Digital Darwinism & Generation C: Navigating the Future of Business with Digital Transformation

Digital transformation now isn't a one-and-done event, and it's certainly more than just a collection of data. To really effect change, an organization must embrace a purposeful mindset shift, and cognitive automation can lead the way. In the "Unlock New Growth Opportunities through Digital Transformation" webinar, CGT senior editor Alarice Rajagopal is joined by digital analyst Brian Solis and Aera Technology client partner Fred Fontes to share what successful innovators understand about today’s ever-impatient consumers. Forget millennials or Generation Z; it’s the expectations of Generation C that should be on your radar. The full transcript and presentation slides from the conversation are below. Alarice Rajagopal: Over the last 20 years digital native companies have transformed how people consume, from movies and television shows delivered digitally, to physical products at your doorstep in a matter of hours. While these companies have clearly set a new bar for convenience and for consumer experience, they have also opened the door to new types of growth opportunities and business models. For consumer goods and retail companies looking to capitalize on these opportunities, digital transformation is no longer optional; it is a prerequisite. I’m Alarice Rajagopal, senior editor of CGT, and joining us to discuss are two subject matter experts on this topic. Brian Solis is a world-renowned digital analyst, anthropologist and futurist who's been called one of the greatest digital analysts of our time. Brian is also a global keynote speaker and an eight time bestselling author. His latest books include Lifescale: How to Live a More Creative, Productive, and Happy Life, X: The Experience When Business Meets Design, and What's The Future of Business, a.k.a., WTF. Based in Silicon Valley, Solis studies disruptive technologies and the impact on business and well as innovation, experience, design, culture dynamics and digital behaviors. Through his research and published work, he humanizes disruptive trends to help leaders understand how technology, markets and people are evolving, and how to drive innovation and growth. Brian will be joined today by Fred Fontes. Fred is a client partner with Aera Technology, where he helps customers drive impact across the enterprise by digitizing and augmenting complex decision making. Before joining Aera he was an associate partner in McKinsey's advanced analytics practice, where he led clients through large-scale analytics transformations across the CPG and retail sectors. Brian Solis: This is really an exciting conversation. It actually goes well beyond even just the context of what we're going to talk about today. What we're really talking here is about the future of business in general, the future of markets, the future of customer engagement, the future of just all customer experiences, all enterprise operations. And this is why it's so exciting, because in my research I've been studying digital transformation for the last — I don't even want to say long, it'll date me — but at least 10 years. In that time digital transformation has evolved from this conversation as sort of migrating to the Cloud and upgrading and updating all kinds of systems across the enterprise, to fascinating conversations like what we have today that almost start to show us, or do start to show us, what a post-digital transformation era starts to look like. When we talk about retail, sometimes those conversations can be pretty dire. I've studied retail innovation, and you can't help but not find things like articles talking about the Retail Apocalypse or Retail Armageddon. Every day it seems that there's more and more retail closures, but in the same vein, for every store that closes, there are a number of stores that open, and this is a real important part of the conversation. I think it's overlooked and one of the reasons why I was so excited to join today is that it's not so much about retail as a sector; it's about the approach to retail. If you think about it, the traditional retail industry itself hasn't really evolved much over the last 100 years. And if you think about the move to the web, dating back to 1995 when it really started to get mass consumer as an option, we still didn't necessarily see it as — or from the legacy perspective — as a brand new paradigm to reinvent the concept of retail, especially when we got with e-commerce, which was a digital iteration of catalogs and just traditional retail experiences. What I think is making retail so exciting right now is the idea that, especially if you look at the D2C states, it's just that companies that innovate, whether you're legacy or new, whether you're an incumbent or an up-and-coming hot challenger brand, what's key to pay attention to is the approach, the infrastructure, and more importantly the understanding of the market dynamics and how they've evolved, how they've completely changed and also how customers have evolved and changed. That's where I think this conversation gets really interesting because what was once guaranteed — what was that old saying, that no business are too big to fail — but nowadays customers no longer associate large with success or trust or stability like they once did. In fact, in many cases it's actually working against these brands because they're not taking a leadership position in understanding how their expectations and behaviors and preferences have evolved. I call it Digital Darwinism. This is a term I've been using for way too many years. I've been studying digital and its impact on markets and also as an anthropologist digital's impact on society since the late ’90s, and it's only getting more and more interesting. It's only getting faster and faster, but really Digital Darwinism is what you make out of it. It's a threat or it's an opportunity, but what it is is a reality. Things change, and digital transformation sprung up along the way as a means to adapt to all of this change, to create a more digital — hopefully a more agile — infrastructure that didn't just modernize how an organization works, it also modernized how we thought or how we think about business, or at least it should. At least in the more mature digital transformations that I've studied, organizations are really looking beyond technology to give that technology or those technology investments and those roadmaps a sense of purpose. And at the No. 1 spot for many, many years, not that it ever actually lost that No. 1 spot, was customer experience and/or staying ahead of market disruption. And, in those cases, digital transformation really starts to become something much more profound because if we look at technology stacks in any capacity, for example, of customers' experience and you break out the customer journey, it's overwhelming. If you look at it from a simple technology perspective, but also what you're looking at here is any enterprise organization, and I can't remember the exact stats, but I think something along the lines that enterprise runs something like 464 custom apps, a ridiculous amount of disparate Cloud applications and also still on premise locations and a lot of that stuff just doesn't talk to each other. So this is as much as we want to talk about modernization, much of this is also a hindrance; it's an anchor to evolution and also to innovation. This technical debt, so to speak, is a burden when it comes to thinking about ways to transform. So if you attach this to retail conversations about iteration and innovation, what we really start to see is from an infrastructure standpoint, a back-end standpoint, there are real reasons why retail innovation is difficult. But we can continue on that conversation all day long. I hear things like, "Well, private equity, of course, has their tentacles in this. They're trying to salvage what they can." But any company, any organization, any leader that wants to stay relevant for a customer, for a market, can look inside and find opportunities to improve, even it at the smallest of levels, but it all starts with understanding customers and understanding customers in this day and age is all about data. The beauty about this era that we live in is that AI and machine learning has now proven, and it is allowing businesses, those businesses that want, to understand infrastructure, to understand markets and to apply those insights into new models or into creating new models that are both externally facing, but more importantly, internally transformative in order to be more relevant on the outside. Digital transformation now, if we could put a stake in the ground, is not a one-and-done event. There is no end state to digital transformation. This is an important mindset shift that we all can benefit from. It is an infinite game. I think up until this point, at least a lot of the roadmaps that I had an opportunity to look at, were finite. They had timelines, but if you think about it, technology is only going to become more disruptive. Technology is only going to continue to evolve; it is still going to challenge even the most agile infrastructures that we're building today. The processes, operations, everything is going to continue to evolve, and I think that's really where this conversation gets so exciting, because the future of what the retail or business in general looks like is really to be defined, and that comes down to mindset. Mindset is a huge competitive advantage right now. Depending on your mindset, technology can do or it can't do a lot of things. This is why I tend to look at the data that humanizes markets, to give insights in order to learn to prioritize technology investments or operational models because when we look at, for example, the priorities that any business substantiates today, we have the usual things. If you're held by a stakeholder or a shareholder value, we still have cost controls, we have margins, we have quarterly business results. We have all the things that a business is governed by, but at the same time you have customers who are evolving regardless if they're B2B, B2B2C or B2C or D2C, whatever it is, it's all about human beings. As technology changes, so do customers and their behaviors and their expectations. And it's not just physically, it's also emotionally, it's intellectually, it's also philosophically. So a lot of times what happens is that markets really start to divide because what a business prioritizes. What a business knows and how markets evolve, they sort of create what I call the Experience Divide, and as that divide starts to expand, that becomes the exposure to disruption. It's not an overnight thing, but it is a real thing. I call it Generation C. I know a lot of times in retail conferences and events or with executives that I speak with, we tend to look at things like, "What are millennials doing? What do Centennials want?" I totally get it, but no generation behaves entirely the same. If you look at what are the common things that are disrupting their behaviors or changing their behaviors from the generations before them, it's sort of their access to technology, but also how that technology affects their lifestyle in work and in their personal lives. What becomes so important to understand is that there is no demographic for this; there's a psychographic for this and I call it Generation Connected, or Generation C, for short to get executives to see the people and the behaviors specifically that they couldn't see before behind the demographic. In fact, demographics tend to sort of bring up cognitive bias in executive decision making, and anybody who works in data, and especially now in AI-powered data analytics, will tell you just how unhelpful the biases can be, but when you start to look at the reasons behind that transformation — I mean anybody really with a mobile phone is this new consumer to varying extents and that connectedness changes everything about how we think, I mean literally how we think and how we operate. It rewires our brain — that's called neuroplasticity — it accelerate our decision making. It's one of the reasons why we probably have 1,000 tabs open. It’s sort of a physical metaphor for what's happening in our brain and carries this cognitive load that keeps us moving faster and faster and it creates this quiet, or unstated I should say, anxiety that all customers carry with them. So when looking to make a decision, when they're looking to shop these are all opportunities for innovation, for not only how we talk and communicate but how we stock shelves or how we stock warehouses or how we think about product design and innovation because every aspect of the world around our business is changing in ways that when we see it outside of the context of our work, it makes us stop and think like, "Whoa," or "Wow," or What?" And these are all important reactions to remember that when we're inside of our work it becomes a bubble. I often say that, "I tried to be innovative but I got stuck in meetings all day." The reality is that we are becoming as customers increasingly impatient, increasingly demanding. I call this loving, of course, accidental narcissist because everything on the smartphone, everything in technology today is telling us that we're the most important person. I studied recently, for example, buying online and picking up in store and published a report on the subject that showed that the customer expectation shows that once you park, your expectation is to get your merchandise within two minutes or less. That's crazy. Most infrastructures can't support that, but most executives don't even know that. So these are things that we have to think about. Of course the data tells us this stuff, but we have to have an infrastructure and also a mindset ready to be able to see these things. More importantly, every time there's something new, it just completely transforms how people expect to show, how they expect to engage, how they expect to learn, find information, etc., so voice is transforming customer experience. Voice is actually now also starting to transform enterprise employee experiences; it's that profound. So when this all adds up, when you really start to look at just how extensively the customer is changing, I call it the Now Economy, I call it the On-Demand Economy. Whatever it is you want to call it, what they all share is that impatience is a virtue. The pillars of these new economies are that customers expect choice, but if you look at No. 1 and No. 7, they expect that choice to be personalized, just like they expect the experience, like 5 and 7, to be personalized, they expect things to be immediate, convenient, accurate, integrated, which is something that all enterprise organizations are struggling with, and they also expect that experience to be native. So if they're on the web, if they're on their mobile device, if they're in store they want all these things to be native to that experience, but again, like 6, sort of integrated and 5, experiential, so all these dots connected. Not that they're all physical silos that are supposed to be constructed in of their own bubbles; they're supposed to reflect the blurring of the lines that's taking place as the customer evolves. Look, the stakes are getting real. When we see store closures or when we hear about Retail Armageddon or the Retail Apocalypse, what's really happening is that customers are finding that those experiences are no longer good enough; they no longer match the experiences that they love and that they enjoy, and they are making no divides about what experience is great over here versus what experience is great over there, what industry this happens to be in versus what industry this isn't in, and they are all blurring together. If you look at any business, retail aside, just every business, every industry, most organizations are still struggling with this, and that is why disruption is so rampant, yet also the more mature and more digitally transformed organizations see this as a competitive advantage. This is where they're winning. You have, the slide looks like it broke a little bit, but you have highly complex enterprise infrastructure supported by disparate systems and this is a really big deal. These disparate systems aren't talking to one another. It's not just so much about the people, it's also about the data that's strewn across the organization that can't be leveraged as fast as it needs to be to deliver that integration or that personalization. There's also conflicting direction, goals and metrics. We're still operating in silos; we're managing hundreds of concurrent programs, and by programs I mean whether that's promotional campaigns, whether that's product rollouts, whether that's seasonal engagement or whether that's technology systems. They're all just sort of being run across the board, not necessarily together. If you look at where companies are prioritizing their technology investments, in the grander scheme of what we're talking about as the customer evolves, as technology evolves, as AI becomes more and more prominent, companies are prioritizing stuff that's just so dated, in email, websites, digital advertisements. This is exactly why we're seeing so much disruption around legacy brands. If you look though at those who are winning, experience innovation is a differentiator and that's where those investments are being made. Because when innovation succeeds, the customer wins, and when the customer wins, everybody wins. A lot of my work has been really diving into customer experience that levels that I want to go beyond, okay, well let's improve touchpoints. Let’s make things more integrative. Let's make things much more personalized in the right time on the right device in the right context. Really, at the end of the day, the customer experience is theirs. This is one of the reasons why when I talk about CX I add an apostrophe at this, the customer's experience and the customer's experience is the sum of all engagements that a customer has with your organization in every touchpoint throughout their journey. So this is as much about what they see and when as much as it is about how the backend operates, how data powers that operation, and to what extent the speed of which it does so. Because customers will find their way, and it's not going to be anything like what we think it's going to be like, and that's where mindsets and perspectives need to shift. I call it digital empathy. We have to think their way, through their lens, through their mindset, which is the only way to close that Experience Divide. I often ask retail executives if they remember the exact year that Amazon disputed the entire retail industry. I'll ask executives in general, "Do you remember when Uber disrupted the taxi industry? What year was that?" And the reason I ask that is because it's actually not an overnight answer. It's an evolution; it's a continuous evolution and it's not done. Amazon is not done disrupting anything. It is continuing to disrupt retail and once a customer experiences the benefits of that experience, so whether it's online, whether it's Prime delivery, whether it's walking out of a store without ever touching the cash register, this becomes a profound experiential shift in expectations that customer hold as a standard for everything. Then though, what becomes so powerful and the message that I really want to communicate here today is it's not just about what the customer sees; it's everything that they don't in order to be able to see something much more profound and meaningful on the other side. This is an article from 2017 that talked about Amazon's future in AI, logistics and Cloud. This is exactly where everything business needs to go. This isn't new. This is three years old. I'm sure there's articles that talk about this longer. This is what we have to see. It's a transformation. It's not overnight. It's continuous and it's always happening and in many ways what we're seeing today started years ago. The same is what's happening, for example, in the D2C disruption of traditional commerce, traditional brands. The number of incredible D2C companies that are succeeding, and also those that are being funded, it's mind blowing. In fact, I was having a conversation with the founder of a group called Brand Innovators, and because this is becoming so huge that Brand Innovators, which help challenge your brands and legacy brands compete through innovation, is now having to dedicate an entire track just to direct to consumer brands that are just springing up all over the place. But what's interesting with it, they're all data driven, they're all digital. They all build an infrastructure that's not unlike, for example, Amazon. They're using AI, and they're using data to power new ways to engage people to just create new products. They're also taking that data and those insights to find you ways to create physical store experiences at a time where, oh my gosh, we're supposed to be in an era of Retail Apocalypse. Now what they're finding is how they just deliver better experiences that customers want. So this is why data becomes so important, so critical. I call it the Relevance Revolution. Essentially, once we start asking different questions of data we can apply any different platform to be able to solve this. But, again, it's not just about what the customer sees, it's also how we operate — the back end of how all of these disparate data systems are able to feed a centralized system to help us get insights faster, to help us improve and accelerate our decision making. The type of decision making that delivers better results and better outcomes faster so that we're starting to mimic digital, that we're starting to mimic more innovative legacy organizations, that we're starting to realize time to value much faster in ways that improve the bottom line. So one of big burdens is ERP and also CRM, but there's been a lot of movement on the CRM side of things lately. It will only continue to accelerate, but ERP has just been terribly sluggish, yet it is the lifeblood of how an organization excels, delivers, creates. All of these things are so profoundly necessary for innovation and acceleration of that innovation. I had the chance to work with Aera over the last several months and exploring its roadmap for how it's going to transform enterprises. Specifically, I was looking in those post-digital transformation era of my own research and was fascinated by this concept of what Aera calls the self-driving enterprise, which is how cognitive automation starts to enable this real-time decision making that it's just unbelievable, yet it is the source of new possibilities. In fact, Fred Laluyaux, the CEO of Aera, whom I’ve spent quite a bit of time with and learned quite a bit from lately, talks about the future of work this way. We're shifting from an era of people doing the work supporting by machines to machines doing the work guided by people. It creates this new genre of creativity and strategic thinking. It unlocks possibilities that once we're able to move things into this level of automation and augmentation, imagine the possibilities of how retailers or any organization can operate. To close that Experience Divide we have to look at many things, of which data is the answer. Do we employ real-time data driven empathy to understand customer behaviors and expectations? How is my experience at every level performing against customer expectations? How do I deliver the most relevant and high potential offer that matters to the customer in that moment? And does our infrastructural align with the above and also the rapid evolution of the industry? These are tough questions but they have to serve as the prioritizers for what we doing leaving this webcast. I often say that if you're waiting for someone to tell you what to do, you're on the wrong side of innovation. It's that creativity. It's like how Fred talked about the future of work. If you free up the burden of repetitive decision making or tasks or execution, and you free up that level to think much more strategically about the industry, about performance, about needs, about choice, that creativity is exactly the pillar of innovation. So, on that note, it's been a pleasure to have the opportunity to share some of research, some of my work and some of my thinking with you. Now let's turn it over to Fred Fontes of Aera Technology. Fred, take it away. Fred Fontes: Thank you so much, Brian, and at Aera we really couldn't agree more with your perspective and the insights that you shared are really the challenges, the disruptions and the opportunities that folks at retail and CPG have in front of them. As a result of this wave of digitization, both on the consumer and the enterprise side are massive and really core to the belief that drives our vision, is that the scale, the complexity and the pace of decision making in this digital era requires a fundamentally new way of working. Organizations that have been built around these traditional pyramid structures with siloed functions, systems and processes need to transform to become more agile, more connected and still driving decisions a lot closer to the point of impact. It really for us is not just a question of adapting what consumers are asking for in terms of their expectations that have arisen over the past decade, but it's really also a question of what is the workforce of the future is going to expect out of the organization. You mentioned what is Generation C going to expect their day-to-day work experience to be like? Is it going to be voice and mobile first just like everything else in their day-to-day life? Or is it going to be based on enterprise systems that were designed and built decades ago? How do you as an organization expect to train and expect to retain the workforce of the future in that setting? So to do this, as you mentioned in the quote from our CEO Fred Laluyaux, we believe this requires a fundamental shift in how work is done and moving from an era people doing the work supported by machines to an era of machines doing the work guided by people. It's really a fundamental departure from the last few decades of enterprise software, which have focused on giving teams incrementally better tools, platforms that allow collaboration, Cloud-based solutions and interfaces, but still fundamentally expect the human in the loop to analyze information, to arrive insights, make decisions in a world in which machines are autonomously driving the decision making process and engaging operators if and when they need guidance, experience, insight or support. If you think about what this looks like for an organization like an Amazon or a Stitch Fix or any of the other digital datas that you referenced earlier, they no longer have human planners making decisions about what should the forecast be at a particular location, for a particular product or what should my inventory levels be? Somebody else in the organization thinking about pricing and all these groups infrequently communicating, maybe in a monthly SP meeting about what the outcomes of their decisions are and how to interconnect them. Instead what you have is essentially of algorithms that are making decision about, inventory, forecast for every single product and every single second in full real-time coordination with each other and it's not a new phenomenon. Like you said, organizations long have been working on these problems for years. What is holding them back. At Aera we fundamentally think in order to make this transformational leap organizations need a brain for the enterprise or we think of it as intelligence, it has the following characteristics. First it has to be connected and outside in, meaning that it fully understands everything that is going on within the four walls of your business and your enterprise systems. It is also able to connect to the outside world and consider signals from your customers, your vendors, and third party data sources. Second is that the system has to be real-time in hours on, meaning that it is constantly evaluating risks and opportunities, and is not held back by products, processes or cadences that run on a weekly or monthly basis and just don't move at the pace at which organizations and the environments around them is moving today. Third, the system has to be thinking learning, meaning that it can apply rules and it can apply data science, optimization, other approaches to programmatically evaluate the systems, a.k.a., think, but more importantly it learns both from its own performance but also from the interactions and expertise from the human operators that are writing the system. Lastly, but possibly most importantly the system needs to be autonomous, meaning that it can take action directly back into the transaction execution systems without needing a human in the loop. These really are the four characteristics and the foundation what we've built at Aera. Now what does cognitive automation really deliver to the business, and how does it help automate and augment work at scale? Let's take the simple example of demand planning. Just like a business analyst, a planner, would understand what is happening in your business looking at your historical sales data, upcoming promotional events, point of sale volume information that you've gotten from customers and partners, external factors and many other sources, really building a view of all the factors that drive demand. From there, again just like your analyst, it would recommend a new course of action. For example, adjustments to a demand forecast or revisions to a financial plan that is no longer valid as the expectations for the future have shifted. From there, it likely predicts the impact of the recommendation. For example, what is this new demand forecast going to do for inventory levels that we need to hold, for implications on new forecast or annual financial targets? Then, lastly, it takes action, automatically adjusting the demand plan and, just like a business analyst within your organization would, the system has identified an opportunity or risk, determined the best course of action, taken action and consulted you whenever necessarily to do so, say for a major deviation from the plan that requires human intervention. Now you might say, "Fred, I got it. That sounds fantastic. What's holding organizations back from achieving this model today?" In our perspective we believe it's really related to needing to deliver on the following four pillars with a single interconnected system. First, it's a question of bringing 100% of the data that's required to make a decision in to a single data layer within the enterprise. Why is that so important? Well, human operators are great at working ambiguity, making approximations or assumptions. Machines really need to have all of the data at hand in order to make a digitized process work. Secondly, it's a question about applying science, whether we're talking about rules, machine learning, optimization or data science, analyzing the data, building predictions and driving recommendations on top of that harmonized data across the enterprise. Lastly, it's a question about decision-making processes and the digitization of those end to end, in a manner that is scalable, flexible across business units, regions and product groups. It does not require every single process to be adapted to the underlying system that it is working on, but working a layer of extraction that allow you to design a single process for your enterprise and easily scale up. Then lastly, a question about operationalizing change and to us that's not just in terms of driving action back to the transaction or execution system, but more importantly it's a question of building a permanent memory of how decisions are made within an organization and how operators interact with the system in real time. While these concepts may sound farfetched, some of the largest complex organizations around the globe are well on their journey. They were the earliest pioneers, we've listed on the slide here. For example, J&J where Neal is using cognitive automation to improve the customer experience at one of the largest and most complex medical devices organizations around the world, getting B2B customers the same level of visibility and product availability and order status that you would expect from a D2C brains, as a result driving higher service levels and much better customer experience. What's particularly interesting about this example, Brian, is that Neal is ex-Amazon. He saw what was possible made at speed and scale, and he's working at J&J to replicate that agility in an organization that has 134 years of history, but also has the processes and the system legacies that come with that history. At Merk, Alessandra's is leveraging cognitive automation could drive the ever-critical service levels that a pharmaceutical organization has to maintain, ensuring that life-saving drugs make into the hands of patients by micromanaging how orders move across their organization and making sure that nothing falls through the cracks. The key at is trying to how the organization thinks through that forecasting letting machines autonomously drive the process in a completely touchless manner and thinking about promotion optimization, where teams now start with a data-driving approach to promotional calendars versus what is traditionally been an incredibly manual effort that starts often with a copy paste of last year’s activity. Lastly at Unilever, where Mark is fundamentally rethinking what a side chain organization can deliver for the business, focusing on cognitive automation on topics like inventory management, transportation and logistics, and emphasizing the way and impact that this new way of thinking can have on their environmental impact that supply chains have to their global front. And these are just four examples of early pioneers that opt in cognitive automation to transform their business, improve the performance of their organizations and unlock new opportunities and business models for enterprise. There are many more organizations that often use technology to tackle challenges and opportunities, not just across supply chain, but across sales, marketing, finance among other topics. The future of the enterprise really is here and we couldn't be more excited about the opportunities that it brings with it. With that, I'll hand it back to you, Alarice, to walk us through the Q&A section. Rajagopal: Thank you, Fred and thank you Brian as well for the insights. We do have some time for questions. So the first question I want to look at is about insights. "Do you have any insights or point of view on retailers" — and the example given here is B8ta, I believe — "who are making experiential learning the center of their design?" Brian, can we start with you? And then, Fred, if you have any insights on that as well, that would be great. Solis: I'll give it a crack, but I know that Fred is going to really be able to talk about the potential of the backend of this stuff. For those who don't know what B8ta is, B8ta is sort of this new generation of bringing this B2C and Amazon like insights to physical spaces. So they're behind two of the new, or I should say renewed, Toys"R"Us stores that recently opened up last year, and they're a much smaller format, but what they're doing is they're bringing in new experiential sort of retail formats that are in real-time learning about how customers are interacting with products and also how they're maneuvering through the store itself, through things like computer vision, data analytics. They're able to understand how to optimize what products they place, how they place, where they place, etc., and then also all of their online research dictates what types of innovative products they want to put in the store. So it's not just so much about coming in and buying something; it's also about coming in and experiencing something that then you choose to purchase, which may or may not actually be in that store. So it's sort of putting in this new technology layer to enliven a different type of immersive retail experience that quite honestly is really fascinating and the level of technology around it, it's really about learning about how to constantly improve that. Fred, maybe you'll have some thoughts as well. Fontes: B8ta is a great example. I think I look at some of my former colleagues from McKinsey launching the modern retail collectives at the mall in Minneapolis. There's a lot more focus on how do we make physical experience an opportunity to learn from consumer behavior just like we're able to from online, web interactions? I think the challenge that comes along with that is, yes, it's fantastic that we have access to new datasets and new insights about what consumers are doing in that physical space, but we now need to think about how do we actually develop the processes, the systems, the routines to take action on that at the speed that consumers expect from an online experience. If a consumer comes into my store, I gather new data on their preferences. I also need to send them products to their home directly from an order they made from an iPad in the store. I need to rethink how my organization is able to deliver on that, but I think it's an exciting evolution for what's going on in the physical retail world because it's reinvigorating what I think what a lot of analysts over the last five, six years have said, is the dying side of the industry. Rajagopal: When you look at events that we hear of weather events and now even the coronavirus, how do you see cognitive automation helping organizations with supply chain responsiveness and resiliency? Solis: In the conversations we've had in the past and this is where you shine, the key is to understand how to shift that mindset, not to just “How do we improve the processes that we have today?” or “How do we throw automation at repetitive tasks?” or “How do we take excerpts of data and explore where we can make incremental improvements?” I think what this really is about is looking at sweeping automation and bringing those disparate sets together in one place where we can actually translate those insights into business outcomes. So this really now starts to take less of a vertical or specialized set of capabilities to look at how does something as critical as this, how do we use cognitive automation as a way to deliver business outcomes that actually transform the business from where we sit? Fred? Fontes: Yeah, I think it's a question of how do you use cognitive automation to spot trends, spot disturbances in the supple chain or in demand earlier than you would through traditional cycles? And certainly it's not limited to the human crisis what we're undergoing with the coronavirus, but gosh, think about the myriad of things that have happened over the last couple of years that have had supply chain teams up late till the wee hours of the morning trying to figure out what to do, but it's also a question of how agile is that response? How quickly and how automated of a fashion can you understand what's the impact of my tier three supplier in Southeast Asia or in the Middle East, being in the middle of a crisis? What product is that going to impact? Where do I need to go find alternate sources of supply? How do I let customers know that a disruption is coming? It's really a question of how do you become more agile on all of those fronts, as Brian said, by bringing all the data together into a single layer for the enterprise, but also automating a lot of the inside generation and decision making that needs to come as a result of these changing trends and disruptions. Rajagopal: Along those lines, we’re getting a few questions on specifically Aera. I have one asking if it is a best fit for midsize consumer products companies and what's the timeframe for seeing the ROI on an investment like this? Then also, I'll combine it with another one that came in: “Does it provide consulting services, and then” — I know this is very loaded — “how do they start?” Fontes: Yeah, absolutely. Let me maybe piece that one apart. So best fit, I would say typically for organizations that have at least a well-established ERP base, but from there really size becomes a little bit less of a concern. We really, I would say, excel in helping organizations that are looking to do more with less and have complex supply chains to the point of maybe the first slide in my presentation, right, where you just pace the scale, the complexity of the decisions that they're trying to make are outpacing what they're teams can manage. In terms of timeframe for ROI, I would say the beauty of a lot of the work that we've done with our clients is that it's extremely fixed. The nature of the operating system that we've developed allows us to deploy in a matter of weeks relative to what in traditional implementation used to be months if not years, and because we are driving decision making, not necessarily expecting organizations to fundamentally change how an individual does their work, the time to value is incredibly quick. So we've got clients where within six, seven months of Aera being deployed, really starting to work with Aera, they're seeing significant ROI within their business. And then on the consulting services side, just to quickly round out that answer, yes, with we provide professional services that can help you all the way from cognitive automation strategies, where do you apply these kind of technologies and what kind of problems do you solve with them all the way down to actually new technology to solve those problems. Even though it is a SaaS platform that is meant for your teams to use. Rajagopal: While I'm grilling, Fred, in the process, what sort of uses and experiences does Aera have to drive sales growth in the FMCG industry? Fontes: I would say one of the strongest use cases that we have so far is in the area of trade promotion optimization, where we've been working with RD among others to really fundamentally rethink how that process runs and drive it into much more automated fashion. So that what used to be manual work and driven by limited analysis and honestly a lot of gut feel and experience is turned into a process that is fully automated, fully digitalized, operating in real time and responding to changing working conditions and driving really what is an optimal answer and an optimal outcome when it comes to driving sales through promotions. As we deployed that use case, we're also spending a lot of time thinking about how do you tie other levers across revenue management into the process when we're thinking about pricing or some of those other things we can fix, and we think there's massive room for improvement on not just current processes, but also current technologies in the space. We're doing a little bit of work around broader sales and marketing topics, whether it's sales and territory management, helping companies really understand and drive the effectiveness of their sales team using cognitive automation, but also looking at things like real time budget reallocation across marketing levers where you can be much more dynamic with how you're driving growth when you're not tied to quarterly plans or semiannual plans that determine in a rigid fashion where your marketing dollars are going. Rajagopal: I just want to backtrack a little bit and ask how cognitive automation is different from an AI powered ERP platform or RPA? Fontes: Relative to ERPs, I think it's really a question about a system of transaction or a system of execution versus a system of intelligence. ERPs have gotten exceptional about managing tasks and processes at the transactional level, but are still really struggling, even within a single ERP to paint that complete end-to-end picture of the enterprise that allows insight generation, decision making and automation to happen all at once. So really that's, I think, the differentiator we provide, is we're that system of intelligence above and beyond the ERP to really help the company drive decision making at a much higher level of accuracy at a much higher pace than you would again with teams of people operating on those traditional ERP platforms. Relative to RPA, I think it's really a question of moving from the task level. I think RPA is absolutely excellent for, right, go process an invoice or update a transaction or click through a series of screens, copy/paste data and execute a task. Whether or not that involves artificial intelligence, I think there are plenty of use cases of OCR or classification within an RPA process, but really moving from that task-level to enterprise-level decision-making processes. So again, compare that, go process an invoice to scan my entire supply chain, look for deviations or disparities between supply and demand, go evaluate opportunities or possibilities that I have to resolve that disparity between supply and demand, evaluate what the best option is, execute on that option and then record what was the underlying background that drove that decision, what was the decision that was made, what is the action that was taken, and what was the impact and learn from it. Rajagopal: We have one more coming in, sort of along the same lines. "From your experience, how long does it take to bring all the data in one place in a company with ERP, CRM and social data to fully leverage cognitive automation?" Fontes: This is a fantastic question, and we get it quite often. I would say for us because we've invested very heavily into what we call our crawling technology, which is really connections to all of the major ERP, CRMs and data sources that you might work with, not just the connections but really a deep understanding of how those systems are structured table by table. It is a question of days or weeks. So I can give you an example of one of our large CPD clients. They've got 47-plus ERPs around the world, I think it took us four to six weeks to connect all of those ERP systems, the CRM systems, they were tied to the plannings, since they were tied to and then just then harmonized that data into a single layer. Now if you're working with super exotic systems, that timeline may be stretched a little bit, but I think we have made tremendous investments into both those crawlers and the data layer that those crawlers feed into that really allow us to accelerate that process and it's one of the main unlocks that our clients often see them working with us and that is help quickly we can solve a problem that has historically been a question of roadmaps, like painful data link developments and date warehouse developments that seems to never end.

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