Bhavesh Vaghela: Hello and welcome to Ops game changers, the podcast that shines a light on the crucial role that operations teams have in driving business performance and improving staff and customer experience. My name is Bhavesh I’m the Chief Marketing Officer here at ActiveOps, and in each episode I hone in on one KPI or a challenge that a business has solved. I delve into how they went about doing this, and we explore the outcomes achieved. Now, today we’re joined by Patti Eyers, CEO at First Mortgage Services to learn about how FMS embarked on a strategy to align finance and ops. Now, Patti’s going to share why this was so critical for FMS and the impact this is now having on operational decision-making. But before we jump into the topic. Let’s welcome Patti to the podcast. So, Patti, welcome to Ops Game Changers.
Patti Eyers: Thank you, Bhavesh, lovely to be here.
Bhavesh: So, listen, before we jump into this this whole topic of finance and ops. Perhaps you can tell the listeners a little bit more about yourself, and I guess how you ended up in the role that you’re in right now.
Patti: Well, I probably ended up here without a lot of planning actually for this. So, I’m a bean counter. I’m an accountant by trade and so I grew up, you know, in a management reporting function, very much on the analytical side. And then, as I grew up professionally, I ended up in in a lot of CFO roles, but I think because as an accountant, you’re able to have quite transferable skills from industry to industry.
Bhavesh: Correct.
Patti: And I was able to move from IT industry to building industry to financial services. And I was just really keen to trial different types of workplaces, different types of products. And then one day found myself in a financial services business which had investment management it had a bit of financial planning, and it had this corporate trustee business that had acquired a lot of mortgage processing services as an ancillary business, and I found myself loving that mortgage processing that outsource processing business, and then over time started to run that, and then they threw an insurance business alongside it. So really, quite unplanned. But I think I had a lot of opportunities to build different skill sets along the way. So here I am. Mortgage processing and insurance.
Bhavesh: Who knew right? That’s the start of the journey and how you ended up here. Well, I mean, it’s quite interesting, because it’s similar to being an accountant. Being a marketeer, I’ve had a similar kind of journey where I worked with lots of tech companies that market to different industries. So, you get to see a lot of different areas. And it kind of hones your skills as you go along. So, before we jump into ops and finance then perhaps tell the listeners a little bit more about FMS, and what you guys do?
Patti: Yeah. Sure. FMS is an outsource processing business. It acts for lenders as a back office. Alright. So, we take a loan from unconditional approval all the way through to settlement and registration. And I’m located here in Sydney, Australia, and so we very much operate within the Australian and New Zealand environment. And the legal structure is quite important in that, because it impacts, obviously, you know, lending protocols, etc. So, we have different jurisdictions, state jurisdictions that have different rules and this business is able to operate on the national level right across different types of lenders taking their back office, their back and middle office away from them so that they can focus on the front line of sales and relationships and growing that frontline business. So, the business has been here for about 30 years across Australia, New Zealand, and we are part of a bigger fortune 500 company. It’s an American company that has been around for 130 years, so very much portfolio alignment across the globe, which is wonderful. And this business is very much run autonomously in this region. So, we’re very lucky to be able to be quite innovative about what we do be quite autonomous, independent in the way we make our decisions, that are able to tailor that our business approach to the local market.
Bhavesh: Fantastic. So, I guess let’s kind of delve into that topic, right? And you know this whole idea of finance and ops, it’s quite an interesting discussion, right? Because, you know, typically, when we talk to ops folks, there’s always that. Oh, I’ve got to convince finance in order to be able to invest more. I need more headcount or all those kinds of conversations that you’re having. And I guess from a finance point of view you’re trying to understand, you know, what’s the return on investment on these things? And how do we actually make sure that we’re doing the right thing across the business? So, there’s always this really interesting relationship between the two. And that’s why this, your story is so fascinating. Because, you know, this whole idea of becoming much more data centric across the two functions kind of almost takes away all the mystery, right? It makes it. Everybody’s talking in the same language. So perhaps if I kind of take you back to when this journey started? So, what was the rationale? What was the starting point for you in in going down this journey?
Patti: I guess, in terms of the journey around operational metrics itself, I would say that, you know, we used to run, it’s almost like a manufacturing style operation. There’s a lot of handoffs. It’s almost like a production line rhythm to this business, and I think that’s important to understand. So, we have about anywhere between 300 to 500 staff at any one point, depending on the business cycle, and how the property markets going in volumes. And so
so, I guess when you’ve got a really fast paced business like that, that capacity management piece becomes really important, you know. And if you think about the fact that when outsource provider. The back office is actually the front line.
Bhavesh: Cool, so.
Patti: So, where’s that? That process for a lender is actually their back office. But for us that’s our front office. That’s the face of our business, it’s our front line. And so, when you think about capacity planning and hitting those SLA’s and those milestones, that’s incredibly important. And when you’re acting, for you know, multiple different lenders with different processes that becomes very, very complex. So, if you imagine managing capacity across that type of operation. You know, we were in the old days, and I say, old days, pre-2011
Bhavesh: okay
Patti: Managing this business through the sheer capability and expertise of each leader. Right? So, they were making decisions. They were then looking at that, P and L and then, of course, finance is saying, why are you spending so much money? Your volumes that down all the things that you just alluded to. Yeah. And then managing, using you know, some operational metrics, but simple ones like volume and then going to the P and L. And then they look at FTEs. Whatever got too many people for the amount of volumes I’ve got compared to say a year ago, and you know that that type of comparison to get a sense of whether they are performing well or not. And I think the problem we had was the ability of flex according to that volume curve quickly enough to make it sustainable, consistent, and profitable, but not sacrifice service levels. And so really, the problem that we had was just operational transparency. We weren’t, I think, clever enough to think about data, centricity and linkages to find it. We just wanted some operational transparency, and we wanted to stop talking anecdotally about resourcing. We would ask, I would ask as a leader, operational questions that really needed empirical answers. Not anecdotal stories about, you know, we move these 3 people here and wait for this, and you’re moving the deck chairs without any net outcome. I was actually after empirical responses, but I wasn’t getting it. So, its anecdotal explanations to variances and forecast usually means you’re not consistent from one operating line to another and if you want to drive that consistency and comparison, you need some more formal framework to gather, and measure will measure and gather your operational data. And if you think about a finance function. It’s very uniform, and it’s quite prescriptive. So, I guess that’s where the journey started. We wanted the transparency, and we wanted consistency around capacity management.
Bhavesh: And I guess, Patti though it was, it’s it was driven by the finance function in this context, right? I mean? What was it like talking to the ops folks? Was there a lot of resistance at that point? Because there is that there is that constant battle right.
Patti: Yes, I think I think the resistance came when I realised that you know it needed a finance mindset. The program of work needed a finance mindset. The governance of the program and the governance of the data needed a finance mindset. Because if you think about financial people, they generally play that they’re generally playing a governance role, they generally playing an audit role. They’ll check, you know. They’ll verify right? They’ll reconcile. And what we’re finding as we got more progress around the operational data, when we got really good gathering and measuring the operational side of things we suddenly realised, well, wait a second, that operational data set as a standalone data suite. So, when I then ask questions about productivity against a cost base and you merge those two together, and then you started to then go. Oh, wait a second. What about the volumes. How are they looking versus the resourcing versus the financials versus the payroll. Suddenly those numbers didn’t stack up. So, if you added all the minutes and times that were clocked on the operational data and you went to the transactional system suddenly, if you added all of those up according to standard timings, you suddenly worked out. Wait a second I’ve actually process five times the amount of files that actually existed through the system? Right? And then how do I equate that to the contractor. Time sheets in the payroll. That doesn’t make sense right? Because that person wasn’t even there that day. They said they process 10 files. Does that make sense? Yeah, so.
Bhavesh: Totally makes sense
Patti: We went. Wait a second. I can’t. It doesn’t make sense. When I started to bring financial and operational and transactional data together. So then that cross validation started to call upon finance skills. And I and I think you know, when we look at the need that arose, you started to then having to consider the finance culture versus the operational culture. And that’s what you’re getting at, Bhavesh
Bhavesh: Yes, totally.
Patti: Resistance came when we said, okay, finance is now going to manage that that program. And they went, wait a second. That’s my program, you know. And so, someone is questioning the integrity of my data because operations has to own the data.
Bhavesh: Course.
Patti: Finance owns the structure, the framework, the program, the governance, but operations has to own the data and that’s where you might get some friction because I think you need to explain that to the stakeholders about who owns which part of the program versus the data. Yeah, does that make sense?
Bhavesh: It makes complete sense, and I think there’s a couple of things that you kind of align. Well, bring to life there, one is that whole inconsistency of data across different departments. So, you know, when you’re trying to connect it all together, it doesn’t add up. There’s something not quite right. And it’s really common right in in lots of businesses. And even if I just look at ops generally in in in a large operational environment. There’s, there’s lots of inconsistencies. And even just how you measure certain things like productivity or utilisation, or whatever those key terms are. So even having a common framework in a department makes such a big difference. So, I can. If you, if you just take the upper level and have a common framework across the business. Then, all of a sudden, you’ve got much more. Well, A. You can trust the data, and you you’re much more clear, and I guess trust is the probably the next thing to kind of delve into, because the moment you are seeing the data is not adding up, I guess nobody believes the data anymore. And then you’ve got to try and rebuild that. And did you find that was a challenge that you had to get across.
Patti: Yes, that’s a massive challenge, right? Because the only reason that you would seek to drive that data together from different functions is because you wanted to use the decision management decision support.
Bhavesh: Okay.
Patti: And that’s where this operational data starts to add a lot of value beyond just managing the day-to-day operations. So, the reason to do that. If you think about the fact that the rationale to do that is to add value to the business, in pursuit of decision, support. Right? Now, if your data doesn’t add up across the functions, you’re going to get dysfunction in that.
Bhavesh: Course.
Patti: Those decisions you’re going to. You know, you’re going to make poor decisions because they, it’s based on floored data. So, it’s really important to that get that integrity, you know. And if you think about using the finance function as a benchmark that’s the most audited governed data there is in your organisation, right? Because you’re producing accounts. It’s everybody’s looking at that. Everyone’s answering that you’re doing lots of variance analysis. So actually, the various analysis that is produced by finance oftentimes needs the operational data as the basis of an explanation and that, too, has to have synchronisation. So, you know, there’s so many reasons to, to bring those two together. Not the least of which is trust so that operational data has value because if there’s no trust it has no value.
Bhavesh: Yes, it’s really, it’s really interesting. Actually, we’ve just done some global research. Across 7 countries, including Australia, New Zealand, UK, Ireland, South Africa, Canada, and US. And we’re looking at from an operational perspective.
The level of decision making with data and kind of having to think about where people are, and what we found was really fascinating in the sense that the numbers were quite scary. You know. I think you know in the nineties. So, I can 90 percent plus of ops readers, you know, making decisions based upon data that’s older than a week at least, and older than a week. So, when you’re talking about decision support which is clearly what the data is there to drive, it’s there to drive in a better decision making at the point you need it. And if you’re doing that based upon data, that’s, you know, in some cases 45 days old, I mean, what chance do you have to make a good quality decision for what you know at that point in time? And if you, if you lay on top of that AI which needs good data in order to drive forward looking things like predictive and prescriptive nature. Then all of a sudden, you’re in a bit of a situation where you can’t capitalise on what the potential is of these new technologies. So, the data framework is such an important thing that for many it seems like in ops it’s something that’s been overlooked for so many years, and it’s now becoming more and more prevalent. And you’ve been on that journey obviously will be ahead of the curve from that perspective. So, taking you back in terms of you know, you’ve got this driver. You need to solve these problems that you’ve articulated. You know, how did you actually go about doing it? What were the kind of things that you actually did in order to bring those two worlds together.
Patti: Well, I think first you have to train the finance people on the operational methodology in terms of collecting and measuring that data. So, the time in motion study. So, I think the best way is actually get them to manage that program. So, if you have to re baseline every year, finance runs that re-baselining process. Operational people do the timings, but they run, they project manage that baseline. They govern, you know, access to the systems, and they govern the setup of the system for the re baseline process, for example. So, you need to train the finance people on whatever platform or methodology you use for your capacity management. And then you need to train your operational people on how that data links to the profit loss statement that they are responsible for or the expense base that they’re responsible for. Then I think you know, one of the things that we have learned is that you can have finance run the program but I think, as you get more sophisticated about it, what you’re looking for in terms of personnel is having operational expertise. Say, leaders who who’ve been in the business for a long time that are looking for some career progression and are very, very numerate and understand this capacity program and the data. Actually, move those operational experts, who love analytics and reporting, move them into the finance function. But reporting management reporting structure within that finance function. So, they’re sitting with the finance analysts. Finance love that because suddenly they’ve got an SME who can explain financial variances in terms of operational metrics right down to the minute and second and last widget in real time, if you’ve got the systems and framework, and the operational people suddenly with that integrated data, are able to almost predict their P and L on a weekly basis, because they know that linkage about time and motion with the impact on the cost base.
Bhavesh: Yes.
Patti: And I think with a lot of this capacity management methodology, the trick is in trying to flex your workforce or your resourcing as close to the volume, curve as you can, as close to real time as you can. And that was really difficult to convince operational people of, because they thought, oh, we could run for a week or a month, and then we’ll look back, you know, over the last 3 months, and are we resourced correctly, and then they’ll do this retrospective review, and then they’ll make those changes. But actually, by combining data sets, you can see very quickly that if you flex according to the volume curve very close, as close as real time as possible, intra-day , intra-week, the compound effect of small movements is so much larger than taking a big shift, or a big cut or a big upscale every 3 months or every month and when you merge those pieces of data that’s really visible in the reporting for both sides to see. And suddenly they have to work together. They want to work together because making small pivots is much less painful than making tough decisions in larger intervals.
Bhavesh: So, I think you know what’s really fascinating there is that I guess at the simplest level, what you’ve done is, you’ve kind of demystified both sides there, right? So, there’s no more mystique about it. There’s no more, well, I don’t understand what ops do, I don’t understand what finance do. You’ve kind of blended the two together, so they both become an important part of the delivery of the outcome
Patti: They both need each other, Bhavesh
Bhavesh: Exactly
Patti: They both need each other to make decisions
Bhavesh: And that’s you know. I guess that would have been, so does that. I mean, obviously, there is a really interesting takeaway there about picking the right people
to go over one side and the other, and learning more on both sides, and put in somebody from ops into finance. That can be your SME any. I mean, obviously, that the individuals need to be analytics, but also analytical, but also need to understand the ops. And I guess those people obvious in your team, aren’t they? You kind of know who those individuals are.
Patti: Yeah.
Bhavesh: It’s quite a good career progression for them, as you say. And have you seen those people flourish as a result.
Patti: Yes, absolutely. And I you know that cannot be overstated. You know we we’ve seen that. And I think when you talked about, you know maybe some of the resistance, you know, in making some of these structural changes, or handing the baton over to finance. You know that was tough, right? But also, when the people that you put in from operation to finance are usually very experienced, they’ve been in the leadership roles for a while. They’re looking for something new, and they’ve been around for quite a quite a while. So, they actually have credibility with that operational team, and that’s made it so easy to build the trust that you’re talking about that’s so important. So, when they go back into the operation to roll out, for example, in the last six weeks we’ve completely re-baselined on everything, and quite drastically, because we’ve put in some innovative system changes which really has cut down the timings across the board, and that’s tough for the operations, but it’s coming from one of their own. And they look to them for advice rather than approach it with trepidation and defensiveness. That’s been a massive step forward for us, you know. We haven’t always had that insight, you know. That would be one of the lessons learned, you know, you know, one, that the function and the governance has to be independent. So outside of ops. So, we had set up a quality team to do that originally. And but that quality is still reported into ops. So there really wasn’t that independence? Nor was that that finance oversight. So then, the next step was, we moved parts of it to finance. And now we’ve actually moved certain personnel from ops trusted personnel in under that finance umbrella, you know. So, you can see step by step, it’s evolved. And this structure right now is working incredibly well. And we know that because we’ve made some really significant and I guess, for ops quite scary changes, because we’ve cut timings by, you know, in some of those core processes by 70 percent.
Bhavesh: That’s amazing.
Patti: And that’s frightening, right? Because.
Bhavesh: Yes, it is
Patti: So, when we do that, because the technology we’ve put in has enabled those timings, and you can actually clock them whilst you can see it. It’s still frightening to suddenly, you don’t feel that you’ve got to work to time and motion studies that are 70 percent less than what they were, so it helps to have a trusted person on the other side of the fence, so to speak.
Bhavesh: Yeah, I mean, I think you know, it seems that that culture that you’ve now formed in across finance and ops is really, you know, it’s like an engine that’s purring, right? That that people are working together really well so it’s kind of it kind of leads us on to. Certainly, we’ve already started some of those conversations around. So, what’s the results? So, what has happened? You’ve talked about some of the innovative tech that you put in in order to kind of reduce the kind of servicing time for the things that you’re doing. And as an outsource, it has a material impact for you in your profitability when you’re dealing with your, you know the sustainability of a client. But also, the service levels you can give to the client, and the cost you can provide to become more competitive in these kinds of environments. So, what have you seen in terms of from a kind of business benefit perspective on this kind of work that you’ve done to combine the two?
Patti: Look. I think I think you know; we’ve got to a point where you know, the data that we get for the from the operations is so well integrated with finance, we can actually, we do all of our pricing, using all of our operational data because we can capture it by person, by the second, you know, and you can even capture it based on the individual processes that with time and the bundle processes that might we might be proposing a service for we you know it to a point where, if you know the person people that you’re putting in particular team to deliver per particular service. We use that data to price. And so, I think from that point of view that that data has been really useful. We also like, I said you know, we look at the pricing of, for example, time sheets on the payroll system to make sure there’s integrity. In the billing of contracted invoices from agencies, for example. So, because we know how much time is spent, we know how much it should cost. You can model that if the actual invoices come in and the time sheets come in that doesn’t work out that way, we actually validate that. So, there’s actually savings in that as well, because there’s actually a lot of errors in time, sheets and agency invoicing. So, the validation pieces like that. There’s many aspects of the way operational staff manage their P and L, that’s really become quite sophisticated, you know. So, they can actually project their cost base now. It’s really easy for them to use those standard timings versus the actual timings versus the payroll data as an operational leader to basically say, what if my target is X, or my forecast is X or my budget is X, I know exactly what I need to which operational levers I need to pull, and exactly how much and how long it will take me to arrive at that targeted forecast.
Bhavesh: So, I guess, Patti, that’s, you know, going back to one of the original things you said around, you know, using data for decision support. What you’re describing is now the ops individuals and leaders in operations are making decisions backed by data and can also think about forecasting in a better way than they were able to be before. So, it’s gone from, you know, that experience and the hunch that you had in the anecdotal evidence that you were talking about at the start to now, it’s based on true fact of data in data that you guys trust and everybody’s, you know, been through the journey with together. So, they all understand what that data is doing. And that’s a that’s a big shift, isn’t it? In terms of.
Patti: It’s a huge shift, and structurally, it’s been a huge shift, too, because you know, now that we’re able to do this we’ve been able to change the operational structures to a completely different model. So, if you think about a processing business and outsource processing business. It’s traditionally organised by process.
So, you might have a settlement team. You might have a document verification team. You might have a document preparation team and you’ll do that right across the business, and they’re led by leaders who are experts in those stages of the fulfilment chain. But when you’ve got not only the integration of the data, but the integration of the skill set now, where you’ve got finance people understanding operation, operational people understanding, you know, every minute’s impact on their P and L. And we’ve been able in the last two to three years to change that whole structure, so that we organise that operational business by client, and we can put leaders there that are now skilled to run almost a mini FMS business, and so client by client with their own P and L right? And because they have the financial skill sets to do that now in combination with that operational data that they’re already experts at. So that whole operational model now has completely changed to o break up the business into autonomous parts, because we’ve got the leadership, capability the operational and data and financial analysis at a leadership level to always run a full-service hub, full service. P and L by clients. So, it’s more customer centric and it gives these wonderful career paths to operational stuff that were previously just managing one line of operation.
Bhavesh: So, it’s really interesting. I mean, you’ve got ops people that moved in some way into finance. Did you see anything go the other way. You see, finance guys going into ops and becoming those leaders in your autonomous groups?
Patti: But do you know what, it’s interesting because some of our ops leaders came from a financial, academic background. So, they’ve got commerce degrees, etc. So, it’s quite interesting because they’ve loved this transition because they suddenly, they’ve learned the ops skills. They’ve got the academic financial skills. And now they’re able to put that into practice. So, whilst we haven’t seen finance move into ops, because it’s quite a complex, unique process. Quite legalistic in some ways. Right?
Bhavesh: Okay
Patti: You’re almost a panel solicitor in in some instances. But what it has done it’s really enabled the operational parts of finance. So, accounts payable. You know, cash management, because we’ve got a cash management team that runs all the settlement moneys. The reconciliations team. So, the more operational parts of finance have adopted the practices.
Bhavesh: Fantastic.
Patti: And the reporting platform and framework that operations use for their operational data.
Bhavesh: Well.
Patti: Whilst we, the people, haven’t moved. They’ve borrowed and leveraged all the operational protocol skill sets, methodology and systems to run their finance operation. So then, now, you’ve got a more efficient operationally based finance function where it’s relevant in areas like accounts, payable payroll and those large transactional volumes.
Bhavesh: Makes sense, right?
Patti: Yeah, makes sense. So, you know, it’s really had a compound effect across departments.
Bhavesh: And you know, in terms of the outcome of this, I mean, it sounds like there’re definitely some employee benefits that have come off the back of this. There are career progressions and in in ops generally, I know, ops generally has always been, I always say it’s like white noise right every year ops is told to cut 10 percent or whatever that number is. It’s just the pressure they’re on all the time. It’s in order to do that. So, they’re always in that mindset well they should always be in that mindset about further efficiencies and improvement. So, you know anything like this, even though it’s scary, it’s something that’s part of their role in some ways. But it’s still a change management thing. Have you seen kind of things like staff morale, increase, attrition, go down. Have you seen those kinds of benefits of employees generally?
Patti: Look. I think you know at a at a middle management and more senior leadership level for operations. And I’m talking about what we call service delivery managers and team leaders. You know, you’ve really seen A great engagement with this approach Because, you know, they realise at those levels, they realise that as they climb the operational hierarchy the one thing that’s always missing is financial training. So, as you go more senior in the operational department, suddenly, everybody wants to talk to you about your numbers, right and your cost base and actually in a pure operational function, no one ever really takes the time to sit down and teach you that stuff unless they send you to a course which is what we traditionally do. But actually, what we’re finding is as they do this, they’re getting that financial training. And those leaders, because they know that that’s a gap as they progress because we talk development plans, that type of thing. They welcome this opportunity with open arms, because it’s actually filling those development gaps.
Bhavesh: Yeah.
Patti: And they’re asking for more. So, we put them with a finance mentor as they’re progressing through this change. That’s one of the things that we do. So, we’ve got finance people that are mentoring them, you know, and sometimes, if they’ve got the finance background, we might give an HR person just like you might give an HR person mentor if they need more of the people management side. This is no different and you know, we found that actually, the engagement has gone right up for those levels because they understand this is fulfilling a career gap.
Bhavesh: You know. Apparently, I think that’s such a great takeaway. I can’t think of any other companies that I’ve spoken to engage with are using finance people as mentors to ops. That’s just it sounds now you’ve mentioned it so obvious. Why wouldn’t you? Right? And it makes perfect sense. So that’s a great takeaway as well. So, in terms of the impact on customers you talked about the restructuring from you know autonomously from a customer-by-customer perspective. Have you seen a difference in the engagement you have with customers and the results you’re able to give your customers as a result of some of this?
Patti: Well, absolutely, as I described before, you know, would not have been able to completely restructure the operation based on customers rather than based on process.
Bhavesh: Yes.
Patti: We would never have been able to do that, and that in itself a fully dedicated squad or team or department that purely looks after that customer and the ability to match leadership in those hubs to the type of customer they are. So, we sometimes we have large bank customers that are you know the machineries. It’s big machinery that we’re working with, you know, whereas on sometimes you’ve got smaller, more boutique lenders, and you know they prefer to nurse one deal through one at a time, because the volumes are lower. So, you need a different style of leadership.
Bhavesh: Of course.
Patti: For each of those customers, and the fact that we’re able to structure it that way because of what we’ve done around this the combination of an integration of data that in itself that structure fully points to a greater level of customer centricity.
Bhavesh: Completely, completely.
Patti: You know those leaders that sit in the hub, that the hub itself are then also watching NPS scores that are specifically for that customer, and they own that along with a P and L along with their operational metrics, you know. And it really is a holistic ownership of that that business, so that in itself, I think, is fully pointed towards the customer.
Bhavesh: Totally. Totally, I think sometimes there’s a gap between what ops do and the outcome from a customer perspective. And this way the ownership is across everybody to make sure they’re delivering those outcomes, which is, which is, which is really interesting. So, I mean, it sounds like, I mean, you’ve come a long way from when you first started. I don’t think you probably, if you look back now, did you even think you’d get this far at the start? I mean, you know, it feels like and a complete transition from start to finish.
Patti: Oh, do you know what we’ve been at it a long time? You know, I think we first put an operational capacity management program in about 2012, 2011 thereabouts. Right? So, we’ve been at this a long time, and you know we have learned many lessons along the way, and sometimes we’ve had to learn them more than once. But you know, it really has been a journey, you know, and I, you know the last thing I would want your viewers and listeners to think is that we had this all mapped out, and we had a vision. You know, we kind of solved one problem at a time made one mistake at a time. But I think we’re quite a learning organisation. So, we, we tend to really ponder each fall and pick ourselves up and really analyse it, and probably overthink it. But that’s the way we’ve gotten there, you know, to really internalise those you know any mistakes that we’ve made, and honestly, sometimes you make them, you know, more than once, and you know I’ll give you one example of that. I’d say that, you know, when we used to always have to come and remediate some of the integrity of the operational data, and we bring your helicopter some senior operational expert in. Then, after a while you think, well, wait a second. Why do I have to bring someone independent in all the time to kind of check and audit that data? And that’s when we said, wait a second. It actually needs to sit as an independent governance role, you know, on an ongoing basis. So that’s the way we’ve done it, really honestly learnt from each stumble.
Bhavesh: And, you know, I don’t. You know I never believe anybody that says we had it all mapped out because it never works that way, does it? You’re doing it step by step, and you end up in the path that you’re in. So, it’s a great, it’s a great learning tip, actually, because you, you know, planning, you can plan whatever you want. But you’ve got to get yourself, you know, meandering your way through it, which is really cool. So, looking back, then, you know what’s been your proudest moment on this journey?
Patti: Do you know, actually, my proudest moment is the development of the people. And I never thought like you said I would say that of a program where I’m collecting operational data and madly trying to maintain long-term and day-to-day integrity in that data. But honestly, you know, if you go into any finance function, what you hear time and again, no matter what organisation you’re in is from your accounting staff is I want to learn more about the business. I want to get closer to the business. Have you heard that, Bhavesh?
Bhavesh: Yeah, completely.
Patti: And marketing people, too, I’m sure, right. They have the subject matter expertise, I want to know about my business. How do I get more embedded in the business? I want to be working, you know, alongside the business. This has been fantastic for the finance staff, do you know? And they learn about all of the heartaches and challenges that the operational people go through, that you don’t get a view on sitting in the finance ivory tower, because that’s what it is, you know. And I think because the finance team delivers so much value because they have enterprise-wide access to all types of data, because they’re sitting, and they have this aerial view of everything.
Bhavesh: Completely.
Patti: But the downside of that is, they’re not really close to the business.
Bhavesh: Hmm.
Patti: So, this draws them back and really embeds that in the business, because they need the operational data to explain the variances. And if they manage the program. They’re very much entrenched in that business, and there’s a greater appreciation by finance staff of pace and the sheer intensity of an operational department. You know. That’s I think things like that, you know.
Bhavesh: They’re inspiring, aren’t they?
Patti: I’m most proud of.
Bhavesh: Definitely inspiring. I mean, as we wrap this up. Patti, I’ve got a question to ask you which is which it’d be, it’d be wrong of me not to ask this. Bearing in mind. I’m talking to a CEO, and as well as you know, the data journey that you’re on. And that’s the AI question. Right? So, from a point of view of what you guys are doing. And you talked about innovative technologies. Are you exploring AI as part of all of this? What’s your view on it and what you think it’s going to potentially bring for you guys?
Patti: Yeah, it’s pretty buzzy, AI. right now, I think what I’d say to you is being part of a First American financial corporation, we’re very, very lucky. You know, there’s something like 23,000 employees across 60 trading jurisdictions. And First American have a lot of AI programs going working directly with the AI divisions of Microsoft and Google Analyse. So, you know, I think we’re very lucky because we leverage all of that. And, like you’ve rightly said Bhavesh, you know, it’s all about the data in AI, you know, it’s rubbish in rubbish out. There’s also a lot of dangers around intellectual property. And how if you’re not accessing this technology properly and you’re not using it properly, you might overstep the marks of your intellectual property and the marks of others and so you don’t want to cross those boundaries. So, we need to progress through the AI journey very cautiously. From a legal perspective, from an information security perspective, from a data security perspective. And we’re lucky to have that First American support. The other thing I would say about AI and any other types of technology. You know, there’s a lot about robotics before. And in our business all this stuff is really, really important because we’re trying to get drive greater throughput and it’s a very competitive space. So, whilst we invest a lot in that area. We also have a venture capital arm through First American, called Parker, 89. So we’re very lucky with all that technology. But what I’d say to you is this, that that we always have to define the business problem first, you know what I really don’t want this business to do is to go down a path of AI or any other type, just for technology’s sake.
Bhavesh: Completely.
Patti: We don’t want AI just for AI’s sake, we need to define a business problem and AI, combined with other levers, might be that solution. So, I think that’s really important. Start with that business problem. It’s really important to design a solution, an AI solution, carefully and to be accessing the AI technologies very selectively, and really understand that if you access that technology in one way, are you handing your IP and data over.
Bhavesh: Yeah, it’s a very important. It’s a very important observation, actually.
Patti: Yeah. So, I’d say, we are very much exploring that space. We’ve had AI for a while. You know, and a lot of people now are moving on to that Gen. AI space. So absolutely, we, we’re very, very active in that space. But we do it very in a very structured way. And a very controlled way with a lot of support from data security teams and AI teams as well as global partnerships.
Bhavesh: Yeah, listen, thank you for that. Patty, listen. It’s been an absolutely fascinating conversation. I’ve loved every minute of it and learning more about the journey that you’ve been on. So, I wanted to just thank you for participating on the ops game changer podcast and sharing this fantastic story. And I think there’s so many of our listeners who come from an ops side. There’s a lot of really great takeaways on how to work with their finance counterparts and potentially work closer together and building that that synergy together as units to drive the main business outcomes that businesses are looking for. So, Patti, listen. Thank you so much for taking your time and spending some time with me.
Patti: Been an absolute pleasure and good luck with all your listeners and ActiveOps programs.
Bhavesh: Thank you very much, Patti and thank you very much for our listeners, for joining us. Please do check in to catch up on more episodes of Ops Game changes to hear more fascinating insights from other leaders in the fields of operations, about how they overcame challenges to drive productivity, boost performance and ultimately drive business outcome, so bye for now.
