Oops! Something went wrong while submitting the form.
In this Ecosystem Aces Podcast episode, Chip Rodgers, Chief Partner Officer WorkSpan is joined by Nick Otto, Head of Strategic Partnerships, IBM.
Nick Otto is a seasoned professional with 22+ years of experience, including over 15 years at IBM. He currently serves as the Head of Global Strategic Partnerships, overseeing vital relationships within the IBM Ecosystem, including $1B+ partners.
Nick's expertise lies in aligning corporate strategies with technology transformations. His broad background spans product development, ecosystem management, finance, sales, talent, and operations. Previously, he was a Partner in IBM Consulting, specializing in Data & AI, and played key roles in IBM's Corporate Strategy and Chief Analytics Office, focusing on improving sales execution.
Topics covered include:
IBM's ecosystem strategy and partnerships - 0:55
IBM's AI strategy and partnerships - 5:50
AI models, governance, and partnerships - 9:06
Partnering with IBM for generative AI solutions - 13:08
Hybrid cloud strategy, partnerships, and innovation - 23:16
Chip Rodgers 00:07
Hey, welcome. Welcome back everyone to another episode of ecosystem aces. I'm so excited to be talking to Nick Otto, today, Nick, welcome.
Nick Otto 00:17
Thanks, Chip. Glad to be here.
Chip Rodgers 00:19
Nick, I appreciate that you're taking the time here and a quarter. It's a busy time. Nick is General Manager and head of strategic partners for IBM running some of the biggest partnerships that IBM has, I'm sure with AWS, Microsoft, Google, Samsung, Cisco, SAP, Salesforce. That's quite a portfolio. Keeps you busy. I'm sure.
Nick Otto 00:55
It certainly does.
IBM's ecosystem strategy and partnerships
Chip Rodgers 00:59
Well, that's great. Nick, thank you for joining us. And let's just dive right in. Maybe we'll start there. Tell me a little bit about what your role is? And what you and your team are up to at IBM these days?
Nick Otto 01:19
Sounds like a good place to start. So as you shared, I lead strategic partnerships for the IBM ecosystem. I've been at IBM for about 20 years, electrical engineer by training. I joke often now that I still use my engineering, but I engineer different things. I work a lot with our partnerships with our clients to solve problems. But across my 20 years, I've been in consulting, technology, corporate strategy, and joined the ecosystem mission just a few years back.
So really energized around, as you said, kind of ecosystem strategy, where we are today, where we're heading and, and really energized around the discussion that we're having today.
Chip Rodgers 02:01
So that's terrific. And I think it's really interesting that, IBM is has made, I think under Arvind’s leadership over the last couple of years, He made a big splash a couple of years ago, saying, Hey, we're investing, I think it was was a billion and a half dollars into, building more relationships with partners and everything.
And then, you had the announcement, the partner plus announcement, and it's just been phenomenally successful. Bringing in new partners, I think I saw a number of about 3000 new partners over recent history. And so, tell me about how that affects partners? And How's it going?
Nick Otto 02:56
Now, that sounds perfect, I guess you're kind of to that point. Obviously, we've been very clear about the importance of the ecosystem in our model, the investments we're making in that space, the prioritization we're giving to that part of our go to market motion. So maybe I'll start with kind of the broader ecosystem where that fits into our strategy. And then I can zoom into, as you said, the strategic partners in kind of how they fit in as well. I think we've been clear as a whole, when we go to the market and talk about our strategy. There's two fundamental pieces to the IBM strategy today, hybrid cloud and AI.
I think, obviously, the AI piece, as we talked a few minutes ago, is becoming a bigger and bigger part of that narrative this year. The good news is we've been consistent with that strategy for several years now. But I think the only way you can help clients deliver on those two fronts, hybrid cloud and AI, is the realization that no company in the global market today can succeed alone. I think together with our partners, we really have an opportunity to bring solutions to market faster, achieve collective success for IBM, our partners, and most importantly, our clients.
And I think, exactly as you mentioned, today, the role of partnership for us is more important than ever, particularly helping clients move faster, which is why we've really been investing and accelerating our ecosystem motions. You mentioned the launch of partner plus back in January. This was a huge change for us. It was really a simplification of consolidation. And to lean into the ecosystem even further. When we launched a partner plus, we actually pulled strategic partners into that same ecosystem.
So you'll sometimes hear the terms interchangeably now, we introduced the IBM Blue partners, which are the strategic partners for the IBM ecosystem team. It's a really select group of leading cloud service providers, as you mentioned earlier, infrastructure partner software vendors that we partner with Across all of IBM, on the technology side, the consulting side, in many cases research really across the board.
I think those seven names you mentioned earlier, what's really exciting is, they're some of the most recognizable tech players in the world, especially when combined with IBM, you're talking about a trillion dollars plus in global technology purchases, hundreds of millions of developers, brand value, market, pervasiveness, all those different pieces.
And I think what's really exciting to me, is not just having relationships with these partners, but the joint selling we do together, the engineering, we do together the architecture work that we do together, really to solve problems for our clients, the complex needs that continue to get more complex, with the core strategy, still helping our clients transform around hybrid cloud and AI.
IBM's AI strategy and partnerships
Chip Rodgers 05:50
I love that, Nick. I think even that sort of approach. And that attitude of, as big and successful and powerful as IBM is, as an organization, it's like, you still can't like you need partners. There's things embedded everywhere. So it's really important to stay connected and have them working with you to build out the whole ecosystem and deliver value to customers.
Nick Otto 06:20
Chip Rodgers 06:23
Fantastic. So I love to dive into the topic of AI. IBM has been on the forefront with Watson for many years. And then I know, you just recently announced Watson x. So let's talk a little bit about that, and how that affects partners.
Nick Otto 06:49
Absolutely. I think not just the topic of the day, the topic of the month, the topic of the year, but probably the topic of the decade, seems to continue to be AI and generative AI. And I think we've all seen the stats over the last nine months around the importance of generative AI. A recent study that we did, our Institute for Business Value, showed that 75% of CEOs surveyed believed that competitive advantages will come from those with the most adoption of generative AI.
And I think, exactly as you said, we've really been a champion around AI for a very long time now. And our focus has always been around open innovation, pulling the community together, which just really reinforces why partnerships make so much sense here. I think we have a lot of history from an AI leadership perspective, partners get access to our capabilities, partners have their own capabilities. I think what's exciting to me is kind of the world evolving towards. So we talked about hybrid clouds, we've been on that for quite some time.
But I think I see on a regular basis. Now this hybrid AI type discussion and open session around, everyone wants to use technology to solve really big problems. And I think we've been very clear that our belief is an open and hybrid approach is really fundamental to that success. So different types of models, different types of compute different methodologies. What's exciting to me, and what we've done with Watson.
Next is how does all of this come together to drive insight and add value with all of the capabilities that are out there, of course, using our core platform, which is built off of three main components, we have .ai, which is our framework, for driving Insights, where we've got a tuning studio, we've got some of our LLM 's and different pieces, we've got dot data, which allows us kind of our data lake model that allows you to reach into different data sources. And then we have dot governance, which for us is critical. We want to make sure we have a true representation of the trust and the ethics behind all the AI and dot governance is our approach to do that.
AI models, governance, and partnerships
Chip Rodgers 09:06
That's a hot topic. It's interesting, the whole concept of Gen AI, listen to a lot of podcasts about the topic from folks that really know AI deeply and it's just a whole different way of,ordinarily, traditionally, when you're developing some kind of code, you write something and you're telling the computer to do something, you're to end and in AI, it's like, that's not really happening. It's like you sort of throw in a bunch of data and you're not exactly sure how it's gonna turn out. So governance, I think, is just a really critical topic.
Nick Otto 09:51
Completely agree. We've all heard the terms hallucinations and all the fun new AI terms that have come out. Fundamental to us is, no matter what the outcomes are and what the outputs are, we want to be able to understand them and learn from them. And that's kind of fundamental to our approach.
Chip Rodgers 10:10
So how can partners participate and take advantage and plug in to those, to the exciting things that you guys are doing?
Nick Otto 10:25
Yeah, I mean, there's so many ways. And I think, kind of starting at the higher level. For us, everyone is building different types of models, taking different types of approaches, looking at the efficiency and the efficacy of all these different models. For us, kind of back to the point I was on earlier, I think our main goal is to really ease their enterprise AI burdens that come with all of these different approaches from all different directions. How do we help customers easily develop, tune, deploy, enterprise ready and trustworthy AI models at scale.
And back to the point like, we don't view there's one model that rules all there's lots of great insights that are being created from so many different players out in the market. So this multi model approach is really important to us. I think that the large models have shown that they're tremendously interesting and valuable in certain instances.
However, what we've also seen from the enterprise is sometimes LLM can be too large, whether that's getting a quick ROI, because obviously, the larger the model gets, the more expensive the insights become. But I think more importantly, it's the targeted nature of some of these models, back to the hallucination point, like when you ask a question to a very large model, and you can't understand the response that comes back, it's because you have this tremendous amount of data, and trying to sort through all of that data to come back with a very targeted answer can be challenging.
So a lot of the we've taken is very much focused on a multi model approach, how can we create targeted models to balance with those larger models that are out there to really translate all of the excitement from the big models in the market with some of the more targeted prioritized models for enterprise to drive faster business value.
So we've built some of our own models, designed for real world use cases like code, modernization, digital labor, we've announced a lot of those things, already a lot more models that we're off building around these targeted use cases. And back to the point we were just on the foundation of our models and our whole approach is really trustworthy AI, we need to understand the data, we have the insights that were deriving. And I think what's exciting, is we're also incredibly open to work with our partners who have their own models, and they're using other Gen AI platforms and other Gen AI models in general, how does all of that come together to provide next level of value faster, easier, in a more productive way, is really our strategy, which hits on partners kind of through and through.
Partnering with IBM for generative AI solutions
Chip Rodgers 13:08
That's terrific. It's such a fast growing and, and changing. I mean, it's just like, every week, there's something new coming out. So it's hard to even sort of get your arms around what's actually happening. So it's, it's terrific that IBM is helping customers kind of get their arms around it, especially at the enterprise level, because it's one thing to sort of play with Chat GPT, but it's another thing to actually build it into enterprise grade solutions for customers.
Nick Otto 13:50
No, I think what's been really fascinating is you meet with a board member, as CIO, they're all they've all been told, I need to do something with Gen AI, and I'm going to do something with Gen AI. And then you start to kind of decompose that. It's like, well, what's the value statement? What are you trying to accomplish? What's the business value you're looking to drive, which is where you quickly get to exactly where you were, it's like, we need to take a lot of different capabilities, a lot of different models to really hone that into business value for you.
And I think that's kind of an interesting learning experience with clients that I've worked with already. Many times this, I need to do genuinely because someone told me to, people aren't quite easy around the business value that goes with that. So we're there to help them.
Chip Rodgers 14:36
That's awesome. So let's talk a little bit about again, bringing it back to the partner participation. I think I know you guys are working with partners on doing some joint joint development Co- innovation. Talk a little bit about that. Maybe some of the use cases or industries where you're seeing, working with partners and bringing some interesting capabilities to customers.
Nick Otto 15:16
Absolutely. I think, as we've been discussing here, the ecosystem is so essential like that back to our desire, our strategy of investing more and more in the ecosystem. I think that this generative AI topic has just supercharged that. And you've seen that announcements we've made already this year, as you said, hopefully, right here at quarter close, some real new exciting announcements coming in as well, with some of our collaborations, but let me give you a couple of examples.
I think SAP is one that we announced, earlier this year back at IBM think, working with SAP to embed Watson x to power their digital assistant SAP start, which is a capability that allows users to search across the SAP landscape using generative AI capabilities. So you can get insights back from your SAP a state S for HANA across the SAP estate. That's a perfect example of amazing data sources, amazing enterprise insight that comes from a platform like SAP, and then our capabilities coming into work with their capabilities to really help accelerate that transformation.
I think, when I look across my portfolio, Salesforce is another great example, working closely with Salesforce to allow users to take open source models, train and tune those models using Watson x.ai. And then bringing those back into Salesforce as a part of the Bring Your Own model approach. And I think that's what's been really interesting.
As you mentioned, these constant evolutions in the really quick moving market that we're in, everyone is realizing there's so many different value sources, and how do those different value sources come together to really accelerate the transformation has been key, I think, a couple other examples, working closely with other blue partners or strategic partners in my portfolio, Adobe and Samsung, working closely with both of them around, what is the future of AI look like? Also working with a lot of the top consultancies and systems integrators.
So we recently, with Wipro EY, NTT, DATA, created centers of excellence to give clients even greater access to Generative AI skills, expertise and capabilities around Watson next. And we on our own side, I think what's always interesting from an IBM perspective is having our technology arm as well as our consulting arm. Within the consulting arm, we've created generative AI consulting practices with targeted practices around Adobe, Salesforce, Microsoft, and a lot more account. So it's kind of to your point. It's coming from all directions, and I think coming to life in all different parts of our market and all different parts of our business, which is really exciting.
Chip Rodgers 18:06
It's terrific. We actually have a question from Blake Williams, who was watching and he says, Any surprises or lessons learned as you're working with partners and on Gen AI?
Nick Otto 18:23
Yeah, there's a long list of surprises. It typically goes back to your point, just how fast this market is moving. I think, the early dialogue that I would have with clients, on this topic, the surprising point was, how do you build bigger and bigger models to solve bigger and bigger problems? I think the biggest lesson learned that I've had to date and will, of course, keep learning as we continue through this evolution is the bigger the model doesn't necessarily mean the bigger the insight, I'd much rather the experience, I've had to date working on this with clients.
I have a combination of models with a lot of different types of insights across different parts of my business, different subject matters, different areas of expertise, and pull those together to drive a lot more business value and a much more efficient, productive way. While you know, of course, not forgetting about the value that comes from these really large models, but how do you take those insights and combine it with smaller has been probably one of the biggest lessons learned and a lot of the partners that I've worked with in this space, many of the ones in my portfolio are building bigger and bigger models, which is great, but one of the biggest challenges that they're faced with is the bigger the model becomes, the harder it is to influence when you reach the enterprise.
So you almost imagine like having this massive model that has all kinds of great insights, across the world of information that's out there, but then you have this one small pocket of really important enterprise information that you want to influence that bigger model, that's incredibly hard to take this small, data of sand that has influence that you think is really important to influence the bigger model. So I think that's probably one of the biggest lessons learned that I've had thus far, is just keeping, constant evolution and constant learning around how do you take all the value from all the different data sources, company's own models, really large models, industry specific models? How does all of that come together to maximize value for the customer is a huge focus for us?
Chip Rodgers 20:40
Interesting. Well, I love some of the examples that you've talked through, with SAP, with Salesforce, with Adobe, where, you know, certainly, SAP, Salesforce, we know, they're massive amounts of data that they've all accumulated, but it's not generally available. It's not out in the public domain. So how do you take that data for a company, and then apply that to force it to take that power, and then give it to customers to be able to get insights from it and figure out what to do?
Nick Otto 21:24
Exactly. And it's funny, because this is a point that we've been on for probably nearly a decade, the value, so much of a company's value is in their own data, and making sure that they can derive insights from their own data is still right at the core back to the whole trustworthy AI point we were on earlier, I think there's a tremendous amount of value that can come from generic data, getting insights from publicly and for public information that's out there.
But how does that then come together with the incredibly valuable data that an individual company has and individual business units within that company have? How do we make sure we protect that data, protect those insights, and then combine it with this broader data set is still right at the core of so many of the discussions that we're having today.
And when you look at the big ISVs, SAP in particular, they do to your point, companies running on SAP have a tremendous amount of data there that they absolutely believe they have differentiation around, they're not going to go share that with the rest of the world, take SAP data and say, Hey, everybody, please learn from this. But how do you take that open, collaborative, general data, combine that with your industry and enterprise specific data to derive better insights, while protecting your data is absolutely where all of our discussions are right now.
Hybrid cloud strategy, partnerships, and innovation
Chip Rodgers 22:51
The other topic that you Nick brought up towards the beginning, one of your top priorities is hybrid cloud. Talk a little bit about what that means for IBM and, and how are you seeing, what are you hearing from customers in terms of their needs? And how high IBM and partners are able to deliver?
Nick Otto 23:16
That's been an exciting transformation. I think when I first started to go talk to clients, five, six years ago around a hybrid cloud strategy, what's your hybrid cloud strategy? There were a lot of people saying, Well, what exactly does that mean? Now, what's exciting is, you look at the marketing out there, you look at every one of our strategic partners, they're all talking about hybrid cloud.
So it's been fun to watch that maturation, of really learning and thinking. But what's exciting for me right now, from a hybrid cloud perspective, is I think, every big company has realized that hybrid cloud is no longer kind of a stop along the way, it's absolutely a destination from an infrastructure perspective. I think that we've learned that there's particular types of compute that make sense in different places, when you kind of think about big industry problems.
When you have latency concerns, you're thinking about edge, you're thinking about efficiency, you're thinking about existing data centers, you're thinking about incredible compute capabilities like IBM z, how does all of this come together into this heterogeneous environment, where I'm putting, compute where it belongs, and not just I'm going to shift everything here because we have a mandate to do so. I'm going to keep compute where it belongs from a financial perspective, from a performance perspective. And really, at the end of the day, from a value perspective, that's what's been really exciting. And I think to kind of supercharge that what I've really enjoyed from a partnership perspective, is being able to launch things like ARO Azure, Red Hat OpenShift like Rosa Red Hat OpenShift on AWS to see the cloud providers also coming in saying this hybrid cloud thing is really important.
How can we be a part of the hybrid cloud mission and allow workloads to run where they belong and kind of be an optimal place for that has been a really exciting transformation. And I think it's funny, because so many of those narratives fit right to the AI discussion we were just having. It's like, there's so many different places to have your compute. There's so much different value that comes from different types of data. How do you pull that together into this hybrid AI or hybrid cloud environment to maximize efficiency and success and value for your customers? We're seeing a lot of conversions across those spaces.
Chip Rodgers 25:40
Really interesting. So many use cases?
Nick Otto 25:43
Yeah, that's for sure.
Chip Rodgers 25:47
Terrific, Nick, this has been fantastic. Thank you so much for sharing so much information about where IBM is and how you're working with partners around these, like critical topics these days. Love to hear, maybe you have some thoughts, we have a lot of partner folks that are our listeners, anything that you've picked up along the way, working with partners that has been really helpful in your success, that you'd want to share or convey to other other partner folks?
Nick Otto 26:28
I guess I'll do the typical, here's three thoughts type responses. I think, first of all, is always putting the customer first. I think a lot of times, I've kind of come into partnership discussions where it's all around the two partners talking to each other, and forgetting about that customer. So reground in around the customer, what's the value we're trying to bring to that end user?
And how we can come together to solve that is really important. I think, too. And this is back to the very first point we were on, I think continuing to remember, for your own company and all of your partners. The fact that no one today is going to be successful by themselves. And I think that's just learning. And I think it's really accelerated in the last five years, that there used to be a lot of companies out there that would say, we will take on all of this for you. Let us solve this, and we'll hire people to pick up skills. I think we are very much in a world today where that no longer works.
There's amazing capabilities from a technology perspective, from a services perspective, how do you take those capabilities and derive value the fastest, it's by taking those different pieces, and coming together to solve a problem? And I think third, it is really this notion that we've hit on multiple times during this is to continually innovate and continually adapt, and adjust approaches when the market changes. And I think that we at our company do that often, because it's a fast moving space. And I think one of the challenges I've seen when you kind of look at particular relationships, particular opportunities, is there's always someone that I'm sure you and I have both seen, it's like, no, we don't do things that way. I think what we're learning here is we're constantly reinventing, and we need to always be thinking about what that next change looks like, to make sure we can, move as fast as our customers and as fast as the market is moving.
Chip Rodgers 28:40
Sure, it is moving fast. That's for sure.
Nick Otto 28:43
100% It’s a fast market faster than I've ever seen in my time.
Chip Rodgers 28:49
I love those. So customer first. No one can do it alone. And, and and always focus on innovation and, and, and creating new capabilities for customers. Well, those are good ones.
Nick Otto 29:06
You got it. I wish I was better prepared for that. But that's the quick.
Chip Rodgers 29:12
You're good. Nick, thank you so much for taking the time today. Really appreciate you sharing so much. I feel like we could keep going here for a while. But I know you got a quarter day to get back to. And I appreciate you taking the time out and sharing with us.
Nick Otto 29:35
No, thank you Chip. Really enjoyed it. And I look forward to following up.
Chip Rodgers 29:39
Awesome. So thank you all for joining and for Nick Otto. I'm Chip Rodgers, Chief Partner Officer at WorkSpan, thanks all for joining us and we will see you next time. Thanks, Nick. Thank you!
To contact the host, Chip Rodgers, with topic ideas, suggest a guest, or join the conversation about modern partnering, he can be reached on Twitter, LinkedIn, or send Chip an email at: email@example.com