Gwyn Roberts [0:02]
Hi, everybody, welcome to SSON. My name is Gwyn Roberts from HighRadius. I’d like to introduce you to Dave, who is going to be presenting today. And Dave has over 20 years of experience designing and operating world-class global business service operations. And Dave has recently joined OQ energy, oh man joined in May 2020. And the purpose is to establish Global Business Services across 18 Group companies in 16 countries. As part of the major business integration program, OQ plans to invest 28 billion over the next 10 years and GBs will be a driving force to maximize agility, efficiency, and process excellence across the whole organization. And prior to joining OQ, Dave set up and managed the award-winning energy-shared service center in the UK. This was the first SSC in the UK to be accredited by the Institute of customer service and achieved world-class performance benchmarks across all key success metrics. In his early career, he worked with a large number of utility companies and implemented shared services and major systems transformations. Dave, welcome and over to you
Dave Hughes [1:31]
Can keep going for the very kind introduction. So pleased to be here for SSON to talk about a very interesting topic that is very dear to my heart and got some interesting experience to share with everybody in terms of automation in the accounts receivable space. worked very closely with grid and a HighRadius team in the past. And we’re going to explore I suppose some key lessons learned and, and some things to think about moving forward in this world of automation. That is what is around us right now. So I thought we’d start with an interesting court. And when I first saw this court, I actually thought it was on the conservative side. So McKinsey’s research concluded that 60% of occupations could see 30% of their tasks automated with technologies available today. And I think the keyword in this statement is the word today. And what I’ve learned when working with automation tools, particularly in accounts receivable, spaces, how, how quickly these technologies move forward. And one of the challenges I found is just keeping up with the speed of change, the evolution from 12 months, is huge. So as we go through this presentation, I guess something to think about is, and some of the things that I constantly challenge my teams to think about is, are we spending enough time thinking about how we can change and transform our processes, leveraging this automation technology that’s out there? There’s so much that we don’t know. But there’s so much that we can leverage today to help us be successful. I think it’s interesting too, to kind of frame the conversation around what’s happening across the globe. The last six months have completely turned and most of what we’re doing is in our heads. We’re all trying to look at our business models and when necessary pivot and but most importantly, maintain operations and remain competitive. I don’t think any company hasn’t been impacted in that way. And of course, that brings cash flow challenges to all parts of the economy. So from an accounts receivable perspective, cash has always been hugely important for every single one of our companies. But there’s no question there right now, that is amplified. So, therefore, the focus has to increase. The third point, we’ve had teams working remotely, most companies where possible have done this, due to the pandemic. And that’s my business communication. And collaboration is very difficult, maintaining the connections with customers there were their customer contacts that we had in the past supplier contacts. It’s challenging. Before we may have been contacting company email addresses or telephone numbers to discuss payment. Now, that’s changed. People work from homework in different locations. The fourth point is that some of those people are not there. Some companies have been impacted so greatly that They’re reducing the size of the workforce. So redundancies resulted in the loss of customer contacts and knowledge. Now we’re trying to contact customers about payments. And that knowledge is just not there, those relationships have gone. And I think this is a particular challenge, that we need to think about how we can manage this move forward. And this all culminates in what I think of in terms of rapidly changing risk profiles of both customers and supply chains. So all the assumptions we had before about credit risk and the ability to pay, we need to continually reevaluate and sense check that that information is valid, and we’re making decisions on what we do moving forward on real-time information. So if you add all that up, that creates a number of challenges for us in order to cash and accounts receivable. The first challenge that I think it presents from my experiences, we need real-time visibility across the end-to-end process to make better decisions. So most of us will have great visibility at the month-end, typically, and part of our period clause, when a lot of information comes together, and we can see what happened in the previous period. But I think most of us would admit that we don’t have real-time visibility across the end-to-end process in the middle of the month in week one and week two or week three. And that ability to create that real-time visibility is something that we’re all going to have to adapt to make much better, faster decisions moving forward if we are to maintain business continuity. The second challenge it presents is what do I want my auditor cash team to work on? And how do I want them to work? So there’s a finite amount of time in my audit cash team is a set number of hours each month I can spend, I want them focused on value-added activities. I don’t want them bogged down in routine administrative tasks that I can automate. I’ve given them the tools to do the job, where they can make some of these decisions within a framework of empowerment. The third challenge, I believe, is looking forward, what new skills are going to support me and my companies to survive and grow. So accepting that automation is the norm, accepting that I want much more real-time visibility, what skills do I need to give my team in order to embrace that and capitalize on those opportunities. And I genuinely believe these skill sets are changing something for us to think about moving forward. So if we didn’t know already, I definitely know about the digital revolution. And I think now we’re in the space of knowledge, this knowledge worker within our accounts receivable process. And the way I look at this is how can I place that knowledge into the right hands and the right people at the right time to make the right decision. I ordered a cash team on the interface with customers when it comes to payments and Cash Generation for my business. I need to equip them with the right data, the right knowledge by which they can make the right decisions.
And in that digital world, the roles and responsibilities are changing. Hence, I need to consider the skill set and requirements within my audit cash team. And I mentioned this but I think this is certainly worth looking at in terms of some of the research that goes into this. So the World Economic Forum survey, look at the skills that are important. Now the skills are important in the future and the skills that will be less important. I think this is a busy slide. But if we just look at the middle column and the five key skills, the top five skills are going to grow and be much more important to us in the next few years. We can see analytic thinking, analytical thinking, active learning, creativity, technology, design, critical thinking, none of these are manual tasks. This is about brainpower. It’s about having the analytical brains to be able to analyze data, much more intently to draw out those trends and those decisions that we need to make as a business that’s going to preserve cash, and it’s going to improve our working capital and cash flow across our business. So I think that that starts to raise questions in terms of how do we start to plan for that? How do we invest in these skills? Is my auditor does my audit cash team have these skills today? Are we still working in very traditional ways of trying to Contact customers? Are we prioritizing, for example, telephone manners and customer service skills or analytical thinking? So what does this all mean? I think there are four areas that I’ve learned, and certainly leveraging the power of automation and tools like HighRadius that I would share with everybody in this session. So the first one is to really invest in analytics, this real-time visibility into our processes is achievable. These are not things that we need to live with just having a month-end view of what’s happening. There are solutions that can provide that real-time information, those alerts, those notifications, that can add up to some really powerful decisions that get made and mid months, if I can call it that, rather than just a month-end review process. So moving away from this month, then focus I think, is huge. I think, you know, not every company has this. But ignoring that, that constraint, and that artificial reporting window, I’ll call it that, to get to this point where we develop what I call right now risk management. So my auditor cache team is operating in real-time, taking information flows from so many different places, to make much better decisions in terms of collecting cash, and determining the customer, the risk profile that lies in our customer base, that’s going to affect us in the future if we don’t address it. The second point is to invest in AI and machine learning. So I’ll just give some examples of the kind of things that the benefits of this can bring to it on our cash team because I think these are buzzwords that get bandied around. And sometimes, we nod and so you kind of understand that what does it really mean to something like accounts receivable? So this is not rocket science. These are really simple examples. But things that I’ve seen in my own eyes really drove a benefit in the effectiveness of my auditor cash team. So why would I chase a customer for payment every single month, if I knew that 99% of the time, they’re paying me on time? I do that because I’m nervous that there’s an internal process within that customer that I’m not aware of. I’m trying to create visibility, I’m asking questions about something that may or may not be a risk, I don’t know. What I do know is that for the last five years, I have had customers paying me on time, and taking this risk-based approach to accounts receivable,
leveraging these tools that can start to learn about you what your customers generally how your customers behave, and also how effective your internal processes are, can help you start to strip back and see the wood for the trees and can start to say to your audit attached to him, actually, you know, I don’t need you to do that anymore. Because that’s taken care of on the balance of probability. That’s not a risk, I want you to focus on these areas. There’s some new information that I’m receiving about this, these particular customers that have changed that risk profile. And therefore I do want you to proactively start to contact me. So taking you, for example, just boring, a specific example of collection strategies, where there’s some really powerful information within tools like how radius that can actually start to drive much more effective in my order to cash team, I’ve got a finite set of hours, I want to use them in a certain way. I don’t want to create the business rules that dictate where I spend my time. But I need to feed that with real-time information. And I need machine learning to continually improve the decisions that I make when I consume that data. This brings me nicely onto data. So automation solutions for the cash are only as good as a dare to be within the system, however, and that goes for any system. However, what I do believe is that we saw a huge improvement in the quality of our data. Six months after implementing HighRadius. And it started to uncover gaps in our data. It started to uncover bits of data that we didn’t have that we needed, we really needed to understand, for example, customer credit risk. So we were spending so long, obsessing about trying to understand how much customers order from an end perspective, we weren’t spending enough time looking at other easily available market data to form a holistic view of the credit risk around a customer. Now that these are basic things that very often when we’re consumed in asking credit controllers to chase customers for payment, we don’t always have the time to take on board and information off we do we empower that to the credit controller to make a point human decision. Well, solutions like our radius allow allowed us to do was to take all this information and assess it in a consistent way across the whole customer base, to say that a negative news story about customer and creditworthiness was treated in the same way by the same credit controller, or the same collections agent in a different territory and a different legal entity, right? It allowed us to flex the level of risk we were willing to accept in some indifferent legal entities, right. So now think of all the decisions around the approach we’re going to take for collection strategies. We had control of the machine, we had control of the business rules, and we were able to flex them and easily implement them across our whole customer base. So that gave us a level of control we didn’t have before, but also gave us the ability to crystallize these decisions that I’m talking about, and actually start to make firm decisions about the customers we interacted with, and the level of credit risk that we were willing to accept. And the final observation, I guess, our lesson learned is around skills. So in terms of the skills that will be important in the future, traditional credit control customer service, telephone skills. In my experience, I’ve started to see these skills, not necessarily becoming redundant, but less important. Because of the reasons I just explained, we now have an automated solution that provides a lot of information, a lot of data. I need my auditor cash team to be comfortable, first of all, comfortable processing that data, and competent in understanding it and interpreting it to make decisions. Otherwise, the danger is I put a Porsche 911 into the hands of a teenager and they crash into the neighbor’s tree. That’s the analogy I like to use. It’s almost too powerful. And what I’ve found is blending these skills started to introduce skills around data science, dedicated analytic analysts, dot dedication to the team, lean skills, continuous improvement skills, investing investigative skills, in terms of interrogating data, has really allowed us to maximize the effectiveness of these solutions and leverage them to an even greater degree, to the point where the Porsche 911 is close to going 100 mile an hour, which is exactly what we all want. So I’m a big, big believer in making sure that the skills that wrapped around these automation solutions are where you start first and start to think about that equip in that because trust me, you will, you will not regret investing in those skills, it will help you will draw forward, and it will click Place more demands on the automation solution you’ve got where you’ve got intelligent users who are basically demanding more from the solution. And that’s exactly what you want. So in a nutshell, I hope that was a useful summary of some experience in automation for all the cash and accounts receivable. We’ve got, I’m going to hand back to Gwyn, who is going to facilitate any questions we received or any other topics you might want to discuss. So going out, I’m back to you.
Gwyn Roberts [19:26]
Thanks, Dave, thanks for that insightful presentation. We do have some questions. Let me start with the first one. The first one is what’s critical to do right now to ensure that the workforce evolves and develops the required skill sets.
Dave Hughes [19:49]
So, yeah, it’s interesting. I think one of the areas that I’m passionate about is not encouraging employees to not wait to be told what skills to develop. So I think, challenging this mindset of trying to establish this mindset of lifelong learning in our team. So really encouraging the teams, for example, the cash teams, to start to look at these technologies and be curious and explore them. So I don’t want a situation where my IT team or heaven forbid me is telling my audit cash team that they need these automation solutions, I’m trying to create a culture where they are out there re investigating and being curious about these new technologies and bringing them back in an agile way and trying to apply them to their processes. So that’s a shift, certainly, in the last five years, a huge shift I’ve seen where it’s incumbent on leaders of all of the cash teams as well as the whole team too, to be having their eyes and ears open to these new technologies, machine learning artificial intelligence blockchain. These are not solutions that will come commoditized yet, we’ll just buy them off the shelf, we will need to understand them, interpret them and apply them to our process. And for me, the final bit of the jigsaw is to drive innovation. So we want to be at the leading edge, we don’t want to be following, we want to innovate around how to cash process, there are so many different things we can be doing as pioneers, so trying to create a culture where there’s an investment of time, first and foremost to understand. There’s a behavior skill set to be curious and experimental, but also open-minded to change. So we don’t, we’re never standing still. Because if we don’t we become extinct. And these are the skill sets and behaviors and culture, they’re not just for cash. To be honest, this applies to most businesses and most functions, but particularly in the auditor, cash space with the speed in which technology is moving. I think there are things that I’m certainly focused on in my current role and in the past.
Gwyn Roberts [22:25]
Yeah. Thanks. Thanks, Dave, we’ve got another question just come through. Do you find that there? Is considerable data cleansing activities that are needed before investing time in automation? Or is it better to use more of an agile approach to implementing automation?
Dave Hughes [22:47]
Well, I’ve never worked on any IT project that where the data was clean enough at the start, so I think I think that you have to have clear expectations, I dependents, and I’m much more in the space of technological approach, using the process and the new technology to identify those gaps in data. As I mentioned, in the previous implementation, we had our data good enough for that. It wasn’t perfect, but it was good enough for us to start to leverage benefits from a solution. It started to challenge us in terms of our internal governance of that data, for example, the ownership within the organization. Customer Data is very often dispersed across lots of different functions. So it started to, we started to formalize that, and that was good for the organization. But it was challenging. So I don’t look at those things as mistakes or errors. Or, actually, you know, we should have done this before we implemented the solution. I think they were necessary to if you like, expose and highlight these challenges because now we were doing it under the banner of this huge improvement in invisibility that we want. We all agreed we wanted it to get worse before just kicking off a project to cleanse customer master data would have got a level of interest, but there wouldn’t have been a clear reason. So I think the agile approach is perfectly acceptable for most companies to take, even just taking a source subsection of the customer, customer database and applying it and learning some lessons as you go.
Gwyn Roberts [24:41]
Okay. Thanks, Dave. One, I think we’ve got time for at least one other question. The question here is about artificial intelligence. Artificial Intelligence is changing jobs. However, there is a lot of fear around AI with respect to job loss. lack of control on systems? How do we trust the system? What’s your take on it? On AI?
Dave Hughes [25:11]
Yeah, well, the first thing I’d say is, as far as any new technology and a new revolution, that’s commerce, there’s trust and trust and fear issues. But I, the approach I take, and my experience of it is AI is a huge enabler to drive improved effectiveness. So less about less focus on the impact on jobs and, and headcount. Now, don’t get me wrong right now, in this current pandemic, and cost pressures on organizations lot, as I mentioned earlier, lots of companies are looking at redundancies to resize as a result of the pandemic so but ignoring that for a minute, on alike, for like basis, AI is a huge enabler to streamline those routine processes within any audit to catch the team. So having a clear vision of how you want your order to cash team to work now, my experience in Mg and OQ is that our whole focus is around value creation for the organization. So I don’t want an order the caching team that may appear very efficient in terms of cost, just doing low-value processing, I want to push on into that new space of additional value adds around analytics and support on decision making, I don’t just want us to collect cash and put it in a bank, there’s actually very, very limited value in that. So there is an enabler to push the auditor cash team into the next level of value creation. And I think this, where the challenge here is to be bold with your vision and share that upwards and show what the hey, our team, and order cash teams can do. I think traditionally, most organizations and most executive teams would kind of see it as a necessary evil, but probably wouldn’t be that demanding of that function. When in actual fact, when you start to shine a light on it and say, look what this team can do with these technologies. I think it’s quite enlightening and quite a lot of light bulbs start to go on for projecting when you do that. So viewing AI as an enabler, don’t be scared of it, understand more about it, and find a way for it to push your team to the next level of value creation.
Gwyn Roberts [27:53]
Thanks, Dave. I think that we’re coming up to the top of the hour, so we’ll probably have to stop the questions now. But I want to thank Dave for this session. And obviously for everybody who’s joined. We’ll try to answer the q&a. To the people who haven’t had their questions answered. We’ll try and do that. directly to you. But yeah, thank you very much, Dave. And thanks, everybody for participating.
Dave Hughes [28:28]
Thank you. Good. And I’ll echo those comments. Thank you, everybody, for your questions, and wish everybody an enjoyable session remain in the SSON event. All the best
