 From around the globe, it's theCUBE with digital coverage of IBM Think 2021, brought to you by IBM. Welcome to theCUBE's coverage of IBM Think 2021. I'm Lisa Martin. Today I have Savio Rodriguez here with me, the VP of Integration and Application Platform. Savio, it's great to have you on the program. Lisa, really great to be here. Thanks for having me. We're going to talk about automation integration, one of the things that we're going to kind of break down versus is hyper automation. Gartner announced that about a year and a half ago was one of the top 10, I think it was the top 10 strategic technology trends of 2020. Well, here we are in 2021. Before we talk about automating integrations, give me IBM's perspective on hyper automation and what did we see in 2020, like reality? Yeah, no, great question. So at IBM, we believe that the next title wave to hit organizations will be really the task, but frankly, the opportunity to automate the entire enterprise. And by that, I really do mean everything in the enterprise. So Gartner, when they talk about hyper automation, they're absolutely right because they're focusing on automating business tasks. But IBM's point of view is broader than that. And so we want to think about the work that business professionals, IT developers, that IT staff, security focus administrators, all of that work. And we think that the real differentiation is going to come to organizations that attack the task of automating work for all three labor types, business, developers, and IT. So hyper automation focuses on the first labor type. IBM's approach is looking at all three labor types. Now, you should pick automation projects that are specific to one labor type to begin, right? Instead of saying, let's automate everything. But the latter is a strategic statement, the former is tactical. And we're seeing clients automating specific business processes like order to cash, and then others are automating work of IT admins, such as reducing the number of security vulnerabilities found in production. And then others are automating the work of developers by automating the approach that they take to the integration lifecycle. And that's what I'd like to talk to the audience about today. All right, so like how you talked about it in terms of prioritization, because that's one thing I think that businesses can struggle with in terms of making automation and eventually hyper automation successful is where do we start? Let's talk, though, about this application sprawl that every organization pretty much is living in. We saw this massive adoption of SaaS applications in 2020, which a lot of businesses were dependent on to even facilitate just collaboration. But talk to us about the relationship between integration, automation, applications. Another great question. I spend most of my day thinking about integration. But I also know that most of my clients and probably the audience here thinks about automation first and then thinks about integration as a means, not the ends. The ultimate goal is digital transformation, i.e. delivering new apps faster with higher quality. If that's the case and you think about what's an application today versus what was an application 20 years ago? So today, there's definitely some business logic and code that you're writing. But the majority is actually integration logic. So you have to connect to a SaaS service like Workday to get data, connect to an app that's running on AWS, get other data that's running on IBM Cloud, to transform it, put it into a different database that's running on Azure. So there's a little bit of application logic and a ton of integration logic. So if you're a line of business owner that controls 50% or more of IT budgets or you're a CIO that's beholden to that line of business and you want applications faster than ever before and you don't want to sacrifice quality, how are you going to do that? Well, the way you do that is by focusing on the integration tier because applications are really driven by integration today. So if you want faster applications with higher quality, you really need to think about delivering integrations faster with higher quality. And integration is absolutely critical. As we look at the hybrid cloud, the advance of AI organizations that are in this multi-hybrid cloud world, what are some of the challenges that they face with respect to integrating those applications? So to your point, they can pull down data from Workday, align it with data in AWS for example, to make business decisions in real time. One of the biggest challenges is manual effort. So we started the conversation thinking about automation and we're coming back to it because we believe that you have to automate your integrations and the way you do so is through AI. So you can of course use rules-based automations and that helps to some degree, but things get really interesting when you apply AI. And the automation is driven by real world data that's specific to your organization in a continuous feedback loop we like to call closed loop and that's continuously driving efficiency. So if you think about the integration lifecycle, you've got to create an integration, test it, socialize it, operate it, govern it. That's what we mean by automating integrations, that whole lifecycle. So for instance, if you can create an integration flow and do a field-made mapping based on AI best practices, you reduce manual effort, you reduce coding, you reduce the need for integration experts or if you're a business user and you're able to describe your intent and you have your integration software handle converting that intent into the integration that's required. So for instance, if you could say generate a lead score and route the leads based on location to your sales team, you know what you're trying to achieve, why not get the software to do that for you based on AI under the covers? Or if you're doing testing, how about letting the AI generate hundreds of new tests for your integrations that reflect real-world usage behavior at your specific company? And these tests are based on other APIs that are running at your company. So we take the operational data, we know which parts of the API are being exercised, we know what data is going through your system so things that are, for instance, personally identifiable shouldn't be used as test data. Or if you're operating your integrations and wouldn't it be great if your AI could uncover optimization in the integration flow such as adding in maybe buffering to a message queue so that it prevents you from overages on your Salesforce account. And having that happen without needing a human in front of a dashboard, i.e. the AI under the covers is doing this for you. So for AI to really drive that integration automation, you need the operational data from your specific company and using that in a closing fashion so you're continuously improving not just your current integrations but your future integration. I can only imagine how much more important this has become in the last year as businesses in every industry were pivoting multiple times to survive and then ultimately thrive. When I think of integrations, I think of customers that I've spoken to who, and you gave the right example with respect to sales. They've got a CRM, they've got an ERP and they're not in sync, they're not integrated so that I can't, there's no one system of record. I can only imagine how much more important having that system of record has been in the last year for supply chains, even for demanding consumers, going, can I get some toilet paper? And if so, where can I find it? Absolutely. And this is where that notion of a closed loop approach to integration and the automation via AI comes in, right? So we strongly feel that today, this is the time that clients need to rethink their integration strategy. And we do agree with some of the other analysts and vendors that are talking about automated integration work and that's part of what we discussed earlier. And that's definitely necessary. But it's not sufficient, right? We'd say, oh. Go ahead, sorry. Sorry. Well, yeah, so our feeling here is that you also have to be thinking about evolving those integrations in a closed loop fashion so you're continuously making those integrations better with AI that's powered by your operational data that's specific to your company. And then finally, that the old approach that integration vendors used to have in terms of this style of integration fits all problems is the wrong approach. And instead, what we start seeing today is that customers are using multiple forms of integration to solve a specific business problem. So they're using Kafka, APIs, messaging, iPads. So from an IBM standpoint, we feel that every integration must be automated, closed loop and multi-style, with AI that's informed by your company's specific data to continuously improve so that you end up getting integrations faster, but they're also better. When companies have that spectrum of different integration processes, as you just mentioned, one of the things that I kind of think is, as we look forward, and you mentioned this a minute ago, wanting to have the foundation so that not only are applications integrated today and communicating well and sharing data, but in the future. So talk to me about this closed loop system that you mentioned, and how does that enable an organization to establish that now, but be able to take on applications that are not even created yet? That's really a foundational aspect that clients need to be thinking about, right? Because the closed loop nature of thinking of your integrations means that you're always looking at operational data and using that operational data and feeding it into your AI to improve your business processes, your integrations today, but also the ones that you're gonna be delivering in the future, right? So I'm sure your listeners are sitting here thinking, where should I get started? And frankly, for me, I turn around and say, you probably should ask your integration vendor of choice how effectively their solutions can provide an automated closed loop and multi-style approach to integration. And if the answer that they give you isn't very detailed, but I hope you'll ask IBM. And when you ask us this question, what you're gonna hear about is IBM's cloud-packed integration, which is our complete platform for automated closed loop and multi-style integrations. It's optimized for deployment across clouds with Red Hat OpenShift. And with IBM, you'll be able to use natural language-powered integration flows, AI-powered flow and field mapping, RPA connectivity, things that really take the manual effort of integration out and replace it with AI-driven automation. Second, you wanna think about the data that's feeding the AI, right? So this is where the operational closed loop aspect comes into play. Sometimes the other vendors in the space are taking operation data from hundreds of customers and putting it together and coming out with the average and using that to train the AI. We don't think that's the right approach because your most important integration processes are shared by no other customer, right? So you want your operational data to feed the AI that's providing things like field mapping, flow creation, creating the API tests automatically, or that's uncovering the inefficiencies that are running in your production environment. And then finally, what IBM will tell you is we've got the broadest set of integration capabilities, of multi-style integration capabilities, all delivered with a common UI and shared reuse and governance with unified management across clouds. And that's exactly what clients need because if you think about where are you deploying applications today, the components are running on multiple clouds, so you have to integrate across clouds. And then finally, what you hear from us is that IBM provides a proven hybrid and DMZ-ready security gateway that's never been hacked in 15 years, over 30,000 TPS per second, but the performance and security that frankly clients need for their applications today. So automated, closed-loop multi-style, you hear me repeat those over and over because we feel that's absolutely necessary for listeners when they think about their next generation applications and the integrations that we required for that. Excellent, well, Savya, I wish we had more time, but thank you for sharing what's going on with automating integrations, AI, what hyper-automation means, kind of where it is now. We look forward to hearing more about this and I'm sure the guests will be excited to see what comes at IBM Think. We thank you for your time. Thank you very much. For Savya Rodriguez, I'm Lisa Martin. You're watching theCUBE's coverage of IBM Think 2021.