The ability of a company to unlock value from its own data is at the heart of many business transformation trends today, from generative AI to ESG reporting, the future of work to more adaptive financial management. Two business automation founders who have made it their mission to help companies build better data sets go on call with Paulo to talk about how to unlock this value and more. ⁠bluesheets⁠’ CEO ⁠Christian Schneider⁠ and COO ⁠Clare Leighton⁠ return to the show to not just share updates on the impact they have had on their customers but also what they’re excited about with use cases like ESG reporting and generative AI as they position themselves to be the data infrastructure layer for these applications. See ⁠open roles at bluesheets and check out ⁠our first call with them⁠!

Timestamps and Highlights

(02:24) Bluesheets a year on: more workflows, more man-hours saved, more cost savings, and their mobile app launch;

“Having rolled out across a number of corporate POCs and now onboarded as clients, we’re talking about say, financial institutions, financial service providers within that say insurance companies that are processing in the tens of millions of claims per annum that we’ve now automated and we can point to an average of a 60% cost reduction in some of those cases, which is just huge. That puts our automated dollars in the millions saved there.” – Clare Leighton

(08:31) Learnings from enabling digital transformation for a multinational insurance company;

“Having that data infrastructure in place is key and that’s going to be a deciding factor as to who’s gonna own industries in the future, it’s going to be those companies who have the best data sets, the richest data sets, the ones that are most complete with the least anomalies and incompletion in general. So having that coherent sort of data set is going to elevate our customers and we very well understand that. And I think in this particular client case that was mentioned, the client really understands that as well. So they’re really looking into digital transformation from that perspective.” – Christian Schneider

(14:17) How to leverage data sets processed and data ops enabled by bluesheets: ESG reporting and Generative AI use cases;

“So the first piece being we provide our solution that actually does the data processing, provides the automation and efficiency gains. There’s a really tangible bottom line saving there and an improved customer experience, but following that, that organization then has a really robust data set at their disposal.” – Clare Leighton

“…we have seen that the data we process and the data we bring to life can be used in various different ways. The one to many approach in terms of sharing that data, maybe with an internal legacy system works just as well as sharing it with an external database…Since we’re pulling together data from many different sources within the company, it’ll open up new use cases for the company to do ESG reporting.” – Christian Schneider

(22:23) Pioneering the orchestration layer for enterprise Large Language Models;

“What’s more important here from the enterprise side is that when you’re addressing pain points inside the company and you’re relying on data sets from inside the company, some of these little [reinforcement learning] hiccups that we see right now can be sorted out much quicker. And I think that’s definitely part of where we’re excited to see ourselves. We want to create these robust data sets which will allow for easy model application and also again limit the exposure to any kind of hiccups that we might see from the outlet provided by this model. And so that’s where we feel we can come in strong. So data infrastructure plays a part in this.” – Christian Schneider

(33:50) #MinuteMasterclass: Digital Transformation for Enterprise Leaders 101;

“…a lack of scope and definition of the project tends to be the best way to derail something. So with scope and clear metrics, you also have the opportunity to define responsibilities, timelines, and so on as a follow on from that, which makes it much easier to execute and actually achieve success in those transformations.” – Clare Leighton

(36:39) #RapidFireRound;

About our guest

Christian Schneider, bluesheets CEO and co-founder, started his career in investment banking and consulting in Europe then venture building with Rocket Internet in Singapore, managing business intelligence for foodpanda, then co-founding Singapore-based foodtech startup DishDash.co, where he began working with Clare as it expanded to Australia.

Clare Leighton is the bluesheets COO and co-founder. Before first working with Christian as country manager of DishDash.co, she was previously part of the launch team of UberEats in Australia then later the head of Uber’s APAC People Operations.

Transcript

Bluesheets a year on: more workflows, more man-hours saved, more cost savings, and their mobile app launch

Paulo J: Yeah, so I just wanted to make sure our listeners are caught up to speed in terms of where bluesheets is, and especially for folks who are listening in for the first time, hadn’t had the chance to catch our first call. 

Maybe you can share a little bit of where bluesheets is now. And I was thinking you could do that in maybe three numbers that you could share, or if you feel that three numbers isn’t enough, maybe you can just add a little bit more there. 

Any particular numbers that you’d want to highlight could be about the problem or some of the success that you’ve seen or some new trends that you’re seeing in terms of the market? 

Clare L: Maybe just quickly, so a couple of metrics and a couple of numbers. Just to really summarize, as you said, it’s been almost a year, so we’ve been very busy since then. A couple of things that are really exciting as we look at the metrics and numbers that we’re focusing on.

Number one is the multiple connected systems that our clients are actually using. Something that we really love about our product is that once we’ve processed the data, we can make it available for clients to use multiple different workflows. 

So for us, a key KPI number is actually the number of exports, and currently we’re sending an average of three simultaneous exports per client, which means that for one workflow, one data set, they’re actually able to operationalize it in multiple different ways at once, solving more than one workflow with our automation. So that is just fantastic to see, and that number is increasing. 

The other that we really do like to focus on is hours saved. So some of the ways that we can actually quantify the value that we’re adding within an organization is of course, the man hours replacing manual processes and double handling of tasks with, of course, automation and our solution. So workflow automation and what we can provide with our digitization there. 

So we’ve implemented our solution across a number of corporate bank offices since we last spoke, spanning everything from insurance claims, credit application processing, account payables and receivables workflows, and we’re now banking thousands of hours saved just for those clients in that few month period.

And then lastly, just the third is cost savings. I think the other big quantifying number here is the bottom line that we’re actually saving our clients when it comes to automation. So in terms of ROI on these projects, that is the number one KPI that we do align on as a metric for success. 

So having rolled out across a number of corporate POCs and now onboarded as clients, we’re talking about say, financial institutions, financial service providers within that say insurance companies that are processing in the tens of millions of claims per annum that we’ve now automated and we can point to an average of a 60% cost reduction in some of those cases, which is just huge. That puts our automated dollars in the millions saved there. 

Paulo J: So exports, hours saved, and cost savings. So all these things really point to some of the pain points that you guys discussed actually in our first call of really increasing productivity of teams that traditionally would’ve spent a lot of time doing all this data management work and also eventually because of those hours are saved. Then you have all these like cost savings as well.

And I also like how you touched on the exports. Because I think one of the other things that we talked about last year was also how a lot of the existing software can be really hard to integrate and companies can have a hard time sort of plugging those things  into their operation. But it seems like you guys have certainly been proving to be efficient in that area. 

And something that I want to discuss later on is also some specific stories that you’ve seen with some of the clients that you have. And I know you guys have shared a little bit of case studies as well on your LinkedIn page, so you can talk a little bit about that.

But first, I also wanted to spotlight the fact that you guys also did a mobile app launch. I think for a lot of B2B SaaS companies, mobile apps aren’t something that you necessarily, you know, come to think of as a go-to-market. But I wanted you to just briefly discuss what this means for the overall ecosystem for the user experience and growth for bluesheets moving forward.

Clare L: Sure, I might quickly start here as well. Love that you’ve actually used that term ecosystem, Paulo, which is exactly part of the value proposition of adding a mobile app experience to our solution. But what we recognize is that in financial workflows in these functions, it is not limited to one business unit or one particular team.

There’s absolutely a collaboration component, whether that is internally within an organization. You’ve got financial controllers, accountants, you’ve got department heads, operators, approvers. That’s a whole suite of workflows in itself. And then you look in other B2B workflows where you have procurement. 

So you’ve got external vendors, you’ve got third parties where a single financial document or workflow actually transacts through many entities. So that collaboration component was super important. 

The other part of that is that for our clients and users of the bluesheets solution, they can increase their value, the more data they can actually process through our system.

The more data comes in, the more value we’re able to add. That’s exponential when we look at multiple data sources in multiple workflows on the way out as well. So being able to make it even easier for our users to ingest and access those different features and functions on the go. So depending on the scope with which the user actually needs to access our system. And having access to all of that functionality on the go as well. Things like approvals, communication at their fingertips. 

Christian S: I may just add here, that we for sure also see ourselves as a bit of a pioneer product in the automation space tackling the problem differently than many other companies have in the past.

And automation doesn’t start usually only with an IT team, for example. It may also start on the ground and I think the mobile app gives us an avenue here to engage different teams within the company very easily. And as Clare said opens up new channels for us to ingest data. And data is what we like, so I’ll just sum it up like that.

“Having rolled out across a number of corporate POCs and now onboarded as clients, we’re talking about say, financial institutions, financial service providers within that say insurance companies that are processing in the tens of millions of claims per annum that we’ve now automated and we can point to an average of a 60% cost reduction in some of those cases, which is just huge. That puts our automated dollars in the millions saved there.” – Clare Leighton

Learnings from enabling digital transformation for a multinational insurance company

Paulo J: I remember you guys also talked about in our first conversation about how bluesheets really sets itself apart by being able to really focus on offline unstructured data, which is prevalent here in Southeast Asia. 

And I think you guys definitely emphasize that the point of having that mobile app makes it a lot easier, again, for the ingestion and being able to snap pictures of receipts or any other documentation that has the data, which you guys like to have on your platform. 

And on that note, I wanted to move into some of the case studies and for you guys to share some of those stories. Maybe even give some sort of behind the scenes as to how using bluesheets really helped their digital transformation. 

What are some of the best practices that you’ve seen with respect to using your platform and how are they able to cascade implementation top down from leadership to, as Christian also talk about, people on the ground, front liners, that kind of thing. So any particular customer that comes to mind or story that comes to mind? When it comes to bluesheets? 

Clare L: And I might start and then hand over to Christian on this one because it’s something that we’ve now covered across multiple geographies, but the client that comes to mind as we talk about the best practices and impact would be one of our insurance customers.

We’re working with a multinational insurance provider MSIG, and you mentioned Paulo, that we’ve covered some case studies on our LinkedIn and on our blog and our website. And this is one absolutely that we’ve covered and has been covered from their side as a bit of a digital transformation success story within their own team which is great from our perspective because really we are trying to work with financial institutions, financial service providers that are looking to be innovators and leaders in their industries.

And for us, that means streamlined solutions which go hand in hand with our solution being a very scalable product. We are language agnostic, currency agnostic. So when we look at a provider of insurance like MSIG who cover seven different countries, based out of Southeast Asia, but cover multiple countries servicing different markets, we really want the opportunity to land and expand within those organizations.

So MSIG Insurance, we did actually start with– have started with since we’ve spoken. They had an immediate turnaround in terms of cost and time to process savings are quite significant, which we’ve averaged around 60% in cost savings across hundreds of thousands of claims processed since we last spoke, which is, of course, a huge bottom line impact.

But when we look at them as a use case and kind of best practice that we like to point to, thanks to the flexibility of our engine, we were really simply able to run a POC. It makes it super easy for bluesheets to come in with that flexible ingestion that we’ve talked about and actually start processing of a workflow, whether that’s between cloud applications, legacy systems, and of course, as you mentioned, that pesky offline component that is very persistent. 

And this is the same across –this is an insurance claims example, but it’s the same across financing. So loans, mortgages, customer credit applications, accounts payables, accounts receivables, it’s all that same kind of bucket of you’ve got offline data, you’ve got online data, you’ve got things coming out of client facing products as well as legacy systems, that all needs to be pulled together, digitized, structured categories, and pushed through those workflows. 

So for MSIG, that was a really straightforward implementation to run the POC, see some immediate return again, on those metrics that we talked about– time and cost savings, as well as the actual operational efficiency of those new workflows. And we’ve since been actually kicking off the rollout of that across other geographies with those teams. 

So for us, really a best practice in terms of digital transformation are those core KPIs and the process that we’ve been able to follow with them. But from our perspective, just the pure metrics that we’ve been able to deliver there is super exciting. 

Christian S: What Claire has kinda led onto is we’re building data sets for clients. We’re pulling together data from various different sources and whether that’s online, offline, legacy, general database.

And it’s a great segue into what we’re gonna be talking about later. If GPTs which have an ever growing hunger for data, every one access to meaningful data, having that data infrastructure in place is key and that’s going to be a deciding factor as to who’s gonna own industries in the future, it’s going to be those companies who have the best data sets, the richest data sets, the ones that are most complete with the least anomalies and incompletion in general. So having that coherent sort of data set is going to elevate our customers and we very well understand that. 

And I think in this particular client case that was mentioned, the client really understands that as well. So they’re really looking into digital transformation from that perspective. Creating those data layers that have a really extreme level of detail when it comes to pulling data from different sources. And whether that is online or offline at that moment, it doesn’t even matter. They need a partner like bluesheets to make that happen. And so we have all these capabilities that they’re looking for. 

Paulo J: I actually wanted to ask a follow up question there with regards to maybe the sales all the way until like customer success. What have been the learnings in terms of working with a client as big as MSIG given that they have operations in different countries and to an extent you mentioned that maybe that doesn’t really matter in the sense of bluesheets being really an agnostic platform in that sense.

But have there been any learnings in terms of how you like to try to sell this kind of thing and try to get them to adopt it easily or maybe check in on them in terms of how effectively they’re using it? 

Christian S: There have been a lot of learnings. And I think maybe if I point to one of the most important ones, I think what we feel is going to be our strength moving forward is we’re able to lay out a solution to the client that is very tangible from the beginning.

So the value the client sees, it’s immediate. That is a very important learning for us. And then of course we will have to make sure that every step along the process, whether that is going into a POC, whether that is doing a user acceptance test with on the ground users or whether that is on the go live stage.

We have to be very prepared and we’d have to have the final results laid out. There can be no ambiguities and we want to present our product as if it is ready to go anytime at all times. And I think that’s definitely our key learning, but that will allow us to roll out faster, get the different departments signed up on the idea very quickly as well, that tangible value from the start is going to be our sort of north star when it comes to onboarding large customers moving forward.

“Having that data infrastructure in place is key and that’s going to be a deciding factor as to who’s gonna own industries in the future, it’s going to be those companies who have the best data sets, the richest data sets, the ones that are most complete with the least anomalies and incompletion in general. So having that coherent sort of data set is going to elevate our customers and we very well understand that. And I think in this particular client case that was mentioned, the client really understands that as well. So they’re really looking into digital transformation from that perspective.” – Christian Schneider

How to leverage data sets processed and data ops enabled by bluesheets: ESG reporting and Generative AI use cases

Paulo J: Moving on to another topic, I wanted to ask also about what are the — I know in our first conversation we talked about a lot of the fintech and finance use cases. And we also discussed a little bit about, how you sort of balance looking at Southeast Asia clients versus global clients, and certainly you’ve expanded your clientele over the past year.

What are some of the more interesting use cases that you’ve seen evolve out of the way that businesses have been using bluesheets over the past few months?

Clare L: Maybe just to quickly speak to that. We do absolutely have a growing number of countries on our user base, which we love. So while we have focused market effort in APAC, we have started to see that traction– that, I guess, just the really, the dawn of that traction coming now from other markets, including Europe and the States where we now do have clients onboarded on the solution.

So we are really excited, one, of course to continue our growth in APAC, but also start to look to those expansion markets, especially given the inbound interest that we’ve seen. The evolution and the kinds of use cases, really, I think the exciting thing for us is that the use case might look different.

It might be a different data set, it might be a different departmental workflow, but the process for our platform and our engine is more or less the same every time. So super scalable and it’s really interesting to see how in different markets, in different industries, you know, we can apply that in where the most value is being added.

But I think honestly, there have been no major surprises in the kinds of use cases and where this is evolving too. So within financial institutions, financial services providers, this is everything from bookkeeping and transaction processing to expense claims, loans, financing, even identification, KYC processing.

Anything within that sphere where we have this complexity of data that is offline and online sitting between legacy systems and new unit cloud applications. It’s a really straightforward use case. I think what we see in terms of evolution that is most exciting that Christian actually mentioned earlier, is that data play.

So the first piece being we provide our solution that actually does the data processing, provides the automation and efficiency gains. There’s a really tangible bottom line saving there and an improved customer experience, but following that, that organization then has a really robust data set at their disposal. 

The nature of the platform is that we get this really rich data set as we process. And that is all then of course, digitized, stored on the cloud and available for them to go back through and really understand the data within those workflows, whether that’s customer behaviors, whether that’s actually around their own metrics in the products that they provide is a super valuable opportunity for them there and how they use that data. 

Obviously we’re gonna talk more about applications for that data down the track. But I think the kind of, the awareness that we see in our clients, actually wanting to apply that data and use it in different ways other than just the– strictly automating the workflow is really exciting for us.

Paulo J: I actually wanted to lead into that part of the use cases where, what happens once you have that robust data operations and all this data like sitting on the cloud now more readily available for an organization to use. What are some of the use cases there that you’re most excited about?

Christian S: Yeah, definitely. I’ve been very excited about use cases around ESG reporting. I think we have seen that the data we process and the data we bring to life can be used in various different ways. The one to many approach in terms of sharing that data, maybe with an internal legacy system works just as well as sharing it with an external database.

And in the ESG use case, it is an external database. It will allow you to calculate your CO2 emissions based on data that we fed into the database, right? And so that’s a very interesting use case for us. Since we’re pulling together data from many different sources within the company, it’ll open up new use cases for the company to do ESG reporting.

Sure there are some sort of — there’s a few of those that will be coming to mind first, which probably have a very high impact. But then downstream there is a lot more that can be done. And I think that’s what we feel very excited about, seeing these first use cases come to life.

And actually we’re in the midst of linking up our public products, which are geared towards mid sized to small companies. Also linking that up to ESG databases, which will allow us to provide ESG reporting for companies of any size. And I think that’s a very exciting topic. And whether that is just for awareness purposes at first or whether that has any further applications down the road, I think we will leave that to the companies themselves.

But we do know that for large companies that’s the mandated feature nowadays, so there will be the first tangible use cases and then we will see how the larger markets and the long tail would pick it up as well. But we’re very excited about that topic. 

Paulo J: I’m glad you mentioned about ESG, because then giving that capability to a lot of organizations, especially large organizations that are mandated to do this kind of reporting can actually move the standardization of these practices forward and make it a lot clearer to a lot of other regulators and businesses about how to do these things and implement them on a wider scale. 

Really exciting to see, and maybe when you guys get back for another catch up call can talk more about how these things have pan out, especially in the ESG use case.

Another use case that we talk about off-call, and I’m sure a lot of our listeners have been itching to hear about since the start of this conversation has been generative AI and large language models since the foundation of all that is at the end of the day data which you guys thrive in. So what does generative AI mean to bluesheets at this point in time? 

Christian S: I can tell you that we’re extremely excited about the topic in itself. I think what we see is a massive opportunity for us to incorporate LLMs into our system. And these LLMS, they will be provided as foundational models and I guess it will be the usual suspects, so these will be the cloud providers. 

I guess we can’t really wait for them to actually open them up in a bigger way. I think some of the LLMs or the models themselves, they have to be attuned a little bit more towards enterprise use cases before we can actually operationalize them to a great extent, but we’ve seen some great progress there.

And just to give you a bit of a sense of where we stand in the evolution is, in our opinion, I think Microsoft has just released numbers that for Q3 they’re expecting 1% of their revenue, cloud revenue, to come from AI and related fields. So now that’s not just LLM related. That means a very small fraction is currently being operationalized, and we understand that.

So I think when we talk to our corporate clients first and foremost, we try to deliver the message that we’re — that nobody’s too late on the topic right now. However we’re discussing with them the first POCs already at this point. So there have been several that have come across our desk, and I think in general the topic is very exciting.

I just listened to a podcast where I heard a very interesting analogy between automation and AI in general, but also related to foundational models. So automation used to be building a staircase to the moon one step at the time which is a very tedious process.

And now thanks to new technologies that we can be tapping into, whether they are already provided on the cloud as a service or whether those are clean APIs, now we can add skyscrapers to our staircase at the time, right? So there’s still no sort of rocket access to the moon.

But eventually right now we’re greatly enabled to build better products. And from an automation perspective, also just really supercharged where we can go and how we can get there. So I’ll say as a summary here, we’re very excited. And I think we can’t wait to have actually the first sort of successful POC with our enterprise plan completed. 

“So the first piece being we provide our solution that actually does the data processing, provides the automation and efficiency gains. There’s a really tangible bottom line saving there and an improved customer experience, but following that, that organization then has a really robust data set at their disposal.” – Clare Leighton

“…we have seen that the data we process and the data we bring to life can be used in various different ways. The one to many approach in terms of sharing that data, maybe with an internal legacy system works just as well as sharing it with an external database…Since we’re pulling together data from many different sources within the company, it’ll open up new use cases for the company to do ESG reporting.” – Christian Schneider

Pioneering the orchestration layer for enterprise Large Language Models

Paulo J: I think what’s really interesting is that I guess regardless of whether you guys knew or not that Gen AI was gonna be hot this year from the time that you guys started out this company, like bluesheets is already well positioned, like you’re not really having to change anything about the company to actually, really tap into this space.

I know Christian you set up that analogy of staircase versus skyscrapers to the moon. Maybe you could be a little bit more concrete in terms of what that means in terms of where bluesheets is in terms of the LLM value chain, right? Like in terms of the process for a company to set up their own, say for example vertical LLM or industry LLM like, I guess like Bloomberg has done right with their own GPT thing. Where are you guys situated? 

Christian S: Let me just quickly start maybe one step back here. So I think we see this as like an industry that unfolds into sort of three different categories. So one are the foundational models in themselves.

So they’re gonna be provided by the large players. You have the cloud players, they’ll have their own language models. You’ll have the guys like Bloomberg who released one, Salesforce, for example. So we’ll see these come up. And so bluesheets isn’t competing with these foundational model players in any way.

Where we see ourselves is on the next stage, which is more or less that infrastructure play or basically we’re trying to provide a solution to orchestrate LLMs in a better way, whether that is helping our clients to tune those LLMs, hopefully made available through the cloud. Or whether that is for us just to really enhance the data sets that those LLMs can ingest. And so that’s where we see ourselves right now. 

The third layer, by the way, is the application layer itself. So how it actually surfaces to the end user. And I think it could very well be that we also build some capabilities there. But first and foremost, I think we’re on the orchestration layer and that’s where we want to provide solutions for our clients, tangible solutions.

In general, like here, we’re at the very early stages of this. As of right now, we’re using several different techniques to prepare data for our clients, natural language processing and various machine learning techniques. But as I said earlier, we’re still at that stage where we’re discussing the first POC with our clients on how we can actually make use of LLMs and feed our data sets into a tuned version of an LLM.

And I think in general, the industry is still early in that regard. So I think enterprise is currently in the stage of understanding and figuring out how to best make use of it. The way that we will be hopefully surfacing this technology mostly is unlike what we’re currently interacting with — so right now, most people understand these LLMs in the form of a tool to converse with a natural language, like obviously ChatGPT is that application that we are referring to here. But I think we’re gonna be changing that modality to this technology quite a bit in the future.

So it’s not about having a natural language input to process into that tool. I think for us, what it is about is going from an automation perspective, say from one step at a time to have a user easily activate several steps at once by making use of that technology. So here you can probably, thanks to the technology, skip many steps in the middle.

It will still require us to be, and stay at that level as to orchestrate these steps. Like we have to make sure that we do have the integrations to the legacy systems. We do have to make sure that we have security layers in place and all of that. So that’s gonna be us. We’re gonna make sure of that.

But the actual sort of manual component that comes in, it’s a starting point, for example, of a chain of events. I think this can be greatly enhanced and it will supercharge thanks to that technology. I think that’s what we’re going to see in the next few years. But this opinion might change eventually as well.

Paulo J: ChatGPT is definitely the modality that a lot of us are used to, but certainly, especially as we see more API integrations there are a lot more applications where the user doesn’t necessarily have to think anymore about the prompt and can just select from a menu because you already see the particular use cases that the organization needs it for.

You talked about tuning and orchestrating how the organization uses its data and builds its own language model, for example. What are the particular pain points in that area that you’re trying to solve or maybe that you wanna prioritize over others?

Christian S: First of all, we can all be very grateful that companies like OpenAI have come out and released their first versions of this to the world knowing they weren’t perfect yet in the sense that people respond, they’re getting awkward behavior back from the engines, they’re releasing and so on and so forth. So to be fair this is — these are the early sort of hiccups that will stay with us for a while. And it all goes back to reinforcement learning.

There is no real way of doing this, right or wrong. If you listen to the people at the very top of this company, they’re admitting that right now it’s not even clear if the way that we do reinforcement learning right now, which is why human-backed is the most scalable and perfect way. 

And so when it comes to the consumer side, I think I’m — I would say we’d be a little bit less bullish about how fast the industry can evolve into something that doesn’t have to be immediately regulated.

However I think what’s more important here from the enterprise side is that when you’re addressing pain points inside the company and you’re relying on data sets from inside the company. Some of these little hiccups that we see right now, they can be sorted out much quicker. And I think that’s definitely part of where we’re excited to see ourselves. 

We want to create these robust data sets which will allow for easy model application and also again limit the exposure to any kind of hiccups that we might see from the outlet provided by this model. And so that’s where we feel we can come in strong. So data infrastructure plays a part in this. 

Another part is just in general helping to bridge the gap between what’s currently being connected and integrated in terms of automation. So that’s always gonna be an issue for corporations. You have different sorts of buckets, different data sets siloed somewhere, and it’s not easy to actually connect them or combine them. And I think that’s definitely where we want to come in and provide that relief as well.

We can call those data lakes. You can also just call them enhanced data sets. I think that’s where we see ourselves come in and then ultimately link them up with the LLMs. That’s going to be hopefully also what we will be great at and use their APIs to a great extent. 

What I said earlier is true. Like in many ways, we’ll be relying on the foundation models to be made available in the cloud. And I’m not — I’m not questioning it, it’s probably still a little bit too early for everybody to understand as to how easy it is to just quickly apply. 

It’s via an API that’s made available by say, Microsoft or AWS or Google. I think here it’s an exploration stage right now. And we’ll be one of the first — hopefully one of the first tools that can run the first POCs. And if they’re successful, then that’s gonna be great for everybody, us and our clients. 

Paulo J: Clare, I wanted to ask also in terms of like how this might — I know I mentioned earlier that bluesheets lends itself readily into this kind of space, but how do you see bluesheets of operations having to adapt?

As you guys earlier mentioned about running POCs and once that shows some promise, then obviously you’re gonna roll that out, right? So how do you see that impacting bluesheets’ operations down the line?

Clare L: Yeah. So I think it’s a starting point, as Christian said, we are very much in this exploratory phase and for us, I feel like we’ll be pioneers in this space as that technology does become more available and deployed to cloud and being that infrastructure.

Of course, when we take a look at the kind of client base that we serve, we have a huge number of considerations that are a little bit separate to the current GPT application where we can go out and automate anything which way. When we look at financial institutions, of course the first thing is security.

So for us being a player in this exploratory phase, we will really be focusing on some of those core capabilities that we already have. But nothing is going to happen in this space without assuring things like data security and data privacy. And when we talk about our operational capabilities adapting — look, I think we’re gonna learn pretty quickly from the first few POCs we start to implement with this kind of solution.

When we think about scaling our operational capability, I think honestly, I’m more excited than anything because this — while it’s an increase on the degree of automation we can provide for a client in their operation, there’s also applications for our own efficiency internally. So selfishly from an operations perspective that kind of increases our internal operational efficiency and margin as well.

Paulo J: I think there’s definitely a lot to be excited about and I’m sure we’ll have a lot to catch up on, especially in terms of the POCs in a future episode. But I’m definitely looking forward to it. And I think for large language models, that’s certainly one way to leverage the data that you have.

But then I think that there’s other themes or other use cases that we can definitely talk about. We just talked about ESG reporting as well. So there’s definitely a lot of things that you guys can do to be able to help companies really leverage their data.

And Clare talked about how LLMs and operationalizing this could also improve the internal efficiency of bluesheets as an organization. But today I wanted to ask how Generative AI impacts your own personal productivity as leaders and even within the organization of bluesheets?

Any particular use cases that yourselves are finding, in terms of the tools out there or any tools that you’d recommend as well? 

Clare L: I’m very much using it in novel ways at the moment. For me, I like to understand and see people make their early mistakes and I’m more excited to follow along with what’s happening. But it’s a very novel application for me at the moment. It’s not something that we are actively applying internally at this point, but Christian, anything exciting that you’ve done so far? 

Christian S: I’ve played around with a couple. I’ve definitely felt if you’re starting from scratch with mostly anything, there will be a tool out there that will enhance that and will get you from step one to step 10 very quickly, whether that’s creating any kind of document of sorts related to legal or whether that is creating a presentation.

It’s this co-pilot idea. I think that is a very valid point for consumers where we will see a lot of AI enabled sort of services that will act as a co-pilot for almost any profession out there. And I think looking at the VC landscape, most of the money is going into tools like that already.

The foundations of it, talking about vector databases, for example. So I’m very excited about what’s to come. At this exact moment in time, I’ve played around with the technology, I think it’s exciting for my day to day work. I think it hasn’t really shone through just yet, but again, this is going to be happening I think in the next few months. I’ve already seen large funding rounds with the first products launching. So I’m very excited about that. 

“What’s more important here from the enterprise side is that when you’re addressing pain points inside the company and you’re relying on data sets from inside the company, some of these little [reinforcement learning] hiccups that we see right now can be sorted out much quicker. And I think that’s definitely part of where we’re excited to see ourselves. We want to create these robust data sets which will allow for easy model application and also again limit the exposure to any kind of hiccups that we might see from the outlet provided by this model. And so that’s where we feel we can come in strong. So data infrastructure plays a part in this.” – Christian Schneider

#MinuteMasterclass: Digital Transformation for Enterprise Leaders 101  

Paulo J: I definitely like what you mentioned about the co-pilot analogy. And if AI is a co-pilot, bluesheets are the training schools for these pilots to actually fly out there and do their job properly. 

And I think one other thing that I really found interesting so far in our conversation is that, I guess for enterprise in particular, what you’re optimizing for would be quite a lot different from what the sort of the end user use case is optimizing for. For us, when we ChatGPT, we want speed, convenience, all of that. 

But as Clare mentioned earlier, for enterprise, you’d want to focus on security and privacy, all those things, right? So definitely other variables that you’d want to optimize for differently.

On that note, I wanted to move into a new corner of our show. I don’t think you guys experienced this since you started it this season. So we’re doing a minute masterclass corner where you can do some quick sharings for organizational leaders, CEOs, all of that across different functions and across different topics. And since you guys specialize particularly in digital transformation, data processing, and workflow automation. 

If you were to give a masterclass on that topic, what would be the key takeaway you would want to have the CEOs attending your masterclass leave with when it comes to actually adopting automation software for their organizations?

Clare L: I think this is a big one to handle in a minute masterclass, but if we were to really simplify it down and I’ll have a first crack at this. I think really the CEOs and leaders that are tackling digital transformation projects, the key thing we’d want people to take away is defining scope and defining desired outcomes.

And we see this a lot in terms of the breadth of automation that we can provide and varying degrees and scale of digital transformation projects. Usually, of course, multiple stakeholders, multiple outcomes that can be driven, but a lack of scope and definition of the project tends to be the best way to derail something.

So with scope and clear metrics, you also have the opportunity to define responsibilities, timelines, and so on as a follow on from that, which makes it much easier to execute and actually achieve success in those transformations. 

Christian S: And to bring this, bridge this to any applications of GPTs, et cetera. I think the same holds true for those applications. We’re in the very early dawn of this technology and how it can be applied in an organization. So I think the message here to CEOs is that you’re not too late. I think it’s more like an exploration phase and running a few POCs on the technology will help.

And also listening, being open to it. Back to what I said earlier, we can be very happy that OpenAI has kinda forced the industry to make all access public now. And that means yeah, there will be a few hits and misses, but I think that’s exciting too. And it’s that openness coming in with a clear scope, but then also that openness to eventually succeed or fail is probably part of that.

“…a lack of scope and definition of the project tends to be the best way to derail something. So with scope and clear metrics, you also have the opportunity to define responsibilities, timelines, and so on as a follow-on from that, which makes it much easier to execute and actually achieve success in those transformations.” – Clare Leighton

#RapidFireRound: 

What’s the superpower of your co-founder that you would want to have?

Clare L: I would choose patience. 

Christian S: I would choose the ability to make a great impression.

If you would be invited to develop a Netflix or OTT series, like what would be the title of your show and one liner for the show if you have one?

Clare L: Scaling with data. 

Christian S: I’ll probably choose something that is a little bit more related to my analogy, the “Automation Staircase” or something like that. 

Looking back now, what is a skill could be a soft skill or a hard skill that you believe you should have learned back in your time as a student 

Clare L: Prioritization? 

Christian S: I’ll actually go with just that patience that grows as you grow older. I may have said something similar the last time around, but I think it holds true for me every day. There’s no short-cutting and I think it is a skill. It’s a big skill. 

If there’s something that you could automate in your job just by wishing for it, what aspect of your role would that be? Or maybe you guys are already automating it for yourself. 

Clare L: I was gonna say finance. So we automated that function. 

Christian S: I think there are still so many angles where hopefully bluesheets can provide that as an information of a co-pilot to us and clients as well. It’s still early for everybody here for sure.

If you could pick anyone alive or dead to be, to become your 24 7 executive coach who would it be? And briefly why? 

Clare L: That is an amazing question. I would say Richard Branson. I do admire his technical savviness. But his ability to align his kind of commercial capability with his strong values and that kind of brings him to line his work along with societal environmental impacts.

Christian S: me it has actually evolved. For me it’s Reid Hoffman. I’ve been following anything he did for a long time. And the way he speaks with passion about the topic around [tech] and that was a very open conversation. Not in a feeling of a commercial sort of way at all. It’s a really honest delivery of how many things we should approach when it comes to regulation, when it comes to security as well. I’m pretty much yeah aligned with that, first of all. And of course I’m learning every day, so I think I would name him.

If there were no issue about budget, where would you wanna bring your team to for a company offsite? In case there are blue sheets employees listening this is in no air form commitment to any just a fun question for the founders here.

Clare L: In reality there is definitely a budget. I would probably say Portugal. We’ve joked about where we might have an offsite and of course it’s a fun topic for the team to discuss as well. And I think it hits a few of those kinda key criteria, being warm and. The ocean and a few other great things, but also activities. And very selfishly, I surf and there’s great surf [spots there]. 

Christian S: Oh, I think I would go to my home country for sure. Germany is a beautiful country and has so much to learn for everybody from the culture and yeah, the way people approach their day-to-day lives over there. So I think it’s unfortunately probably not the best marketed in the world, always. So I would definitely want to broaden everybody’s horizon and bring them there and have a great time. 

Anything you’ve read or taken up recently that you’d like to recommend to our listeners? 

Clare L: I recently read Bad Blood, which was the Theranos story, and I’d love anything in kind of the startup space whether they’re autobiography are biographies, success stories or something like this, but I just thought that was a super insightful look at some of the pitfalls and, the many pitfalls that do exist. But really interesting to understand the ins and outs of what happened there. 

Christian S: I would actually name a podcast so I would recommend everybody to subscribe if they not already are to Gray Matter, which is a podcast by, and I. It’s very immediate and close to the topic around ai. So if anybody wants to stay up to date and hear from the people who are making decisions in these companies right now, the future will be great.