Partnerships Unraveled
The weekly podcast where we unravel the mysteries of partnerships and channel to help you become more successful.
Partnerships Unraveled
Sergio Farache - AI, Efficiency & Scale: Partner Enablement at the Next Level
In this episode of Partnerships Unraveled, we sit down with Sergio Farache, Chief Strategy and Technology Officer at TD SYNNEX, to explore how one of the industry’s largest distributors is navigating global complexity, AI disruption, and the evolving demands of partner enablement.
Sergio shares his journey from founding a tech business as a teenager in Venezuela to shaping multi-billion-dollar strategies across global markets, offering lessons in resilience, strategic alignment, and the future of tech partnerships.
We dig into:
- How to balance global strategy with local flexibility and why bottom-up input is critical
- Why operational efficiency is the foundation for innovation and investment
- What partners need to succeed in the AI economy, from infrastructure to embedded use cases
- The role of industry-specific knowledge and consultancy skills in next-gen partner models
If you’re thinking about where channel strategy is heading and how to stay ahead, this conversation is one not to miss.
Connect with Sergio: https://www.linkedin.com/in/sergiofarache/
_________________________
Learn more about Channext 👇
https://channext.com/
Watch on YouTube ►
https://www.youtube.com/@channext
#channelmarketing #channelpartners
Welcome back to Partnerships Unraveled, the podcast where we unravel the mystery about partnerships, and channel on a weekly basis. My name is Alex Whitford, I'm the VP of Revenue here at Channext and this week I'm very excited to welcome our special guest Sergio. How are you doing?
Speaker 2:Hey Alex, how are you Good to have this conversation today.
Speaker 1:Yeah, I'm excited to have you on. I really enjoyed our preparation call when we sort of discussed some of the topics that we're going to be touching on today. Maybe for the uninitiated, you could give us an introduction about who you are and what you do.
Speaker 2:Yeah, happy. I am Sergio Faraci. I'm currently the Chief Strategy and Technology Officer of TDCnex. I'm currently the Chief Strategy and Technology Officer of TDCnex. As a brief introduction, I grew up in Latin America, venezuela specifically, and always in the technology sector. I am a system engineer by profession. I was an entrepreneur a big part of my life and then moved to the corporate world world, and I have the privilege to be part of the corporate world in America for several years when my company was acquired by Avnet and then from Avnet we transitioned. From Avnet Technologies was acquired by TechData, then TechData went independent with Apollo and then we merged with Cinex and now we are TD Cinex. Then we did several transitions and learned a lot about that process, and I am very, very engaged with the technology segment for many years. Then I have always been with my family. We live in Miami and I have a daughter and a son and I love road bike. Then those are a few things about me.
Speaker 1:So you sort of worked for the same company and for different companies for the last 17 years, but it's all been one sort of cohesive journey.
Speaker 2:Yeah, it's a funny story. I have my formal recollection is more than 20 years in the same company but has been very different companies, Then it's a good way to variate in the same house.
Speaker 1:Yeah, I like it, almost like you're redecorating. So you built your own company at 17. And now, after you sort of meander your way through your career, you're responsible for strategy for a business that does billions in revenue. I'd love to understand what are some of the lessons that you took from that early stage business that you built that has shaped how you sort of run this global strategy today yeah, I think that have the opportunity to be an entrepreneur initially give you the ability to understand many things together.
Speaker 2:I think that one of the relevant success elements of a company, of course, is have people that is significantly specialized and deep in things. Then we love the experts. They are a critical piece of our success, for sure. But sometimes, where you have that trajectory, you don't necessarily see the whole picture. And being an entrepreneur owning companies and selling and evolving those companies give me the ability to really get involved in everything you know, from the financial side to the technology side to the service side Then that gives you the ability to understand the whole, all the topics, and sometimes don't look you know one thing, but understand the complete spectrum of the things that are needed to execute and deliver something.
Speaker 2:The other element that as an entrepreneur, you have is normally is a scarcity. You don't have too many resources. You need to do everything by yourself, with others, you need to find ways to execute things with a lot of obstacles and I think that that is another great factor that helped me across the career is understand how you deal with the scarcity, with few resources, how you optimize the execution of a process and again, if you ask to any successful entrepreneur, he will tell you that where he end and where he start is very different than when he start. Think that he will end, and that is because you fail and learn to fail, and that process is another. Fail and learn and learn to fail and that process is another thing that I think that contribute from that entrepreneurial expertise to understand that in many cases it's not so much be rigid in the perspective of what you want to execute, but understand what is the outcome that you want to achieve and be flexible in the ability to deal with the variables that you need to complete that outcome, independent of how you achieve that outcome.
Speaker 2:And usually in some interviews and talking when I do coaching to some of the teams or the internships, et cetera, people ask me what do you think that creates a real benefit in terms of being successful? And I said that of course there are multiple variables education, intelligence and other things but in reality what I think that makes people successful is perseverance, because at the end, not many people are so smart, not many people have all the solutions, not many people have the answer the first time, but if you perseverance and you intent and go again and go again until you get it. You're normally getting the result and be successful, and those are some of the things that I learned through that process.
Speaker 1:Yeah, sergio, I love that. I talk a lot on this podcast about the importance of mentorship and finding mentors to help guide your career, and you bring me back to a point which is a mentor of mine told me perseverance beats IQ, because your IQ is how well are you able to predict upcoming events. But perseverance is hey, let's go find new data and then get new data and then get new. Let's go find new data and then get new data and then get new data, and get new data and get new data. And if you're willing to put up with that whole process it doesn't matter how good you are at looking around the corner the guy down the road, if he's already around the corner because he's gone and got all of that data over the long enough time horizon you're going to win. I just think so often people and teams and businesses are impatient to to sort of jump to that next phase. But, like you say, if you can just keep battling and keep going, eventually you have a perfect data set, and perfect data set means it's really easy to make great decisions. Yeah, awesome.
Speaker 1:Um, one of the things, one of the topics that we talk a lot about on this podcast, is that localization versus globalization? Um, where and how you find that balance? Because I think that's a really complicated thing. In a perfect world, we build a global strategy and we just be like this is going to work in every region, in every language, with every type of culture and with every tax complication. Unfortunately, that's never the way it actually works. Talk to me how you balance that global presence and operational strategy but give enough flexibility to the regions to adapt.
Speaker 2:Yeah, that is a good question and a complex question to answer. I would say that it's as complex as probably the typical thing about work and life balance. You know how you find that middle ground between those things. I think that the first thing that I would tell you in my personal life and experience with this is this is a marathon, it's not a sprint. You know you won't find a way to do it quickly. You need to do it as part of a process and working together as a team. As part of a process and working together as a team. And I think that one of the things that I quickly learned when I get the corporate strategy role is that you cannot do a strategy in a vacuum. You need to definitely create a process and a collaboration that go bottom up to be able to understand the needs what are the areas of interest, what are the willingness of the team and, of course, intercept that with all the strategic data, trends, models, et cetera that drive the success, but construct that in a process that go bottom up and then, once you have that well-structured, then you can re-architect and go top-bottom from a perspective of more clarity, more definition, more construction.
Speaker 2:I think that the critical factor for success is you need to have a great respect for the value that that, the localization that the regions, that the countries bring to the table because they leave different realities.
Speaker 2:You cannot assume that that you have a tissue, that that you can apply a standard everywhere. Then, instead of thinking and strategy that tell the people exactly what to do, the way that we interpret this is you need to create a framework. You need to create a set of principles instead of prescript actions. And if you define an agreed framework and we have clarity on what are the areas of interest and we have clarity on what are the areas of interest, the priorities that rule by the must-haves in some components and leave the regions and countries the ability to customize and operationalize those components in their reality and adapt to what they live and what they need and adapt to what they live and what they need, then you got a cohesive model that can be deployed and applied, but not instruction layer that probably will be not adopted and have very few possibility to be successful.
Speaker 1:Yeah, you want to teach them how to think, not what to think, right?
Speaker 2:Fundamentally yeah, and more than I would say teach them sounds a little you know too hard because probably you can learn a lot from them. It's integrating the process right and basically ensure that as a corporation you have those most to have definitions, you have a common framework, because what is real is that you cannot let the organization to move everybody in a different way because you won't achieve to any place. Then as an organization, you need a direction, you need a framework, you need a set of priorities and you have a common language. Once you have that, how that is customized, what are the details behind it? If the people like blue versus red or they want to go in the morning versus in the night, those elements are not so relevant if everybody is using those principles, that framework in that direction, in the execution.
Speaker 1:I love it. One of the things that we spoke about which the execution I love it. One of the things that we spoke about which, to be honest, I was quite surprised to hear someone call it out in quite this way was the importance of operational efficiency, but why that's so important for growth, and I thought you had a really interesting way in terms of the fiscal importance of operational efficiency and how that allows you to do investment very, very significantly. Sergio, talk me through why businesses should be evaluating the operational efficiency to more granularity.
Speaker 2:Yeah, I would say that well, now we are in a complete redefining of operational efficiency with the introduction of AI and I think that, without a doubt, one of the key factors for a company of our scale and size remember, tdcnx is one of the largest distributors in the world. It's basically a multi-billion dollar operation. The amount of transactions, products that we sell and manage by day is incredible across the world then the scale is critical and you cannot achieve a reasonable scale without operational efficiency. Then operational efficiency is a fundamental that need to rule basically the way that you go to market. But in the way that you translate that again is in the same principles, you need to have clarity in what are your priorities. You need to have clarity in benchmarking, meaning how you compare your processes versus industry. You compare your processes versus industry. And now, specifically, you need to have clarity in how you really re-architect and re-engineer processes with the use of technology. And I think that we live several stages of relational efficiency from the, you know, normal manual process to then the technology application to to then now the internet and now the appearance of AI and agentic AI, where you now have co-workers that will be digital, completely digital, taking decisions and accelerating elements, and I think that the big paradigm shift that now we need to recognize is that in the past you used technology to automate process and now you need to rethink the process in the context of technology and that is a big shift in how you address those things. To be more practical, what we do as an example in terms of how we bring that operational efficiency to our partner and our ecosystem, you can take the case of our platform, stringone.
Speaker 2:When we released StringOne, that is, our cloud commerce platform, we clearly understood that the challenge that the market bring the hyperscalers, new vendors, with the specification of applications, with the introduction of infrastructure as a service, with the consumption models and all that require a total different level of detail, management and complexity. You know, in the past you sell a software, you visit the guys in the software and two years later you appear to say how the renewal is, etc. Now we are talking about success, execution. You you're talking about outcomes, you talk about life cycle management. Then you need to clearly automate the processes and efficiency of those processes in a digital way. Then many companies do that by themselves, but we don't operate direct to end users. We do partners and partners go to end users.
Speaker 2:Then in the way that we released, stream1 was a complete platform that enabled all the capabilities necessary for us to help the partners.
Speaker 2:But then the partners go to the end users and digitalize all the operational workflow and journey the user journey across all the network processes, billing, combination of financial models like subscription, consumption, etc. All that in a single architecture without the need to invest in those elements, because we are providing that operationalization and digitalization. Then that is an example of how you bring operational efficiency to the system, because now you are drastically enabling a scale to a network that without those tools they can not necessarily scale and connect and and and some derivates from that. As an example is, we now release a set of apis connectors etc to third-party systems like the PSA connectors, the API connectors, the ITSM connectors, in a way that now we are automating not only the digital journey but all the operationalization of that in services, in ledgers, accounting ledgers, in all the motions without manual intervention. Then all those elements contribute to operational efficiency. But again, now think in operational efficiency at the scale because that is basically what they provide to the market.
Speaker 1:Maybe to double click on the AI workflows you spoke about there sort of being three distinct layers when we talk about that AI opportunity Infrastructure, embedded AI and foundational AI models. Which of these, while all important, there is obviously an order of operations. Which of these, from your perspective, is the most important for partners to capitalize on first? And maybe I'd love to also understand how is TDCNX enabling that change?
Speaker 2:Yeah, I think that all depends on the maturity level of the partner in relationship to these areas. Right Meaning? I would say that one of the things that the AI is bringing is the democratization of technology, because, you know, ai is making simpler the access, the development of knowledge in terms of digitalization of component and what you want to achieve. I think that it's an easy and connect natural connection for the traditional hardware partners. To go to the infrastructure side, it's a significant change in the AI world around the processing capacity that is needed, either on-premise or off-premise in the cloud. It's a clear demand for a new networking structure Because, if you think now in the networking concepts, the networking is changing drastically because the interaction in networking that these inference models need to have is tremendous. And if you think in a future where you have multiple engines talking to each other, talking to databases, talking to people, then the requirements of network will variate and it's a great opportunity in the networking space. But if you're thinking all this this introduces new paradigms and connections and flows of data, then you now need to begin to talk about security for AI and that is a complete new space too, meaning we have obviously the traditional security, but now you need to rethink security, not only in the context of how you use AI to improve security, but how you secure AI, because secure the model, secure the access to interaction of data, secure how those agents will interact. Imagine now you are beginning to transition decision power from a person to an agent. Imagine that somebody hacked that agent, what he can do. Then again you need a new way to think about Then all this topic of infrastructure from the data processing to the storage, to the networking, to the security is a huge opportunity for partners and is very adjacent to what they do today how you apply AI to processes, how you build these agents, how you model or help the customers to produce that operational efficiency or to accelerate time to revenue from the perspective of the market.
Speaker 2:And that is a complete new generation of opportunity that go from capitalizing in what I call embedded AI. That is many of the products and applications that we sell and do today. They now are introducing AI and need to be translate in real use cases and scenarios and the partners have a great opportunity to activate on that. And then it's more the customized private models, now with a specific vertical knowledge or industry, that will drastically change processes and I will say starting for services, meaning everything that have a relationship with massive human interaction, is something that agents can do effectively. Hr is an example. You can really revolutionize the way that you answer questions, provide time to answer for many things in HR. You have sales, how you sell customers, how you answer, you have support tickets. All that is an opportunity that can be afforded.
Speaker 1:Well, I actually want to loop back to your previous answer, because we spoke about how do you have a globalization and a localization, maybe philosophy, and you spoke about embedding common language and frameworks and almost that ability of how do we have a similar thought structure for how we decide on how we execute decision making? That, for me, is a perfect use case, right? You maybe sharpened my language slightly in terms of we don't want to teach our people how to think, but we absolutely do want to teach AI how to think, how to think our philosophy as opposed to someone else's philosophy, think our philosophy as opposed to someone else's philosophy exactly, building agents to allow people to make decisions in line with the type of decisions that we would ourselves make if we were in that same situation. And I want to give a very, uh, very sort of example that I've built within my own organization. We have built an agentic workflow that analyzes each step within our sales process, um, and so it reviews and analyzes how did a salesperson perform in that meeting, and that meeting contextually understood what the objective of the meeting and how we do it.
Speaker 1:Um, and gives feedback. Um, to be honest, it saved me hundreds of hours because it means I don't have to listen to every single sales tool. That happens. But what we do have is this process where people go oh, I should have answered that objection like this because of this meeting where we handled that objection very well, and this is how it works and suddenly you get this conversion improvement, this, uh, not efficiency improvement, but effectiveness improvement is actually the one thing that I think most people radically uh, underestimate when it comes to ai, because I think they think how do we do what we do today but cheaper? And my argument is that is true, but it's what do we do today but better?
Speaker 2:yeah, and what? What we did? What we did in that direction to support the partners is that we relay, we release a program that is called Destination AI and in Destination AI we are now doing several things. We basically enforce all these capabilities that the partners can access and understand and we help them to evolve from where they are to where they want to be. And we release sub-programs, including, as an example, a matrix model where, based on your maturity model, you can see the type of vendors, products, et cetera, where you can participate, or sub-programs that will specifically give you capabilities in the infrastructure-specifically side, in the embedded side or in the modeling development side. Then, equally that we discussed previously the strategy and the framework, we create a complete framework and support model and enablement model and tooling to help partners to understand how they want to take advantage of this opportunity and how they will use all the resources that we put on the table for them to maximize that opportunity use all the resources that we put on the table for them to maximize that opportunity.
Speaker 1:Yeah, I think to me. It leaves me to be really excited about where I think the opportunities lie, because I think we are looking at operational efficiency being the bare minimum of the improvements, but also where I think a lot of that value. I'm sort of fascinated at a macroeconomic perspective. If I were to ask go-to-market leaders or entrepreneurs of big software companies, hey, where is the biggest skills gap that exists today? They'd say we don't have enough software engineers. We need 1,000 more software development people to help us code more effectively. I think if I were to speak to them today, they might give me a different answer as AI starts to drive the efficiency and effectiveness of those same software people to be able to scale their workflow much further. If I can ask you that question, for in two, three years' time, the world's going to radically shift based on AI. What are the skill gaps that you think people should be investing and protecting themselves against?
Speaker 2:that will occur in a couple of years time yeah, as you just said, right, uh, the the topic of of expertise and the areas of expertise are are changing in terms of requirement. You know, if, as you said 10 years ago, I will say, okay, you know, if you stood computer, probably you would be in a safe place, in a good place. Now I'm doubting about that, but that is real and not because if you study computer science and you are a researcher in AI, mark Zuckerberg can pay you $100 million, right, then it's always opportunity for everybody.
Speaker 2:But in reality now I think that if you study, if you plan to really evolve in, where AI will capture your full potential is in deep expertise in knowledge of industry segments or areas of activity. Meaning what the AI will bring is the ability to drastically improve, accelerate and optimize things. But, honestly, you need to have the ability to understand those things. You need to have the deep knowledge, and I don't think that a guy who learned to code lose anything today because he already know how to structure a process and a think model. Now, what these people need to really complement is how you understand pure science or how you understand a specific vertical industry and how you help the people to apply those models to processes and activities. Then I think that the biggest skill gaps that we will continue seeing is obviously deep expertise in the AI space, but in addition to that, this expertise in vertical industry and segments of markets and actionable knowledge the way that you transform knowledge in practical, actionable knowledge through the implementation of AI. A good example is that we did recently is we work with IBM and we launched a program that is called Watson 100. And what we did with that was to create a complete process where we elevate the skills of partners across multiple regions working in their understanding of the application of AI technology to specific use cases in companies like I have been describing to you in the HR segment, in the legal segment, in the sales segment, in the service segment, with use cases that they can bring to those companies to provide that level of automation and increase in the dealing, operation and efficiency. The program has been very successful. Partners that in the past wasn't involved completely in AI or don't have a deep understanding, they gained that and now they are actively working with customers in real life use cases and areas, Then I think that the opportunity is tremendous, but the scarcity will continue in terms of pure science and in terms of vertical industry.
Speaker 2:The other area that I still think that will be a scarcity is security. I think that security, continually independent of the race of AI, security, will be a place where still skills will continue to be needed.
Speaker 1:I completely agree. I'd add one extra bit of flavor that I can't believe I'm about to say. But we actually, I believe, strongly lack the consultancy skill. I think a lot of listeners are going to roll their eyes and say, alex, the last thing we need is more consultants.
Speaker 1:But I really think where AI is headed is such unique and customized deployment cycles, the ability for a third party to come in and ask true business questions not hey, what are you doing about communications? But really what's the thing that's keeping you up at night, that's most stressful about your business today, and then applying a technological answer to that fear or that concern, or that opportunity, that ability to understand the customer deeply and then turn that into a technical solution. I think that's where we see the market headed. I don't think that's going to be a person right. It's how do we get that computer science person to come together with that McKinsey-style consultant, to come together with that vertical specialist, and then those units work together in some sort of tiger team. But I think that's where we're going to see some of the most sort of creative and valuable deployments.
Speaker 2:I think the topic is so broad that there are opportunities everywhere At the same time. We are in the hype of the model, meaning everybody is expecting that this will do everything. We will need to really see what this. We are in the hype of the model, meaning everybody is expecting that this will do everything. We will need to really see what this do in the future. You know, I think that expectations versus still real implementation have a significant gap at this point, but it's no doubt that that gap will be covered in the future, will be covered in the future. What I always challenge myself and the team is the other thing that we need to be careful is in some way predict when this will happen, but, at the same time, not necessarily believe everything that you read, because the reality of the implementation of this technology will variate in time.
Speaker 1:Yeah, completely agree, Perfect. Well, I'm always looking for the future and how we can do things a lot better, which is why I like to cheat at the end of the podcast where I asked my current guest to recommend my next guest. Sergio, who did you have in mind?
Speaker 2:Yeah, this industry is so amazing, so many good people in the market. But I was remembering a good friend that is Alvaro Celis. He's an executive. He was an executive in Microsoft for many, many, many years, working in partners in different regions and, at the end, the ISB space. Then I think that he could be a good candidate for you to explore.
Speaker 1:Look forward to having him on and, sergio, thank you so much for sharing your wisdom today. It's been awesome.
Speaker 2:Okay, thank you so much for your time. Great to have this conversation with you, thanks.