and that may include looking at your internal data as well as your external data to complement what you're doing a lot of organizations are having to consider what what is a Federated data model uh look like not only because some of their their data can can have challenges most often right a good example is when you look at the it space most of our clients don't have a clean inventory of their it asset right their cmdb comes up every time I was talking to somebody this morning about that um and and how do you make sure you have the availability of data but also the quality of data thirdly how are you going to connect that data across the the organization end to endend right so you know I used the third party example um earlier and I'll I'll talk about that again understanding the data need from sourcing to purchasing to Contracting to monitoring that relationship to the connections a third party may have with your organization those are all different different data sets that are often managed by different teams you look at another area it right understanding your inventory how systems infrastructure all are being used what they're being used for how do they connect to business processes again some of that data uh when you walk into these companies is in different places and you have to bring it together in order to be able to put metrics around it or be able to look into you know where might there be challenges internally and again complemented by external data right so so the the where you're I love that you're already sort of leading into my next question around I I think that there's a misconception that these larger organizations you work for one I work for another there's that that that we've got it all figured out we've got it all sorted what's the reality yeah a lot of organizations that and I I say this to clients all the time that you would think are very mature often Fortune 500 Fortune 100 companies are having to take a step back uh and that that step back can be driven by a number of the factors I I mentioned earlier but in a lot of times it's driven by you know we want to apply AI we want to automate how we monitor controls we want to have better predictability around our our third party environment or where where there could be disruption and as they start to look at those things they're realizing that they don't have a full picture or they're missing data sets to to to be able to do that and many times right when you when you look at the reasons for that it's because there are silos that still exist particularly in these large Global complex organizations and it really requires a cultural change in some cases the data isn't available right because it hasn't been a focus so you haven't done your due diligence to ensure that you have the visibility and accessibility that's right and I think the other uh important aspect of this that that we're seeing is this really requires an end to end Focus both end to end in terms of looking at a a journey or process not just the individual pieces and also growing more going more cross Enterprise okay and that often requires sponsorship from the top so Dan you're sort of naturally leading into my next question which is a lot of organizations large organizations ey serves now we think we've got it all sorted we think we think we've got it figured out and I I think there's a little bit of a misconception there so what's what's the reality yeah it's often surprising when you look at these very mature Global organizations often Fortune 500 Fortune 100 that in many cases are having to take take a step back and and look at what is their strategy around managing their data so that they can be more resilient and in many cases there's very clear objectives particularly from a risk management perspective that are that are driving that change that could be a greater focus on automation right so we're seeing a lot around how do I automate controls how do I automate the monitoring of the controls it could be around managing their third party ecosystem right and having a lot more data around uh what that looks like uh in in the value chain uh we see it a lot in the it space right with the with the emergence of of AI needing to make sure I understand all my assets where am I deploying uh models and and and data sets and so that is causing them to have to take a step back and look at do they have the data they need is the data quality data that they could they could be using and you know if you look at the symptoms of this I I don't think it's a surprise a lot of these large complex companies still have silos right there's silos there's there's cultural challenges there's uh Missing data sets that they need and there hasn't been as much of a focus we're certainly seeing this pivot in the market more and more to being more end to end right so not just looking at a process and a particular teams part of that process but looking end to end throughout that process and that's also requir sponsorship from the top right to drive that level of change in an organization yeah so uh I'd like to point out at this point you brought up the elephant in the room I didn't but I'm going to use it as a lead in artificial intelligence so in this space now what is needed to apply AI in this space it's funny because we get that question all all the time right and you know there's there's two conversations we're we're having on AI first and foremost what are those use cases and how do you apply it the second is how do you govern it and that comes up because most of the the the functions we having these conversations with deal with risk management compliance governance so it's it's interesting how we're covering both topics and in terms of its application it goes back to number one do you have the data to apply it do you have the right data do you you understand how you're going to be using that that data and an example there is a lot of these platforms come with inherent AI capability yeah many of them require you to have a certain amount of data to be able to apply Ai and in many cases clients haven't looked at that right I'll give you a very easy example that I suggest to clients all the time in the risk management space you're dealing with issues all the time whether they are coming from a cyber lens a third party lens an IT lens Etc controls Etc and and that's one of the largest data sets most of our clients can look at in terms of whether they want to synthesize that data summarize it look for Trends easy easy use case in in my mind in terms of thinking about what's what's root cause what are the symptoms but many of our clients haven't really spend time to think about how they're capturing it how can they drive more consistency right we'll pull up issues sitting with a client and there's just not enough information to really apply in some cases even just basic AI to help route or drive better remediation or engage with the business never mind generative AI so I I I think that's that's number one is having the right data and then number two is thinking through what are those use cases we're particularly seeing clients look at what's practical what can they do now that has tangible Roi but makes sense you know not boiling the ocean on the other hand we have organizations that are looking for true step change where can I use Ai and really augment or do something different and how you go about that is what a lot of clients are are trying to think through because they want to do it responsibly but they also want to do it at scale from a competitive advantage and so just in in it of itself working through that is something else that we see a lot of focus around is how do you how do you think about that how do you prioritize how do you get the right use cases so that in it of itself is is a focus and I appreciate that you uh you came at it from the use case perspective and backed into it I think so often these these conversations end up being a hammer looking for a nail now acknowledging the opportunity of artificial intelligence being so large whether it's tradition traditional traditional machine learning or the new hotness that is generative AI we all know that it's transformational we all know that there's opportunities there so there is a little bit of a walking the line of of okay I I do have a really really good hair let's find some nails so my my next part of this is what industries are really interested in this right now yeah I think from a resiliency and even AI perspective you know the industries we seeing a lot of activity certainly Financial Services right they're very mature they've they've been at this for a while yeah I think the Dynamics in that sector certainly continue to change Healthcare huge area of opportunity just given the sensitivity of data the focus on dig digital engagement whether you're a payer or a provider uh looking at the ecosystem right particularly with the the the change Healthcare event um and and how do you better engage with with patients right certainly a lot of uh Focus around AI in that sector Life Sciences certainly uh just given the the regul regulatory environment as well as some of the supply chain things right how do I manage my supply chain end to end uh seeing a lot in advanced manufacturing and when you think about that space it's not the traditional manufacturing companies it's the conglomerates that are trying to evolve to be more of a growth technology company uh I have a client where they were almost given an edict where the CIO said I want everyone to come up with AI use cases that you can apply in your area and it wasn't specific areas it it was a mandate of of of all the Departments and you know that's I think both a reflection on the environment but also how these companies are are even involving technology certainly seeing a lot of that at technology companies particular really from a simplification automation perspective uh seeing that in energy right as they make uh and think about how they need to change their business models as part of the the energy transition so it's certainly permeating uh consumer a little bit right as they look at digital engagement with with their customers as well as again supply chain Logistics uh looking at at those areas how can you analyze data be more predictive right gets at resiliency as well as deploying some of these some of these tools now I would say this to your earlier comment how each sector is looking at in terms of the use cases does vary M I have a consumer client who is very clear that they wanted to tackle very practical use cases whereas I've seen clients in healthcare Life Sciences where they want practical use cases to start to deploy AI but they're also looking for what are those things that are truly Innovative and can change the way a consumer May interact with Pharmacy and and and prescriptions and what that means in terms of their benefits or you know how do we uh better inter interact with doctors particularly particularly in a uh virtual setting so it it's going to vary by sector in terms of where companies are at as well as uh some of the companies themselves and how they think about what adoption means for them and and and the risk around them yeah so with that can I double click and ask you for an example of a real world use case that that that you're familiar with something that's interesting to you something you've come across maybe some something you've actually been a part of yeah I I think it's interesting so you know a couple examples maybe so I I'll start with a consumer client okay right uh very practical thinking they even joked on from from a governance perspective that there there's a lot of lawyers involved in their process so they're being very practical about the use cases that they they move forward and a lot of the things that they're looking at are in the technology space and terms of helping developers develop or augment how they are developing and that only makes it easier for them to deploy code technology but do it in a way where it's more resilient because now we know it we're deploying we know it's been tested and I can do it a lot faster uh another example with that same client is in the HR space right uh when you think about generative AI capabilities we all have a lot of documentation out there right whether it's training procedures how we uh look to hire employees and so what they're looking at is how do I take that put that into a model and I can provide a simple interface with my employees interact with that whether that's policies or trying to find information about how we train they can virtually have a conversation with my organization without costing me the business owner resources that's right that's right I have another client where this is more in the manufacturing technology side and they're applying AI in various various areas but what I what I think is interesting is in the GRC area governance risk risk management and compliance they're looking at how can they deploy it to you know I'll go back to the issues example as issues come up how can we better look for patterns in those issues how can I better route them to the right people and make decisions without having a human always always involved in in in doing that so we can actually move faster and and prioritize where time needs to be to be spent you know another example I'll I'll I'll leave you with here is you know in in healthcare there's there's a lot of practical things we're seeing but we're also seeing companies look at what is the customer Journey end to end look like and are there components where if AI were applied you can get to more predictable Health outcomes right because when you think about it there's so much data collected both from your company in terms of your you know demographics in terms of your benefits right and then on on a provider side right that your your doctor or you know anyone you you go see for for medical or or you know whatever it is that information is in a lot of different places so if you can bring that together even in just parts of an end to- End customer Journey or engagement with a company there's little things that you can start to change and so we're seeing a lot of focus on that or even using AI to augment call centers right and so when a patient or member were were to call in whether that's virtually with a doctor or being with just you know a call average call center right looking looking into claims or information the I AI can help look at whether or not that call was appropriately handled what was the sentiment right of of that that consumer called in should should a ticket be logged to follow back up with that consumer right we I'm sure I'm sure you feel the same way right right we always call into these companies and you hit zero to try to get through to to a human imagine now if the company actually followed up with you right or on the phone the AI is taking the notes and determining what needs to happen next so those are examples uh in in certainly in the AI space that we're seeing yeah dramatically accelerating my service for and enabling the service provider to be more Nimble to be more informed and to get to those to that resolution that much quicker so what you're what I'm hearing is I'm going to have to fill out fewer pieces of paper whenever I go to the doctors which I'm looking forward to So within that well Dan where do we start like how do we how do we get let let's assume that I have done all of my traditional risk work that I need to do and we're in this this AI Revolution how do I how do I think how am I leading my groups how should I be talking to my teams what action should be taking uh moving forward if I if I want to engage in an AI first world but I want to keep my risk numbers where they are yeah I I think it's it's a great question whether you're talking resiliency or you're talking about applying AI in in the right way I think number one it starts with having the right what we would call foundational data model okay so what's the data you need how are you going to capture that data how are you going to connect that data across the different different areas I mean that that's sort of of when you think about integrated risk management what it comes down to is connected processes and and connected data so see a lot of companies certainly focus on that I think number two is as you have a sense of what that model needs to look like for an area that that you're going to tackle because no one's doing this no one's boiling the ocean right they're picking a priority area and and saying let's go after that it's looking and making sure do you have an understanding of what does current state look like and not current state in terms of maturity or processes but what's the underlying technology yeah what's the underlying data and where does that data sit and you may find in some cases it's still in spreadsheets uh in some cases it's in different systems but at least understanding where that data is the systems that are linked to that data and the processes they support starts to give you a starting point to understand data capture where's data leaving how to how to connect these different systems and then Define what does good look like what is what what's that Target State for the organization and I would tell you that almost 90% of my conversations now in the risk space are on that topic I was just talking to a consumer brand the other day who said we have a lot of different systems for thinking about our sourcing strategy thinking about how we manage our ecosystem of of vendors and then also how does that impact the products we're we're producing they're they're a product product company and it was it was interesting because they themselves and I to their credit didn't necessarily jump to to process right and and you always have to be considering process but they were actually asking questions about the technology and data and what we're seeing in the market so that was that was really the the conversation I think the third thing that's important is is making sure you're you're taking a business Le or experience-led approach absolutely right and so as you're thinking about not only the data model what's current state and Target state but how is this going to impact the business employes your third parties customers Etc whoever the personas are and how do you make their interaction with that process including how you're getting data from them very simple intuitive easy to use digital in in in many cases and connecting it to something that they're GNA they're going to understand and so that's where we see a lot of clients when they think about Target State thinking about the concept of that digital front door or portals or you know what we call a platform of orchestration because connecting data isn't always interfaces it could be workflow it could be how do we uh capture better data up front validate that data before taking it to a to a system of record I I think that makes a huge difference and you know it's not as simple as we're just going to send everybody to to one place it's about how do I better interact with that user and then what's the orchestration behind that to then drive certain actions and and activities right and that'll help you ultimately become much more resilient including on your AI Journey because you're going to have better data and you can figure out where to better apply AI right right so this is been every time I talk to you Dan I learn more and more things that I absolutely love is there any anything that you would like to add anything you want to leave us with yeah there there's maybe a couple things we didn't touch on that I that I think are important here I think number one really thinking about the stakeholders in both spon in terms of sponsorship y right do you have someone high enough in the company that's saying you know what we need to drive change and even though change may be uncomfortable it's important I think the companies that do that and also engage stakeholders at that level are more successful than not secondly also thinking about stakeholders in terms of who have you traditionally not engaged so you know we talked a lot about data AI these are all areas that certainly need business input and there's going to be teams that need to be involved from a risk compliance uh perspective but also where's the data scientists where's the architecture teams where's the engineering team they're not always included up front and so we're pushing clients to really think about how do you involve those teams in the right way you're not creating overhead but you're all you're you're thinking about the decisions you need to make the other thing we're seeing is really thinking about business case and Roi and it's not hard to do it just requires sitting down and saying let's look at what we're trying to achieve and you know there's going to be qualitative benefits but are there quantitative benefits we we can think about whether that is Savings in terms of could be changes to the operating model could be Time Savings as well as what are efficiencies that are going to that are come out of this that that can enable growth that's a huge focus is really nailing that down and keeping that at at the Forefront and then the last thing I'll I'll add is uh when you're approaching technology and and and data we like to to think that there there's our people show up and they have they have a tool belt right and so don't be onedimensional and what I mean by that is when we go and have conversations uh with clients and and the governance risk compliance space resiliency Etc there's a tendency to focus on what's what's that one technology that we're going to use sometimes you analytics pops up but what we're finding is really important is to really understand the problem what's the technology that's going to be the backbone to to help automate or or address what they're looking at typically a governance risk and compliance technology or an integrated risk management technology but then how does that technology fit into the ecosystem right so that that brings the concept of connected data to life and also where can you leverage a data warehouse analytics machine learning Ai and you're going to find that you know if you had a pi that Pi is going to have different percentages of those Technologies deployed which I would expect to evolve over time as capabilities evolve so I think you know we tend to call it an irm plus mindset right integrated risk management plus you know get the best out of out of Technologies and then you know look at those use cases that maybe aren't going to be addressed through another technology and bring a different capability in yeah right it's all about solving the problem and being multi-dimensional and and and doing that that's great I and I it's so timely I I talk more and more myself about the importance of multip multiple dimensions in approaching particularly with artificial intelligence but in in any of these cases you again as you say you think outside be B+ about the the approach to this well Dan thank you so much for joining us today this has been a very insightful conversation as always I love chatting with you thank you for having me yeah of course so and thank you to all our listeners Please Subscribe and share if you like what you heard today and be sure to join us for our next episode [Music]