Marisol (0:00)
Thank you for joining the session. My name is Marisol and I will be your facilitator. All participants will be on mute. So if you have a question, please enter it in the Q&A section on your screen. The recording of today’s session will be available by the end of the day. If you want a PDF of the presentation right away, please visit the Shared Services Tech LinkedIn page linked under the details section. Also, we’d appreciate it if you took some time to leave a review and share your feedback on the session. To do so please click on the hearts above the video player. That being said, we now welcome everyone to the discussion on why there has never been a better time to be a GBS leader. And the session today we have Tony Saldanha, Co-founder of Inixia.
Tony Saldanha is Co-founder of Inixia which helps GBS transform and standardize operations with training and consulting. Prior to Inixia Tony was head of GBS at P&G. Tony worked with P&G for 27 years where he led the evolution of shared services. He has a track record of leading change and was program manager for the 8 billion outsourcing deal with HP, IBM, and Jones Lang the largest shared services deal ever in 2003. Tony, thank you and welcome.
Tony Saldanha (1:26)
Oh, thank you, Marisol. And Hey, welcome, everybody, I hope to you know, convince you on why there’s never been a better time than now to be in GBS. Also, I hate to do a monologue. So I want to remind you to go type in your Q&A. So we can have a little bit of dialogue. Um, but um, you know, when I had spent about 25 years at Procter & Gamble, and we had a little bit of an ironic problem, which is, Hey, you know, once you’ve done outsourcing and offshoring and, you know, one SAP wall to wall across the entire world. What’s next? What’s the next version of GBS? And I felt like, Hey, you know, it’s all been done before. So GBS has got to be a dying industry. And I want to share with you why that’s absolutely as far away from the truth as possible. The GBS industry is actually going to double in size in the next five years. And it is probably the most important asset for most CEOs for digital transformation, which is why, for each one of you on the call today, this is a personal opportunity of a lifetime. And I wanted to kind of back that up with actual stories. So the slide that you see in front of you, you’re going to see about, you know, 15 – 20 pictures, each of those is a 10x 10 times better than industry standard solution that I was able to implement at Procter and Gamble, you know, towards the last couple of years of my career there, which were 2017, 2018 each one of those projects is at least a $50 million opportunity. Also, each one of them was actually disruptive to the whole industry. So for example, you know, you can read for yourself, but, you know, instead of having lots of people, you know, help with traveling expense reporting, processing, offshore or outsource, why should that entire process even exist or things like, you know, can you do accounts receivables, payables, even procurement, doing artificial intelligence, and again, each one of those was was was actually a success story from P&G. Now, just to back up a little bit, Marisol did a little bit of introduction, just to build on that. I’ve had the privilege of essentially running IT and GBS at P&G in every region of the world. That’s about a $2 billion organization. I set up the first-ever shared services in the Philippines as early as 1993. And then also, you know, a lot of other transformational projects. I, a lot of the stories that I’m going to share with you are also captured in my book, why digital transformations fail. Now, just to kind of break up our talk here, let me back up a little bit and share information about the context of Procter and Gamble’s GBS as I mentioned a very very large very complex organization 160 services globally. So if you kind of consider payroll services to be one or you know, AP accounts payable to be another, you know, the total there you know, you will have another 158 more to get to 160 across 75 countries. The other thing you will notice is that, unlike the traditional siloed of you know, financial it so on and so forth. This one was much more, you know, customer-centric, you know, the customer as either an employee regardless of which function they work in, or the entire business, right, so you have to kind of go past silos. The other thing I want to share is that you know, the opportunity and GBS is tremendous. As I said, it is becoming the most important asset because they pop downtrend in digital transformation, which is true for you, which is the number one priority for every CEO in the world is now starting to come together with the bottom uptrend on GBS, which is operations improvement. And to prove that, you know, we were able to make a commitment as early as 2000 to 2003 to the company that we would deliver about 40 – 50% of the budget as saving over the next 10 years. But we said You know, that’s not it, there is a bigger prize. In addition to productivity, we can deliver two to three times that amount, in terms of value creation to the business and the value creation is not just cost reduction, but also help the business drive sales and improve cash. So we have to be very, very clear to define GBS benefit does not just cost reduction, but value creation. So with that, as I mentioned, you know, P&G GBS was mature, started about 21 years ago.
Tony Saldanha (6:21)
Some of the learnings towards the end of my career that I want to share with you is that even the terminology GBS is actually a little confusing because, you know, you may have an organization that just does you know, and offshoring and outsourcing of, you know, one process, you know, maybe accounts receivables, and that’s called as, hey, we have a GBS organization. In reality, it’s a spectrum of maturity, that stage one, where you actually do siloed or functional outsourcing or offshoring, stage two is actually when it becomes global. Stage Three is when you actually go after not just cost reduction, but actual value creation. So in that particular spectrum, the average maturity of most genius organizations is actually somewhere between stage one and stage two best in class in the industry today, is what I would consider you to be somewhere in stage three. And that is the opportunity for each one of you, because there is an entirely new stage on top of that, in some of the examples that I was sharing with you the use of AI, artificial intelligence, to actually do spot buying for purchasing, or, you know, even make decisions around claims and deductions. I mean, all of that is really a stage for the difference between stage three and stage four is at stage three, you compare your performance with your best in class, for your own industry, you know, you may say, I’m in the banking space, best in class in banking is you know, x y, z GBS and this is kind of where I am. At stage four, you compare yourself to a startup, you have to get the startup-like agility 10 times the speed of decision making, and two times the cost efficiency of a large company, which is typically at stage three, you should be able to get at stage four. Now, the question then becomes how? And in reality, I think it starts with defining what exactly is your GBS? And here’s what I would suggest, I would suggest that you’ll have to define GBS as these three things in today’s world delivering better service. So for example, you know, in Procter and Gamble, when we did this particular diagram, this was in 2000, to three, what we said is, we’re going to improve average service level agreements from 80% to 97%. In the next 10 years, right, but today, that’s the price of entry. You know, if you don’t do that, you have no right to be in GBS, right. The other thing, that’s the price of entry is lower cost. So he said, you know, as I mentioned earlier, you know, we will reduce 40% of the total spend. And again, that’s the only price of entry. Unfortunately, today, you know, you have to actually deliver the third thing, which is value creation, the new capabilities, which is three times the amount of the lower cost.
Tony Saldanha (9:24)
And by the way, you know, you don’t have to be linear about this. And to illustrate this, I thought I’d show you these pictures that are actual pictures from Procter & Gamble. And the Harvard Business Review article is also about Procter & Gamble. Now, the kicker in this is that all of this is at least 10 to 12 years old, right? So you know, 10 to 12 years old, you didn’t actually have many of this big data and data scientists and all that kind of stuff. And yet, we had actually taken whatever data even if it was not on a common platform, and said, we’re going to create value With it, and the picture you see in the middle there with the, the two screens is actually the CEOs boardroom for the global Procter and Gamble headquarters. So we went ahead and redesign the room. And we assigned a dedicated today you would call the person, a business analyst or a data scientist, and actually change the way the company ran its weekly global leadership meeting, right, using technology, and these capabilities. So again, this doesn’t have to be sequencing, you don’t have to wait until you’ve got everything cleaned. To do that, you have to start right, you know, right now, right? And why is that, that, you know, you really should not wait, why you really need to define your job much bigger than most people do. The reason for that is, you know, your individual company is being disrupted, not so much by pure companies, but more by startups, right. And you have the ability to leapfrog, you cannot spend all of your time on just continuing automation and continuous improvement, you cannot be saying, Where is the next 10% improvement gonna come from, you have to say, Yes, I am going to deliver that 10%. But on top of that, I am also going to compete with, you know, 10x of 10 times better competition. And the way you So in other words, you have to basically lead from, you have to actually have to start from the end and mine, and then work backwards. Um, and why is that I want to spend a few minutes on that, and I apologize for maybe sharing in the next few slides, a few things that you may already be aware of. But it’s really important to provide your context. Now, the context here is the fourth industrial revolution. And you know, you guys have all heard about this, but I want to kind of take you down to what it really means. If you basically look at the, the the largest companies in the world by market capitalization in 2018, five of the top 10 companies in the world, were actually tech companies. And by the way that didn’t include Amazon or Alibaba. So you know, now you’ve got maybe seven out of the top companies. Now, if you had looked at that only eight years ago, there was only one. So you’ve gone from like one to sell. The other sign, you will see his functions are dramatically changing. So let’s take supply chains, fashion retailers, you know, whether it’s Zara or or, or Firstfashion, can actually go from designing apparel, to actually having it available in-store in two weeks, actually.
Tony Saldanha (12:53)
Fast fashion actually does that in 10 days now, when I had the opportunity to visit them in Japan, I saw how they were able to do that even faster. So in other words, the supply chain is becoming incredibly agile. But it’s not just the supply chain. It’s also research and development. Because if you look at cell phone manufacturers they can go from, they can actually have new versions of phones. I’m not talking about software, you can do hundreds of new software upgrades every hour. I’m talking about new batches of phones every week. Yeah. So in other words, the research and development cycle has crashed, right? And you also see this in terms of things such as, you know, entire sectors manufacturing retail, you know, you see all of these statistics about how half of those jobs are actually going to be done by robots, whether hardware or software. And you also hear at a societal level, how, you know, computers are getting more and more literate. In fact, that particular statistic in the US of machine literacy actually becoming higher than, you know, about 10% of the people is a good sign of why there is so much anger in some of the developing countries’ societies. Now, all of this is context that we’re aware of, but it is actually very close to home. And it became real for me in 2015, when I sought out to talk to 100 different companies. These were CEOs, yeah, you know, consultants, startups, peer leaders in other companies to understand where is shared services going, what’s the future of shared services. And what you see on the slide in front of you is actually just an email dialogue between me and a person named AJ Bronstein who was the CEO of a small company called Manolo and you see the typical phone tag or email tag, trying to set up an appointment. So, you know, when April 10, I said, you know, I’m sorry, AJ, I’m out next week, but you know, perhaps we can meet the week after and he says, Okay, fine. I’m calling In a me who I assumed was admin to do that, and then he comes out in the boardroom and says, Okay, here are all of the times now, the reason I’m telling you this very, very prospect story is if you had double-clicked on X.AI, as Amy signature, you would see that Amy was a robot. Now there’s nothing in the email, if you read Amy’s email that could give you a sign that I was talking to a robot. If you look at the context, when I say I’m out next week on April 10, none of the suggestions that Amy gives are in the week that I’m out. And then you look at the year 2015. In 2015, you had companies that had part of their admin assistant work being done by robots. The insight that this teaches us is we in medium to large companies. You know, we are so conservative because we asked the wrong questions, we asked the questions, is the technology ready for me? And that is the wrong question. It’s not technology’s job to be ready for us. It is our job to understand which narrow use cases of technology are ready and to adopt them. So in other words, even today, the technology for AI to replace an entire admin assistant is not ready. But to do calendaring. That was ready six years ago. And there were companies that were already adopting it. So in other words, think use case, not technology. And the reason for all of this is because of course, technology gets cheaper and cheaper, and more and more use cases keep developing. You’re familiar with this particular graph, which is Moore’s law, which doubles and capabilities, but something incredible is happening. If you look at the year 2023, you’re going to be able to go out and buy in 2023, the computing capacity of an entire human brain for $1,000. That’s absolutely mind-boggling. But of course, it doesn’t stop there. Because by 2050, you’re going to be able to buy the computing capacity of all humans on Earth for the same $1,000. The number one question I get asked, When I consult with CEOs and boards of the Fortune 100 is what should be by personnel strategy, employee strategy, if I have to make choices in the next few years between buying $1,000 computers, and human beings.
Tony Saldanha (17:36)
That ladies and gentlemen is basically what the fourth industrial revolution is when computers become smaller than grains of rice, and they become more capable in the next few years than an entire human brain. That is the fourth industrial revolution. So then the question becomes, what do we engineers do? Right? Do we continue to look at low-cost labor? Do we continue to look at 10% improvement? Or do we define our role as helping those CEOs drive digital transformation? And what it takes is actually very, very simple, we have to define our role as not just running our day-to-day operations. The 70-20-10 model is something that I took from Google, where again, the percentages don’t matter whether it’s 70 2010, it’s basically the fact that you have to have three different strategies. So in Google’s motto, 70% of the organization’s effort isn’t running operations. 20% is continuous improvement, and 10% is to actually disrupt themselves, right? When I was at P&G, that particular ratio for me was probably more like 80 and 19.9. And point one, which is 80% of the effort was run the operations 19 maybe point something is continuous improvement, you know, so as for HANA, you know, so on and so forth. And then point 1% of all of the resources, people and money were essentially let’s disrupt ourselves in GBS. And that’s basically what took us down the path of all of these projects that I talked about earlier. I’m going to do a quick deep dive. We’re not going to have time to go into each of these. But my book why digital transformations fail has much more detail on some of these. Now, you know, let’s take the work that I did along with Sashi at Highradius, which is how do you use artificial intelligence for claims and deductions in accounts? receivables, right? So this was in 2015. And we basically said, Hey, instead of having hundreds of people across the world that look at customer claims and deductions, and decide whether it’s valid or invalid, why isn’t there an algorithm that can do that better? And of course, there was one we found one, you know, with a small startup in Silicon Valley and, you know, we worked with Sashi to actually develop those capabilities, which became part of Highradius’ roadmap, right? Another example, you know, instead of having call center agents, I’m not talking about the IT people I’m talking about, you know, customers like you and me calling, you know, Procter and Gamble, you know, with requests for information on, you know, Where can I buy this product? Or what’s the ingredient for this shampoo? You know, there are about 1000 people across the world that do this type of call center work. Many of them outsourced. And so instead of, you know, having them essentially take, type your address, type your question, look into the database of the company to respond. We had two AI algorithms, one was a voice to text. So even as you were talking, you know, some of the Salesforce information about you was getting populated by the algorithm. And by the way, because we’re kind of crazy, we tested this using multiple languages. So, you know, you may be talking in China, in Mandarin, and Salesforce.com was capturing this in English, right? And then the second AI algorithm was, we look at your question and search the databases of the company to give you the right answer immediately.
Tony Saldanha ( 21:16)
Another one, which I share with you, is how to use blockchain for Import Export. So the shipping company Maersk, and IBM had a blockchain project they still do, where they were putting information about, you know, all of the shippers, port authorities, customs, freight forwarders, you know, for specific shipping lanes, about 15 of those onto a blockchain. And so we said, okay, that’s fine. But you know, we want to be more aggressive than that. If we have all of the information about import, export, all of the costs all of the contracts, then why do you actually need those suppliers to generate an invoice because they are wasting their time generating invoices, and we are wasting our time processing these invoices. So for 15 shipping lanes, we said we are going to auto invoice using this as a platform. So this basically gives you an idea of the type of disruption that is possible. I’m not talking about conceptually, you know, is this the future I’m talking about actual examples of where this exists? And the reason I do this is because I want you to understand that this is not you know, as the saying goes your father’s GBS. GBS has evolved tremendously. You know, Marisol mentioned that I founded an organization called Inixia, which is essentially the standard for the whole global industry. So you know, just like you have PMI Project Management Institute for project managers, or you may have, you know, whatever it is CPA for a, you know, chartered accountants, you know, now you’ve got Inixia for GBS certification, you can look it up online. And what we’re trying to do is we’re trying to certify people so that you can continue to evolve your skills and your capabilities because staying with the earlier versions of GBS is no longer an option for companies, we actually have to improve our capabilities, because this is the opportunity of a lifetime. This is what is possible. The question for us is, you know, what can we do? How quickly can we get there? Okay, so let me stop at that. I want to give ourselves at least a few minutes for Q & A. So let me turn this over to you, Marisol.
Marisol (23:41)
Thank you, Tony. So I will initiate the q&a with a quick question we’ve got for you: what advice would you give for GBS leaders wanting to evaluate and implement AI for their order cash function, any pitfalls that we need to avoid?
Tony Saldanha (23:59)
Oh, there’s tonnes of pitfalls. So I already talked about a couple of them. Firstly, you know, what I would say is frame your question about AI correctly. So in other words, always start with the business outcome. And you know, remember I talked about, you know, admin assistant, you can’t replace an admin assistant, but you can do calendaring. So focus on use cases. The same is true for AR right?. So the question is in implementing AI for AR. The question is, can you do specific types of AR work? And I give the example of the word that Sashi and I had done around deductions and claims management, right? So, you know, if you’re basically and in large companies, like Procter and Gamble, there are billions of dollars that actually eventually kind of go back and forth, right. And rather than having people you know, the accounts payables or organizations of the customer and the receivables organization Have you try and do this over email and you know, across different systems, you can use this specific algorithm that can actually do a really good job of saying, is this incoming claim valid or invalid? So, you know, number one pitfall is people define the problem too broadly, which is, can I use AI? The better way to do this is to say, Can I actually do disputes and claims, which is a judgmental type of task? It’s not a transactional type of task. And can AI help me do that? And I would say the same, you know, the second thing would be to look for where you have a lot of people and a lot of waste. And if those happen to be within judgmental type judgment type decisions, then look for partner vendors, look for algorithms that play very narrowly in those types of judgments, right? And then of course, that’s not sufficient, it means it is theoretically possible. The next pitfall is it may be theoretically possible, theoretically possible, but your data, your underlying data is not ready, right? So the quality of your data, because you need a lot of trained data to be able to do artificial intelligence. So that’s the next pitfall. And then there are many other change management pitfalls, even when you do that. But again, you have to approach this very, very methodically.
Marisol (26:31)
Got it. Thank you, Tony, for your feedback. And that is all the time we have today for a Q&A. Thank you, everyone, for joining today’s session. If we were unable to answer your questions, we promise to follow up with you. If you have any additional questions regarding today’s topic, or like to connect with any of the presenters. For further discussions, please feel free to reach out to myself. This officially marks the end of Shared Services Tech 2021. We encourage you to rate the session and check out the Shared Services Tech event, LinkedIn page to obtain a copy of all the presentations, and visit the agenda page for the recordings of each session. Again, thank you Tony, and everyone for joining us today. We hope you have a great rest of your week.
