Nitin Badjatia is the Senior Vice President of SAP CX Solution Management. In this conversation, Nitin walks us through the delicate balance between surveillance technology, access, and individual rights with a compelling business model – Trader Joe’s. This profitable brand has managed to thrive without advertising or loyalty programs, by simply talking to customers and understanding their needs.
As our conversation with Nitin takes a deeper dive into the role of AI in contemporary organizations. We bring Peter Drucker’s 1959 ideas on knowledge workers and Taylorism back to life, examining how they hold relevance today. Discover how the right data can steer you towards the right outcomes, and how AI is forging better relationships by providing understanding at the point of need. Get a peek into how AI is revolutionizing customer service, by accurately categorizing inbound cases and severity levels. Strap on your seat belts for an enriching discussion on how AI is supplementing human decision-making and what it means for the workforce of today.
Full Transcript Follows
Today I’m with Nitin Badjatia, who is Senior Vice President of SAP CX Solution Management. It’s a mouthful of a title because Nitin has a humongous job, but rather than have me elaborate on that from a distance, from the sidelines Nitin, welcome. Thanks for joining me and maybe tell us in your own words what you do at SAP and what kinds of things you’re working on.
Thanks for a stop, mike, for having me on for this conversation. It is a very large title but I think the role is fairly simple when you think about what we’re trying to accomplish. The portfolio of CX is a fairly large one and really CX Solution Management in the operations of the way SAP positions itself falls into a category of straddling the needs and wants of two distinct parties inside of the organization. But we also work very collaboratively with them. First and foremost is from an engineering perspective. Our role with Solution Management is to provide engineering a over-the-horizon view of specific areas, give them some guidance on white spaces where maybe there needs to be some development or engineering effort, and then also we take that and we inform our marketing organization and our go-to-market, our selling organization, on how best to position in the marketplace.
Wherever there are gaps, there’s always going to be gaps or there’s going to be opportunities for partners to come in. That’s where the solution gets separated from the product. Engineering builds product and we work and to also help define where there are areas whether they be specific to geography or application stack or industry, and build the solution that our customers ultimately want and that our sellers can sell collectively as a package.
So just so that I can kind of fit this in my own puzzle map, is the role more kind of consultative for consultative purposes versus consultative slash liaison for product purposes? Does that make any sense?
So in many ways we, the Solution Management team, which reports into Julia White and Sven Denecken, and their role is known as a CMSO, so it’s chief marketing and solutions officers. So in many ways we are informing the corporate strategy around a specific discipline for us at CX and CX for industry, but we also it’s not just being a side influence the fact is that we actually build documents. This is the world of engineering we’re ensuring. Obviously we’d like to understand what’s happening in the market, but also we help define the market requirements in documentation that flows into the marketing organization as well as into the selling organization so they can understand how best to position the innovations that are coming out of engineering.
The other thing that I would say is the Solution Manager role at SAP, broadly speaking, across all of the disciplines. No single social manager. Every social manager does things slightly differently. It’s really based off of the maturity level of a product line or the relationship with engineering as well as the selling organization. Ours, for example, is very tight. We have a very tight relationship with product engineering and I’m on daily calls with our go-to-market organization as well. So we feel we’re very well aligned in CX. Much of it, like I said, is hard documentation, but some of it is also soft influence through that process.
Thanks for the context. This conversation could easily take us down another two-hour dog leg easily. But just to mention that, even I’m aware of several startups, a couple of them really well funded, that were and I think one of them is still active that were set up specifically to help consumers gain more control over their personal data and, in certain cases, monetize it or at least control it. It seemed like the technological hurdles seemed like they were more manageable than the cultural hurdles and the educational ones, largely for the consumers.
That’s right. I think part of the challenge and I’m going to bring in another organization that is one that I’ve worked with which is the People-Centered Internet (PCI) Mei Lin Fung, who’s a Hossel-Platner Institute fellow, along with Vin Cerf and others, are focused on essentially providing connectivity and access to the 50 percent of the global population that has limited access right now and a big part of the concern I wouldn’t say a primary concern of customer commons at the time. But if you’re driving surveillance technology into individual behaviors, then you fundamentally impact the rights of those individuals. So, working together with PCI, there’s a desire that PCI’s direct focus is the lower, the bottom two billion of global society as they come online, respect their rights, give them access. But the challenge with access is it removes without the context of what individual privacy is. You run into all of the surveillance components that unfortunately at the founding of the Internet and Vin Cerf’s involvement in that I think is really critical. There are certain aspects of standardization because the Internet by design was decentralized but it did foretell commercial transaction. So that’s been one of the challenges that we’re trying to overcome.
An example of this, very quickly, is that in the United States the grocery chain that has the highest per square revenue in the industry is Trader Joe’s. What’s interesting about Trader Joe’s? They don’t advertise. They said one flyer out here. They don’t have a loyalty program. So how can they be so profitable? Largely because they talk to customers. They listen to customers and they produce what customers want. So surveilling customers for them has been proven to be it doesn’t return the value. So you see this across many industries where there’s a commitment to durability and longevity, that relationships evolve over time and they are not the sum total transactions and interactions.
We could title the next conversation with what you just said nicely, and have an awful lot to talk about there. I’m very serious. I would love to schedule another conversation or several to explore this further, but I want to spend a little bit of time on what SAP is doing in the marketplace in terms of enterprise CX. You have a unique perspective on a lot of CX activity by nature of your position there. I would love it if we could spend a few minutes talking about Nitin’s global perspective. What is it that you see from your corner of the universe? Cx let’s even go as broad as opportunities and gaps and trends.
Sure, I think that somebody has been in this space for approaching 25 years. What CRM evolved into CX and is now morphing into something, I think, even bigger. The realization is that there’s two things that I think are really important. First is from the lens of a customer. No customer ever wants to know the complexity of the organization they’re dealing with. Yet, 20 plus years into developing these technologies, what happens? You call in and you’re routed to one department and to another department and then maybe you have to go find something, or the person that’s engaging with you doesn’t have access to all of that relevant information.
For us at SAP, powering so many industries in the way that they run their supply chain, the way that they manufacture, the way that they deliver products and services, inventory management, financial management, surfacing all of that relevant to the interaction that happens at the point of customer experience is what helps us drive unbelievable context for that experience to be successful. This goes back again to the point I made earlier that an experience hopefully drives a durable, symbiotic relationship with customers. It’s not a transaction. The fact is, it should have more meaning. That’s really a critical piece of the puzzle. The other part of that for us is really that other part of the story, which is customers don’t want to see complexity. Most of those on the other end organizations have scaled, because they’ve scaled basically on departmental applications. But the way that I fashion this is that and my background is mostly in customer service I tend to rely on that as an example.
But customer service is not an activity confined to a department. Serving customers will allow you to activate the entire enterprise. That is a design philosophy for our CX. When you start tailoring that towards industry, the behavior is the requirements of those processes, and understanding the flows and where you might need to connect requires you to take what I would call 50 years of SAP mastering industry and surfacing that up in the experience itself. We know that a fast food chains customer interactions are very different than a defense contractors. So designing for that and understanding what those process flows are, understanding the underlying data construct, and then there’s a huge, what I would like to refer to as the dark energy of the enterprise. These are the flows that happen across departments that have been hard to capture for very long time, that we’re starting to capture now.
This is how work gets done. Nobody knows how it gets done, but it gets done as we start working on that. An example of that is our very strong partnership with Microsoft, and Microsoft Teams is that when we’re meeting on with conversations, having conversations and what in the customer service world is referred to as intelligence forming, when you’re trying to solve for a problem and you have to bring in people from various departments and you’re working together on Teams, teams can capture the transcript and, using generative AI, we can summarize that and place that into the case log. What that does is all of that stuff that was happening in the ether, in the dark energy, is now captured so you can analyze and understand and drive what I like to call the next phase of customer service, which is really resolution intelligence. It’s about going something bigger and deeper than logging a case. It’s about solving the problem.
Often that problem goes beyond just the reason for the immediate call or interaction. That’s right. There are relational components that are usually backing all that up. It seems like I’m going to paraphrase. It’s always dangerous, but it seems like what you’re talking about here. As this next phase you described it is a phase of CX that is connected across the enterprise, where senior management can tap into the insights of departments outside their own to understand context, to understand sentiment, to understand solutions that might otherwise have been siloed.
That’s right, I mean. Another classic example that we look at in customer service is if you have a high volume context center that’s engaged with a complex product. You see this a lot in certain industries where an inbound exception that you start working on certainly cascades from one agent to five agents to 200 agents and now you’ve got everyone bogged down working on the same problem. What we’re looking to do is say intelligence says there’s a signal that’s coming in that looks awfully similar to others. Let’s roll that up so you can work on from an intelligent swarming, which is a methodology that the industry is favoring. Let’s push that off so we can swarm together and solve for the dozens of issues altogether. Now it’s great to solve for the immediate, but let’s say it was tied to a product defect out of manufacturing or a supply chain failure. Now you can educate through the stream as to why this issue arose. It gives you a much more strategic view on the long run as to what you want to do On the flip side, in sales automation, for example.
Imagine a world where we’ve lived through this. We’re through the pandemic and the supply chain shock and everything else. Sellers still have to hit a revenue target. Oftentimes the revenue target is tied to whether the product that you’ve sold has been delivered. Imagine a world where your selling intelligence, your management, can be in real time aware of what’s sitting in a warehouse as opposed to what’s sitting on a container ship. Therefore, you can educate your sellers to, say, sell Widget A as opposed to Widget C, which has not arrived yet.
This is a different way of looking at opportunity management. This is about looking at what can we actually sell as opposed to what promise are we making? Again, this goes back to take this back to the customer comments respect the customer, drive durability, sell them what you have and not a promise. I think many of us have lived through this where in the last several years, we’ll be placed in order for something and then suddenly you get this in in-bond email saying it’s delayed by three months. I know I had that problem with my daughter and we were going to buy a bike. It’s hard to tell a nine-year-old that that bike that she really wanted is stuck on a container ship in Oakland, California, when she wanted that bike. It’s a practicality of that. The fact is that so many of those systems that power all of that are powered by SAP and we’re surfacing that data, that intelligence up in the world of customer experience to drive more meaningful, durable relationships.
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We talk about this kind of I do anyway in two layers. One is operationally, what we’re trying to do within the organization, getting things done, the physical touch points, kind of more tangible aspects of these tasks. But there’s a whole cultural aspect that is and we even mentioned this when we were talking about customer comments education and culture and the things that kind of come along and create silos, like management’s KPIs, like how we’re incentivizing people, the way organizations have been structured, can all be just impossible hurdles if they’re not managed well. So it seems to me that maybe even the bigger hurdle here is using I think you’re probably in a great position to do this using the decades of insights that you’ve called to prove the cultural case so that the operational case can be sold through. Yeah, I totally agree.
And here’s the way that I think about this. Peter Drucker in 1959 wrote about the knowledge worker. The knowledge work is something that’s as he says. It’s all voluntary. I don’t have to tell you anything if I don’t want to I mean it was my job as a result but the fact is that it’s hard to measure the output of a knowledge worker. A quick example of that is from in customer service world there’s a methodology called Knowledge Center Service full disclosure. I’ve helped write it over the last two decades. Knowledge Center Service says that a knowledge article if somebody writes only one knowledge article but that knowledge article is used 50,000 times, it’s more valuable than somebody writing dozens of articles that never get used.
So it’s not about activity, it’s about what the outcome looks like. Yet when you look at industrial scale, it’s actually from an earlier philosophy, Taylorism, which was to digitize and grow. Well, the last 50 years have done. We’ve taken the evolution of knowledge work but still applied a lot of Taylorism to it. Right is activities. We’re now at the spot where I think the realization is that knowledge work is amorphous per se, but when you have access to the right data you can drive to the right outcomes, and I think that that’s a big part of the opportunity we have at SAP is that, yes, we do the manufacturing, which is essentially Taylor, scaling a factory so you can produce more widgets, produce more quality widgets, but then also shift our thinking in the world of Peter Drucker and say knowledge work is more about outcomes and really measuring those outcomes right, and that’s a big part of you know. I think that we want to drive with the world of CX.
I’ve got another 30 questions for another dog leg. That will wait for a subsequent conversation. But the one thing we haven’t talked about here and I think we’d be remiss if we didn’t at least just touch on it is AI, and what I’m thinking of is how SAP views the concept, deploys the concept, is building it out with all of its. I think we’re at this really.
I’m going to say “inflection point” right now, because for the past 30, I don’t call it 90 days or so it seems like the news cycle has been all about the problems with AI and what it’s about to do to us, and the sky is literally falling if you listen to certain outlets. But as people who are probably closer to it I’m sure you’re closer to it than I am, but I talk about this and hear about it all day long I certainly don’t have a sky as falling perspective. I’m really interested in what yours is, what SAP’s is, and how you are going about building for the next phase of this, because I almost feel like AI in its current form is almost it’s not worth talking about because it morphs so quickly into the next thing that we might as well, I think, the wise conversations to talk about how it’s going to be used in the next six months, 12 months.
Yeah, so I think you’re right. I think AI has become a catch all phrase for a lot of things and, frankly, artificial intelligence or machine learning has been a foundational element of so many of the processes that we activate in customer experience, that they’re there, they’ve been there. Things like auto classification and categorization of an inbound case or lead scoring in sales automation. Those are supervised machine learning models that have been deployed the CX stack and have been there for quite some time. I think that I refer to what was a technical conversation to escape the air gap and met it into general society.
Now gen AI is driving all of the conversation today and in that world, first and foremost. I think in many ways we’re privileged to sit at a company like SAP, largely because of its European heritage, but does fundamentally understand that there’s an ethical role for AI to play, that there needs to be guide rails around what we design and deliver. We are building business intelligence, and business intelligence for us is how can you drive better relationships, drive more contextual understanding at the point of need and utilizing a blend of technologies?
There are LLM capabilities that many of the large technology vendors have built that are being provided at scale. We’re using those technologies but also using SAP’s own proprietary AI to build use cases that are really important for customer experience. Again, I think AI generally define whenever there’s a broad asymmetry between the volume of data and the intent at a point of need, ai serves very well because somebody interpretation and scale From that contextual awareness. Again, we’re really fortunate at SAP because we power so many of the operations or organizations that we can drive much more context in the world of CX. In the world of CX we can tap into that and surface up that contextual awareness that many other CX vendors would have to design and construct. We’re working on it from that perspective.
It’s really about efficiencies, operational scale and doing things like I mentioned before about when you’re intelligent swarming in a customer service scenario, being able to summarize that conversation and plug that into the case which, let’s be honest, when you have customer service involved in complex conversations, does anybody go back and write the notes, or if the notes are really taken in the real context or not. That becomes a challenge and, quite frankly, in many industries it’s now becoming a regulatory challenge in that there’s an audit function required. You need to say this is what I said at this time because of this, there’s legal constraints that actually AI is enabling us to fulfill. That would have been a burden on most organizations in the past.
In my role, what I get to do all day long because, folks like yourselves, I’m seeing something of at least a perspective shift in the way that AI is being talked about with a lot of technology vendors. That is that it seemed for a while like AI was assistive technology on an application to bring some efficiencies to it. Bring some pretty cool efficiencies, pretty amazing efficiencies. If I were to analyze the conversations objectively, what I think I’m hearing is that AI is being brought closer to the center of large platforms and larger conversations and larger problems that need to be solved, rather than just a facilitator of a solution. I don’t know if that makes any sense, but I’d love to hear your thoughts on that.
I think it’s a funny thing, my non-technical friends. Like I said, the technology skipped the air gap into the common consciousness. The term chat GPT pops up right. Chat Chatting is still a very complex human behavior. Is it the right place to apply AI? Maybe, maybe not, if you have the right guide rails. When you can define the guide rails, what you can do is accelerate the decision for humans. Instead of having to chase the auto summarization of a conversation. You don’t have to go look through the entire transcript. You can say well, this summarized and over time AI gets better and the summarized will get more intelligent. That lets you decision a lot better.
So it’s really about augmenting human decisioning in the places where it becomes critical. Now, one place where we’ve seen great success, where human decisioning actually was slowing down the process, was in customer service categorization of an inbound case and severity level Most humans. What we found is that categorization based off of the inbound data AI is about 85 to 90 percent accurate. Most humans are about 80 percent accurate. It’s actually more accurate. Quite frankly, it works faster. So on that element on how to route AI is pretty effective. But I think it’s really about this surfacing up the right information to drive decisioning, which is still ultimately a human trait, is where the value resides.