Creating The Ideal Future Experience

Today's digital investments are driven by the technology that's available - not necessarily what companies need. However, companies have the power to shape the technologies being developed and create the ideal future for their organization and stakeholders. Companies don't need to wait for a silver bullet technology to land on their doorstep - they can take part in creating the best possible approach.   

In this session, Melanie will introduce keystone technologies that are on the horizon of the insurance industry and promise to reshape insurance over the next decade. During the second part of the session, I will lead the group in an interactive exercise to play out what business impacts these technologies would have on their organization and how their companies could begin engaging with or developing these new technologies and processes today.

Transcription:

Melanie Subin (00:08):
All right, who's ready to get started? It's early morning. It's your last day. Your spouses are wondering when you're coming home. I'm going to kick it off. We have about 55 minutes left and we have a lot to accomplish today, so it's really great to meet you all. My name is Melanie Subin. I'm the Managing Director of Future Today Institute. I will warn you only the first 15 minutes of this is me talking at you and the rest is interactive. I want you to be able to go back to your offices and your workplaces with something you can actually use. So I come from a company called Future Today Institute. We are a management consultancy only because there's no better label for it, but what we practice is strategic foresight. Has anybody in here heard of Strategic Foresight? You have? Okay, a couple. That's great.

(01:02):
So strategic foresight is really just good long-term strategy. That's all it is. It's taking evidence and information that you have today looking at uncertainties about the next couple of years or the next 10 years, and then using that to make a plan. So that's what we're going to be doing today. You have worksheets in front of you that we're going to be using to create this ideal future experience, whose experience, which experience. That's up to you. So if anybody, if you two have changed your mind and you want to move to a table with more people, you're welcome to do that. If you want to stay there, that's fine too. I'm going to walk you through just a little bit of information, and then we're going to spend about 45 minutes doing some table work, creating that ideal experience. So before I start, I just want to tell a really quick story.

(01:54):
Did anybody see the New York Times article from a couple of days ago about how management consultancies are spending all this time and resource now on creating AI products? Anybody see that? So it was talking about how BCG and McKinsey and Deloitte and Bain, so much of their business now is about creating AI products, and it's reminiscent of the.com boom. It's AI suddenly is in everybody's awareness. Now, everybody needs to create something using ai. The problem is now they're going, they're creating not for the consultants, but for the companies. They're going and creating these AI products, but they're not really thinking through what it needs to do, right? It's a hammer looking for a nail. My perspective on technology is that it can be a great solution for something. So before we get too far into this, I want you to, not on your worksheet, but on your piece of paper, take a minute and think about a problem that you would like to solve as customer experience professionals.

(02:55):
Think about the cohort. Maybe this is an age group, maybe it's a demographic, maybe it's a certain market. What cohort would you like to solve for? Think about the challenge. So what's your biggest beefiest unsolvable challenge? The thing that you can't figure out, and I recognize there might be carriers and distributors and insurtechs in here, and that's fine. So think about your cohort and your challenge, and then finally, just for a minute, think about what you would want the ideal outcome to be, and then we're going to use these in a few minutes when we come back to it. So just take a minute, think about it, and I'm asking you to write it down. I'm not going to collect it, but your opinion might change

Audience Member 1 (03:44):
45 minutes. So if you want to switch to a table, definitely make your life, me and my imaginary friends. Yeah, exactly. I mean, you're welcome to.

Melanie Subin (04:01):
So what cohort do you want to solve for? What's your beefiest challenge and what outcome do you want to see?

(04:22):
All right, so you can keep on writing down. I'm going to keep going a little bit. You can keep thinking if you want. Our plan for today is I'm going to take 10 or so minutes just to bring up some technology trends that I think you should be thinking about. I'm not going to teach you about them because you probably are already aware of them. Instead, I want you to be keeping these technology trends in your mind as you're creating this experience throughout the day. What I want you to be thinking about is what problems could they be solving, right? Instead of having technology be a hammer looking for a nail, what problems could we solve with these technologies? Then we're going to spend the majority of this session in table work, figuring out what that future experience could be that you could create. Part of it is going to be table work. At the end, I'm going to give you a few minutes to write down what your next steps might be. You may not want to share with your tablemates if you walk away from this with a plan, and then we'll share a little bit at the end.

(05:23):
All right. So three sort of areas of technology trends, future. Today Institute specifically focuses on technology. That being said, when you take this practice back to your work, you might want to think about other factors and influences that are impacting your work. These could be regulatory, social demographic, and so on. So the first area of technology trends that I want us to think about is artificial intelligence and machine learning. This is important, but I want us to get past generative AI today. Generative AI is one modality of ai. It's not the only modality. It's not even necessarily the most important modality depending on what problem you're solving. The second is going to be data collection and analysis, specifically thinking through your company and your challenges. It's not that you don't know that you need to analyze data. It's thinking practically about the challenges of getting that data out of legacy systems and standardize so that you can actually use it and analyze it in a new way.

(06:24):
And then finally, robotic process automation. So when we think about standard repeated processes in the insurance industry, where can we streamline not just for expense efficiency, but for the customer experience, make it faster. We just saw a demo from Y Insurance quote and bind is instantaneous. That's really easy for an insurtech to do. It's not so easy for a huge legacy insurer to do so let's talk through those things today. So AI and machine learning, I am going to make you interact a little bit. Show of hands, how many of you right now are thinking about or looking into AI or doing something with ai? Yeah, exactly. So this is a study from Gallagher Bassett. It showed that 67% of insurers are doing something with AI and chatbots around customer service. The thing is, chatbots are not the best application of artificial intelligence. They're not necessarily the most necessary modality of artificial intelligence.

(07:24):
So I feel like statistics like these can be somewhat deceiving. It makes it seem like every insurance company is ahead of the game on AI. I came from travelers. I spent 10 years inside of travelers. I now work with the majority of the top 10 PNC companies, as well as some of the life companies. No one is ahead of the game on AI right now. If they are, it's in very niche data intelligence or business intelligence teams. It's not for the company as a whole, and it's definitely not baked into long-term strategy. So when we think about artificial intelligence specifically for customer experience, chatbots are obvious. But what about real-time language translation As a demographics in the United States change, can we make it easier for customers to interact no matter what their native language is? Risk assessment and advisory for individual customers. If they have a question, they're probably not going to wait on hold for 20 minutes.

(08:19):
If it's really complicated, maybe they have an agent. A lot of people don't have an agent. Now for small business insurance or home or auto dynamic personalization, making individual customers, whether those are business customers or residential customers feel like they're seen and their products cater specifically to them. Proactive outreach, instant policy changes. So these are the kinds of things that we should be thinking about when it comes to customer experience and artificial intelligence, data collection and analysis. So I came from Travelers Insurance. As I mentioned, huge legacy company had gone through plenty of M&A, lots of old data systems, kind of scotch taped together like so many big legacy insurance companies or brokers are. And so it makes it really difficult to use that data. But if it was managed well, you could hyper target your customers with specialized information. You could improve your fraud detection.

(09:21):
I know there's another session going on right now around AI and fraud detection and how to ensure that you're complying with regulations, predictive analytics for personalized offers. There are so many things here that we can do. Automated claims processing, dynamic pricing. Now, there are definitely barriers. You can't just suddenly start giving people personalized prices that would mess with the rating tables, but five years from now, seven years from now, will we overcome that? Will regulators change the way that they're managing insurance policy possibly, right? And so if you're not watching that or if you're not preparing for that by the time it's approved, it'll be too late. It'll still take you three or four years to implement. So these are the kinds of things that we should be thinking about today. And then finally, robotic process automation. So there are plenty of companies using RPA right now for really typical baseline processes internally, but we can also use it to improve things like claims payouts, claims filing, changing a policy, policy issuance and renewals, customer onboarding. So this is technology that I think companies right now are struggling to implement. Also, candidly, a lot of companies are afraid to do that because it means that they would need less human workforce in certain areas. And there is a taboo right now around replacing workers with artificial intelligence or robotic automation. There are companies though that are going to find ways to decrease their expenses and become more efficient. So these are the areas that we should be thinking about.

(10:54):
So those are the three areas of technology that we're going to have in our minds today as we're crafting this ideal future experience. The reason we're honing it down is because if I just walked into you today and said, what do you think you could do better in customer service? You'd be like, everything. I don't know. Where would I start? So today we're specifically going to look at these technology areas that everybody is talking about, and we're going to flip it on its head and look at it from a problem perspective instead of using the technology as a hammer looking for a nail, right? Alright, so we're going to go through these five steps together in 45 minutes. Believe it or not, we're going to walk through what the problem is that you're going to solve as a table. So not everybody at the table is going to have the same problem.

(11:44):
That's okay. You can just land on a generic one. We couldn't solve individual specific company problems anyway, that would be collusion. So we're just going to come up with high level examples that everybody is going to solve together today for the sake of going through that exercise. Okay? Then we're going to talk about which of those technologies we discussed could help. I'll flip back to the slides so that you can see the examples. We're then going to talk about the business impact. So aside from improving customer service or improving customer experience, what would the other business impacts be? And then on the flip side, what would the roadblocks be? And then at the end we're going to talk about action steps. And that part you're going to be able to write down on your own. Foresight is meaningless if you're not getting to action. So we want to make sure that we're actually walking away with a next step. Cool. Alright, so let's start with what problem do you want to solve? And I'll walk around a little bit, but take the thing that you wrote down at the beginning, the cohort that you're focused on, the challenge that you're focused on and talk as a table. Which one do you want to solve today?

(12:53):
Okay. All right, I'll walk around.

Audience Member 1 (13:45):
That's okay. I was actually going to make a pin

Melanie Subin (13:49):
To the room. So just a hint, if you're trying to figure out what problem to land on, come up like two levels and that might help you. So for example, if you're talking about how to communicate complicated information, you can come up a level and just say, communicating complicated information or communicating about policy language or communicating about fill in the blank. That way it'll apply to everybody even if you want to come up a couple levels. All right, I'm going to interrupt you. I'm going to do that a lot. Sorry for the next 45 minutes. So for the next two minutes or so, try to orient around a specific high level problem. You can use kind of this insurance funnel that I have up on the slide here. Are you solving for pre-shopping and research? Are you solving for quote and bind, right? Shopping and purchase? Are you solving for claims? So try to orient over the next two minutes or so around sort of a general problem area.

Audience Member 1 (16:54):
The problem is yes.

Melanie Subin (17:07):
All right, I'm going to start walking around. I'm going to see what problem you're oriented around and I do just want to comfort you. So foresight, this process is usually something you would spend six months on, so we're going to do it in 45 minutes. So it's definitely going to feel rushed, and that's okay. It's just for the sake of experience. Can I pick on your table first? What sort of broad problem are you orienting around?

Audience Member 2 (17:37):
Servicing and support.

Melanie Subin (17:38):
Servicing and support. Okay, that's great. Any specific cohort? Like personal, commercial. Okay. Just servicing and support. Okay, that's great. Nice.

(17:54):
How about this table servicing and support? I mean, that's where a lot of the customer experience happens. Yep, that makes sense. Any specific challenge or cohort that you want to target

Audience Member 3 (18:11):
Is more internal.

Melanie Subin (18:12):
So the customer. That makes sense. This is a great example. The customer is internal in this case, and that's fine. It doesn't have to be a person who's buying the policy on the other end of the line, how about this table stakeholder adoption? Okay, great. Okay, that's perfect. How about this table? Any preliminary

Audience Member 1 (18:47):
Ideas in terms of policies and when that human interaction?

Melanie Subin (18:59):
That's great. Yep. Okay, so what to do with the data and how to use it to better serve customers. I love that. Alright, I'm going to move you on to the next pause for a minute. I'm going to move you on to the next step and you're going to hate me at the end of this. I made you do it in 45 minutes. So thinking about the technologies we talked about, we talked about AI and machine learning, we talked about data collection and analysis, and there are some sort of undercurrents in their right, blockchain, IOT, real-time data, and then the third area is robotic process automation. So take a minute and think about which of those technologies might be part of the solution for that challenge. And it might not be all three, it might just be one or it might be pieces of each. So we're going to take about eight minutes for this.

Audience Member 2 (19:59):
So what you really need is you need efficiency. Also verification within the process to say this should pass. It's acceptable, right? Like machine and I just participate on all trends. My team collects on all the biggest barriers that an under makes, and we try training them and we try reminding them they're humans. They're going to make mistakes. If you have that validation logic in there, that would, from an underwriting perspective or my team? Well, it's both. I mean it's automating and infusing automation. Yeah, I mean underwriting platforms that it's like, oh, you're talking about AI ML.

Melanie Subin (21:12):
I also think there's some level architecture around the data validating under guidelines.

Audience Member 2 (21:27):
I can change it going.

Melanie Subin (21:46):
So what remind you

(21:47):
What problem you're solving? Stakeholder adoption

(22:01):
Narrow on one cohort different. So when you talk about agent adoption, about agent adoption technology, that

Audience Member 1 (23:16):
Minutes left.

Melanie Subin (23:17):
Do you guys feel like you're around what technology solutions

(23:20):
thinking about? Okay,

(23:23):
What is the,

(24:10):
I also think about in the data things like synthetic data, synthetic data of real world data that you can exactly, real world data. Obviously doctors don't want to use that, but an insurance company use that. So how could you use different types of artificial intelligence tools like synthetic data to help extract insights from unstructured data input for completely hetero different data input you can't really get? Yeah. What do you mean by that? Synthetic? So synthetic data basically takes external data sources, for example, like demographic data based on internet, and it will segment out data for whatever it's you're looking at. So it might be business building.

(25:06):
So if you have all these different heterogeneous data sources, which every company in the insurance industry does, synthetic data could help to bridge that gap between all of that unstructured unmarriable data to create something that could actually extract. Because if you were talking about how hyper target, I would say you have to figure out how to normalize and standardize the data. But if you're talking about learning, you're talking about insights and how do we get more information out of it, then in that case, summing it up and making synthetic data out of it, analyzing that.

Audience Member 3 (25:51):
But I didn't really own any fine art or anything else. So just that. Hi, we're chatting at this point. Great. Okay. What problem are you solving? Again, we're solving an internal problem of yes, the

Melanie Subin (26:07):
Underwriter territory changes. So the servicing and support, there's a lot of pain. So we said that RPA could

Audience Member 3 (26:17):
Probably

Melanie Subin (26:17):
Solve that.

(26:18):
Okay, what are the sources of the pain for the underwriters? The territory changing guidelines, changing underwriting characteristics,

Audience Member 5 (26:28):
The system, background tables, have all of those things together and

(26:45):
There's error in the process. So I think definitely what you were talking about, but also how can you include validation? You make sure that there isn't anything that's missed. How can you use different artificial intelligence modalities to pressure test and maybe send an alert? Proactive. Proactive.

Melanie Subin (27:31):
Alright, so I've talked with all of you and it seems like you are well settled on the technology piece. So now that you've done that, I want you to think about the business impacts. So when I say business impact, we're all here about creating this ideal future experience. Obviously that's the outcome that we're looking for, but also what would some tangential benefits be? So there are some examples up here that you can use to prompt you. I have examples on the customer facing side. So improved retention, increased satisfaction, seamless experience or some examples, but also on the operational side. So cost savings, operational efficiency, fraud detection. So these are just examples, but think about all the different business benefits that you might start to see from this solution that you're starting to build.

Audience Member 2 (29:36):
How long have a lot of data, claims data combine all of that data, your decisions to, and not only being able to pull, I think definitely pulling in external third party data sources about individual customers. But also, I'll use a social media example, hearing TikTok. Kids that use, I love TikTok. Okay, have you had the experience with TikTok where you go on and a month into it, you're like, I didn't realize I had this personal interest and somehow TikTok knew that about me. I didn't know about myself. Kind of knows that about you. TikTok is able to do that based on unprotected. It's not i I data, it's just behavioral data and it seemingly knows everything about everybody. Think about how much deeper it could be with the actual data that you have to be able to predict and anticipate different risk factors, different comorbidities. So using that external third party data plus using better algorithm. I think life insurance is an area that definitely as long as compliance and

Audience Member 4 (31:14):
That all, yeah, that same thing happened at what was the most recent change

Audience Member 2 (32:02):
There? Some

Audience Member 1 (32:06):
Social

Audience Member 2 (32:06):
Inflation,

Melanie Subin (32:08):
all the social inflation aspects that go into litigation and litigation funding.

Audience Member 2 (32:16):
Yeah, I dunno how to explain it exactly. I haven't been in property that long. I started a couple months ago, so I just want to throw out a thought and then I'll go check on other tables. When you think about business impact, I also was thinking, we were talking about all knock distributors aren't out home insurance people start moving out of Florida. Then what happens to all of the agents who are working in that space no longer have a market? What happens to all of the tangential industries that now are suddenly decreasing business? Sometimes new revenue sources or new information sources, who's winning and who. And then those are usually areas where there's an opportunity to go in, provide or So thinking about who are the winner when the teacher walks over. Yes,

Audience Member 3 (33:31):
Exactly. We we're doing our work

Audience Member 2 (33:35):
Network.

Audience Member 3 (33:36):
You got us started. So

Audience Member 5 (33:38):
Did you get anywhere else beyond the gap? No, we

(33:42):
Started talking about the overall,

Audience Member 3 (33:47):
But agent satisfaction is because they get really frustrated when they call an underwriter and now their territory's changed. And so it's a different underwriter building

Melanie Subin (33:58):
A relationship and what changes from the under changes appetite. So real appetite versus appetite. So insurance appetite for whatever, but actually, so about the underwriter, right? There's one underwriter who really likes that there's another underwriter who doesn't. So having standardized in those data capabilities can mean that there's more consistency in appetite. It's a lot about thinking about the next order we did that. Who would benefit from that? Or if we did that, how would that change the dynamic

Audience Member 3 (34:41):
Stakeholder mapping? Stakeholder

Melanie Subin (34:45):
Mapping, I will tell you, right, mapping, basically mapping stakeholders, the value exchanges and also tedious.

Audience Member 1 (35:17):
We know why you're calling. Yeah. Yeah. And that napkin I wrote number on.

Melanie Subin (35:49):
All right. We've talked for probably over eight minutes about business benefit. I'm going to now ask you to do what is probably the easier side of the equation, which is the roadblocks. So pause for a minute. I love being a pessimist. I love creating catastrophic scenarios. So the roadblocks are always easy. So think about if you were to start to implement this kind of technology or this solution, what roadblocks would you run into? Some examples up here. Leadership skepticism. One leader decides it's not for them. And so that's it. Regulatory hurdles, a lot of the things that we're talking about right now are using technologies in a different way, using data in a different way. And the number one barrier to adoption of these new technologies is fear of regulatory hurdles. So that's another one. The build costs. Great. This will save me money in 10 years, but it's going to cost me a billion dollars over the next two. I'm not going to spend it. So think through what the different barriers would be to actually getting this done in your company and in your solution that you're thinking about

Audience Member 2 (37:12):
After. This one is going to be action steps for you write down. So think about roadblocks also just for yourself. If you're coming up with a good idea and you're talking through this table and you're like, I'd really love to bring this back to my own company internally.

Audience Member 1 (37:54):
Yeah,

Audience Member 6 (37:57):
Yeah. That was with the state when we implemented an new claims system and you kind of realize that you're making their life much easier, but they're used to clicking here and they have to click here and they'll die on that film. And really you're building it. Some of them will get on board, some of them will be able to make that change, but you're really putting it in place for almost your next employees who come in because that's where you see real games where they're like, oh my gosh, this is so simple. I can pick it up in a day instead of a week. And so it definitely can be a fight with that attitude. Planes adjusters typically don't young in Texas, in my experience, at least in Florida, Florida. But because of that,

Audience Member 5 (39:15):
How are your kids?

(39:16):
13 and yes, it

Melanie Subin (39:28):
Friday. It's day two, it's Friday. Maybe you went out last night. Network. Totally. So I'll give you some masterclass questions you can think about while you're thinking about the roadblock. Identifying roadblock, tell for example, obvious roadblock, especially on the carrier side that we always hear

(39:51):
What's working for us?

(39:53):
Why would I invest 2 billion forever in the new system? Nothing is broken. Why would I fix it? So if that's the roadblock, what would your answer to that be? So if I were advising a company, my answer to that would be, some of your competitors, or at least one of your competitors is going to implement this at some point. And if they do and you wait until they've implemented it, you're going to be so far behind. You can't be process. When we're talking about new data systems and implementing ai, I mean these things take years to implement. So my answer, if the roadblock is skepticism, that is giving data, giving answers and examples of why this actually is more urgent than So think through some of those common roadblocks. What? Yeah, and that's actually another too. Even if you actually are giving better experience for whatever you're discussing, if the buttons that used to click were like this button and this button, and now all of a sudden it's this button, this button so die on that hill, it doesn't even matter if process is actually easier, it's different. So another barrier is change management, getting people to be willing to go through that.

(41:21):
How are we doing? Do we need another minute on roadblocks or was that easy for us to fill out? Great. All right. So as you finish talking about roadblocks, I want everybody individually, you can do it as a table, but individually, write down even one action step that you're going to go back to your role with. The reason I'm asking you to do it individually is because we're coming up with great ideas, but obviously if there are competitor companies at a table, you're not going to tell each other what you're going to go do. So feel free to discuss as a table if you have a pretty heterogeneous set of roles. But if not, feel free to write down your own next step. This can be very specific. Who are you going to go have a conversation with? What technology are you going to go research so you know more about it? What example of what use case are you going to build to go socialize and communicate with leaders? So it can be very tactical, but something so that you walk out of here today and then you take that with you. Okay.

(43:27):
Okay. Seems like we're winding down. So I'm going to put you guys on the spot. I'll give you 30 seconds to decide who your spokesperson is. I'm going to walk around and I'm just curious if everybody's willing to share at a very high level what you worked on. I think this is helpful to learn from everybody, but also I've heard some consistent points. So one is nobody started out by saying we're going to focus on claim, but a lot of the tables did get at some point talking about claim and the claims process. So let's chat a little bit about what your ideas were that you came up with and what the solve is, especially to hear what that consistency is. I'm going to hand over my microphone. It doesn't bite, I swear. Can I start with you?

Audience Member 5 (44:17):
Ouch. No, we actually talked through an internal problem where the territory underwriter assignments will change. So then their customers are independent, agents change, and then they have to rebuild that relationship all over again. Then

Melanie Subin (44:39):
Yeah. What was the technology?

Audience Member 5 (44:42):
We originally talked about RPA, but then you helped explain that we could also solve it through AI and data so that we could standardize the data validation part and to do the pressure test using ai.

Melanie Subin (45:01):
Thank you. Thank you.

(45:04):
How about this table next? Anybody willing to take the mic? You've been voluntold

Audience Member 7 (45:12):
The life lady. I love it. So yeah, we were kind of all over the place a little bit, but love the ideas that were generated around the table. So my problem, I guess that we all kind of took on is a underwriting of life policies that tends to be very manual and we do have a lot of rich data inputs that we could use. And my team does the audits of the underwriting of life insurance and disability too after the fact. So we don't do real time. So it's too late. The businesses on the books already. So how could we solve for that? So we talked about AI and data that we currently have and how could we utilize that? And I have some great action plans and takeaways. I do have wheels in motion, but it's the regulatory, like you mentioned, that is the barrier for sure. Thank you. Thank you.

Melanie Subin (46:11):
So a couple themes there that I'm hearing, and I swear I didn't influence every table. So data standardization, using AI to be able to pull insights out of it. How about this table? Anybody willing to take? You've

Audience Member 8 (46:26):
Tom do it. Yeah. Alright,

Audience Member 9 (46:29):
Tom. Alright. I'm not even sure. We talked about stakeholder adoption and then particularly in the agency channel for collecting data and analysis of the data that they collect and how we incentivize them to adopt the new technology to drive better outcomes downstream with underwriting loss, ratio reduction, behavioral changes at the insured level with drivers and how they're becoming better drivers through telematics. And then some of the challenges that we had. And then also driving capacity a lot, particularly in Florida. We talked about homeowners, homeowners, insurance, not being able to get coverage down here as much. So having better data collection, making that agent's life easier so that they can get better coverage for their clients. And then some of the roadblocks was around security. Help me out here, Sam, what were the versus buy versus build versus buy on partner, legal compliance, regulatory challenges, and then leadership not being bought in. Change management. We got a good team here. It's all coming from both sides here. I'm just a puppet. Yeah, so I think anything else? I mean, cool. Good, thanks.

Melanie Subin (48:07):
All right. Well, I'm not going to give away the insights from the last table. I feel like there might be some consistency from that table in this table. Who wants to go, Michelle?

Audience Member Michelle (48:21):
Great. So a few of the things that we talked about was improving not only the servicing experience for the person who's on the phone with our customers, but also how do we improve the customer experience and thinking about being better at predicting what they need before they even get on the phone with us so that we can have that information at the fingertips of the person who's doing the servicing and also looking at talk to text and how do we summarize that information as they start to move through that chain of servicing to make sure that all the information we've been collecting and discussing is available for the next person who might be a touchpoint for them. So we're saving time training and providing a better experience for our customers and hopefully putting the right tools in front of our team members and making it a pleasant place for them to work. We see improved customer service, retention, reduced cost to serve challenges around this is the investment in time and resourcing to stand up these kinds of tools and integrate that into our workflow while we're still doing business as usual. And then really the change management aspect and getting people to adopt and be excited about the changes that are coming. It's one of the biggest challenges we see. And just making sure all the right pieces are in place to be able to be successful in rolling out something new and getting people to adopt.

Melanie Subin (49:44):
Great. Thank you. All right. So how's everybody feel? You spent 45 minutes, you went through a process that most companies do in six to eight months. Do you feel accomplished? So what I took you through today was essentially the foresight process. So in foresight we start with the information that we know. You've already probably had heard of these trends, but also the things that you don't know. So some of the things that you tackled today around the regulatory hurdles. Another thing that I heard is in the life space, people wearing wearables, being able to collect data from that, that's also highly relevant in the property space. When we think about things like parametric insurance. So these are uncertainties. We're not sure how these things are going to unfold, but they're not unknown unknowns. We know that regulation is being suggested around a certain area, or we know that technology is being developed in a certain area.

(50:45):
Most companies in the insurance segment are very responsive. They react to the things that happen rather than thinking proactively. But these solutions we're talking about take years to implement. So acting reactively is not helpful in a new paradigm where technology is changing this fast. So we have about five minutes left in the session. I just want to open it up for any questions about the foresight process, questions about the technologies that we discussed, questions about how companies start to do this work. Anything I can answer for you? I almost want to walk out of the spotlight so it feels like I'm more with you. Any questions I can answer about what we did today?

(51:31):
Yeah,

(51:34):
I'll walk around. It's just loud in here.

Audience Member 11 (51:37):
Thanks. I was just curious, we were all coming from different perspectives here in this group, but in a typical engagement, how do you figure out what the problems, what's the most critical problems that the team should be working on and kind of figure out what's the best ROI for? I'm curious about that. We got a lot of problems at our company.

Melanie Subin (51:59):
Yeah,

(52:00):
What company are you from? Oh, Data Crest. So usually the problem set is identified based on who comes to us. Usually almost all of our work is from people who've heard of us and then they come to us. So if it's a product team, they want to know how to build a better product. If it's a strategy team, they want to know how to build a better plan. So usually the problem set is very biased by who's coming to us. We try to counteract that by looking more broadly, which everybody should be doing. So looking at all of the different challenges within the company and then prioritizing them based on, there might be a problem that one particular team feels like is the end of the world, but it only impacts 2% of the business. And there's another problem that no one is talking about because it's the elephant in the room, but it impacts 90% of the business.

(52:50):
So a lot of our work is kind of, it's like therapy, pulling out the threads to figure out what's really happening and what is the root cause of the challenge that we're facing. I'll be honest, most of the time the challenge is internal hesitation or leadership skepticism. So the thing about the work of foresight, and we don't do any digital implementation, which means we're totally unbiased. If we recommend that a company do something in modernization or transformation, we have no skin in the game. A lot of the work that we do is around change management and getting leaders to think differently and more proactively. And the way that we do that is by identifying the different challenges that a company is facing and then presenting them, almost like holding up a mirror to them to say, here's actually happening outside of your company and in the industry and in competitor firms. And so here are the things that you should be thinking about. So the problem set kind of depends on who comes to us, but if we do our job well, we get outside of that too. Yeah. Yes.

Audience Member 12 (53:55):
I assume it was developed within the academic area. I mean, do we have case studies, white papers, or we can see how the implementation of that is. Excellent. I'd like that piece.

Melanie Subin (54:05):
Yeah. So the question was this term foresight, where did it come from? The assumption is that it's the academic space. It was actually war gaming. So the process of foresight came out of just standard tabletop exercises in the mid 19 hundreds around various different war efforts between World War II and then Vietnam. And so the process is very much about what do you know, not know, and what do you have to plan for and what might the next order implications be. It was then adopted into business at Royal Dutch Shell. So shell the oil company. There was a team within Shell that started using foresight, and they used foresight to anticipate a lot of the different impacts in the oil and gas industry, the oil shock in the 1970s. So foresight actually has its roots in business. However, I think in the past maybe 10 or 15 years, or maybe more than that strategy in businesses has become very much about annual planning, taking the things that were on last year's annual plan that didn't get done, and just copy pasting them onto next year's annual plan.

(55:14):
So there's been kind of this lack of true strategy in companies in all industries for the past decade or more in the past six to eight months. What I've seen is suddenly a lot of companies either naming chief strategy officers for the first time, but putting them in digital or making them chief strategy and transformation officers. So now it's not a better pendulum swing. Now it's like strategy is all about digital transformation. So foresight has its roots in business, but it should be applied across all aspects of the business. Yeah. I can chat with you after and send you some resources if you want. Yeah,

Audience Member 13 (55:52):
Great presentation. So thank you. Question around, once organizations finally decide to implement something related to AI or some modernization of their processes and systems, what do you see typically as the problematic issues or roadblocks within the organizations that pop up as they try to implement these solutions?

Melanie Subin (56:20):
So I think there are some tactical, I'm just going to check the clock. I think there are some tactical roadblocks, things like regulation or we can't get to the data we thought we were going to be able to get to, or it's going to be much more expensive to implement than we thought it would be. So the tactical roadblocks are kind of the anticipated ones. The biggest challenge and problem that I see, whether it's AI or some other kind of technological transformation is that once the decision is made to invest in it, it's assigned to an owner. So there's a person who then owns that transformation, whether it's the chief digital officer or the chief strategy officer or whatever it is. And the problem then is that because of the ways leaders are incentivized, it becomes a not my problem kind of situation. So then that leader who's been given the authority to implement that transformation is not getting cooperation from their peers because it's going to take away my team's efforts from our day-to-day that we need to focus on, or it's going to cost us a lot of money in the short term.

(57:21):
And my business unit didn't sign up for that. And so the counter activity to that is having a leadership team at the c-suite level that is fully aligned and that is holding its next level of leaders accountable to working together and setting that expectation, which frankly I don't see in most companies. Yeah, just a tiny little, super easy to solve problem. Yeah. Alright, we're pretty much at time. I thank you for your time and your energy. I know this is a very cognitively taxing exercise to go through in 45 minutes. My hope was to expose you to the process and hopefully you took something away that you can bring back to your office. So I'll hang around for a few minutes, but it was really great to meet all of you. Great to hear your ideas, and thank you again.